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GENIX – A computational network analysis approach to identify signature genes
2024
Sanofi, Cambridge, USA
Single-cell RNA sequencing (scRNA-seq) has transformed the understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects, and is susceptible to false positives. In this study, GENIX (gene expression network importance examination) is presented, a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. GENIX was benchmarked using both synthetic and experimental datasets. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.
GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions
Virginia Savova, Nima Nouri
Added on: 07-08-2024
Improved detection of antibody variable region peptides through proteomics
2024
Erasmus University Medical Center, Rotterdam, Netherlands
The polyclonal repertoire of circulating antibodies potentially holds valuable information about an individual’s humoral immune state. While bottom-up proteomics is well suited for serum proteomics, the vast number of antibodies and dynamic range of serum challenge this analysis. To acquire the serum proteome more comprehensively, high-field asymmetric waveform ion-mobility spectrometry (FAIMS) or two-dimensional chromatography were incorporated into standard trypsin-based bottom-up proteomics. Thereby, the number of variable region (VR)-related spectra increased 1.7-fold with FAIMS and 10-fold with chromatography fractionation. To match antibody VRs to spectra, de novo searching and BLAST alignment were combined. Validation of this approach showed that, as peptide length increased, the de novo accuracy decreased and BLAST performance increased. Through in silico calculations on antibody repository sequences, the uniqueness of tryptic VR peptides and their suitability as antibody surrogate was determined. Approximately one-third of these peptides were unique, and about one-third of all antibodies contained at least one unique peptide.
Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques
Christoph Stingl
Added on: 07-08-2024
Machine learning to optimize geometrically confined cardiac organoids
2024
Syracuse University, Syracuse, USA
Stem cell organoids are powerful models for studying organ development, disease modelling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, micropatterning techniques were utilized to create a designer library of 230 cardiac organoids with 7 geometric designs. Single organoid heterogeneity was analysed based on 10 physiological parameters using manifold learning techniques. The cardiac orgnoids were clustered and refined based on their functional similarity using unsupervised machine learning approaches, thereby, elucidating unique functionalities associated with geometric designs. Furthermore, the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results was highlighted. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.
Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques
Zhen Ma
Added on: 07-08-2024
New biomarker for asymptomatic stages of Alzheimer’s disease
2024
University of Barcelona, Barcelona, Spain
Clinical relevance of miRNAs as biomarkers is growing due to their stability and detection in biofluids. Presently, diagnosis at asymptomatic stages of Alzheimer's disease (AD) remains a challenge since it can only be made at autopsy according to Braak NFT staging. Achieving the objective of detecting AD at early stages would allow possible therapies to be addressed before the onset of cognitive impairment. Many studies have determined that the expression pattern of some miRNAs is dysregulated in AD patients, but to date, none has been correlated with downregulated expression of cellular prion protein (PrPC) during disease progression. That is why, cross studies of miRNAs up-regulated in AD with in silico identification of potential miRNAs-binding to 3′UTR of human PRNP gene were conducted. In this study, miR-519a-3p was selected for analyses. In vitro experiments were carried out to validate miR-519a-3p target on 3′UTR-PRN, and to analyze the levels of PrPC expression after using of mimic technology in cell culture. In order to analyse miR-519a-3p expression in human cerebral samples of AD at different stages of disease evolution, RT-qPCR was performed. Additionally, samples of other neurodegenerative diseases such as other non-AD tauopathies and several synucleinopathies were included in the study. The results showed that miR-519a-3p overlaps with PRNP 3′UTR in vitro and promotes downregulation of PrPC. Moreover, miR-519a-3p was found to be up-regulated exclusively in AD samples from stage I to VI, suggesting its potential use as a novel label of preclinical stages of the disease.
miR-519a-3p, found to regulate cellular prion protein during Alzheimer's disease pathogenesis, as a biomarker of asymptomatic stages
Rosalina Gavín
Added on: 06-24-2024
Personalized brain circuit scores identify distinct biotypes in depression and anxiety
2024
Stanford University School of Medicine, Stanford, USA
There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or ‘biotypes’ to enable stratification of patients with depression and anxiety. Here, task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol across multiple studies in patients with depression and anxiety when treatment free and after randomization to pharmacotherapy or behavioural therapy was used. From these patients, personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy were derived. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity, and activation and connectivity elicited by emotional and cognitive tasks. The six biotypes showed consistency with the theoretical taxonomy in this study, and were distinguished by symptoms, behavioural performance, and response to pharmacotherapy as well as behavioural therapy. The results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.
Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety
Leanne M. Williams
Added on: 07-29-2024
Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling
2024
Shenzhen Bay Laboratory, Shenzhen, China
Plasma cell-free DNA (cfDNA) fragmentation patterns are emerging directions in cancer liquid biopsy with high translational significance. Conventionally, the cfDNA sequencing reads are aligned to a reference genome to extract their fragmentomic features. In this study, through cfDNA fragmentomics profiling using different reference genomes on the same datasets in parallel, systematic biases in such conventional reference-based approaches were reported. The biases in cfDNA fragmentomic features vary among ancestries in a sample-dependent manner, and therefore might adversely affect the performances of cancer diagnosis assays across multiple clinical centres. In addition, to circumvent the analytical biases, Freefly was developed, a reference-free approach for cfDNA fragmentomics profiling. Freefly runs ∼60-fold faster than the conventional reference-based approach while generating highly consistent results. Moreover, cfDNA fragmentomic features reported by Freefly can be directly used for cancer diagnosis. Hence, Freefly possesses translational merit toward the rapid and unbiased measurement of cfDNA fragmentomics.
Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling
Kun Sun
Added on: 07-08-2024
Subtype-WGME enables whole-genome-wide, multi-omics cancer subtyping
2024
East China University of Science and Technology, Shanghai, China
In this study, an innovative strategy for integrating whole-genome-wide multi-omics data is presented, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that the proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, a robust pipeline for identifying whole-genome-wide biomarkers was established, unearthing 195 significant biomarkers. Furthermore, an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping was conducted. The investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, the findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.
Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping
Zhe Wang
Added on: 07-08-2024
AI-powered algorithm accelerates TCR identification to develop personalized immunotherapies
2024
German Cancer Research Center, Heidelberg, Germany
To accelerate the development of personalized T cell therapies, an antigen-independent classifier (predicTCR) is presented in the present study. PredicTCR is a machine learning algorithm that leverages high-performance single-TIL RNA sequencing techniques to identify reactive T cell receptors (TCRs) in tumor-infiltrating lymphocyte cultures (TILs) of various cancer types. The resected tumor tissue used for the study was provided by a male 62-year-old melanoma patient. After successfully identifying reactive TCRs in the donor's tumor samples, the researchers used the results and other sequence data to train the algorithm. The results show that predicTCR's predictions have significantly higher accuracy (up to 90%) and sensitivity than previous methods. Additionally, the combination of high-throughput TCR cloning and reactivity validation enables selection of prioritized TCR clonotypes in just a few days. In summary, the method proves to be an innovative and efficient approach that can help optimize the production of personalized immunotherapies by saving valuable time and continuously specifying and improving the search for reactive TCRs through the integrated machine learning system.
Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy
E. W. Green, M. Platten
Added on: 04-02-2024
Curated data for the aquatic toxicity of chemicals
2024
Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signalling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, the researchers developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. Here, they provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.
Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment
Wibke Busch, Tobias Schulze
Added on: 01-26-2024
AI uses chromatin as a biomarker for the (early) diagnosis and treatment evaluation of cancer
December 2023
Paul-Scherrer Institute, Villigen, Switzerland
Results of previous studies show that tumors influence the chromatin conformation in peripheral blood mononuclear cells (PBMCs) through so-called secretome signals. In addition, the concentration of the secretomes depends on the stage of the disease. Following this, the present study evaluates the potential of chromatin biomarkers for early diagnosis and treatment evaluation of cancer. For this purpose, the three-dimensional chromatin organization from blood samples from 10 healthy volunteers and various tumor patients was first imaged and analysed using fluorescence methods. Characteristic chromatin patterns and features were transformed into subject-specific data sets and fed into a machine learning system for prediction. In a first series of tests, the data sets from 10 healthy volunteers were compared with the information from 10 tumor patients. The AI was able to distinguish between healthy and sick people with a high level of sensitivity based on the chromatic characteristics. In further series of tests, it was also shown that the AI could distinguish three tumor groups from one another with up to 89% accuracy. Finally, the chromatin conformation of 30 tumor patients (10 glioma, 10 meningioma and 10 head and neck tumor patients) who underwent proton irradiation was examined. The blood samples were taken and analysed at three different stages during therapy. The results showed characteristic cellular changes that indicated a response to therapy and will be further developed in future clinical studies. In summary, the AI-based chromatin biomarker analysis proves to be a new and valuable method that can help to diagnose cancer at an early stage, to predict the success or response of patient-specific therapeutic approaches and to avoid the use of invasive diagnostic procedures in seriously ill patients.
Imaging and AI based chromatin biomarkers for diagnosis and therapy evaluation from liquid biopsies
Damien C. Weber, damien.weber@psi.ch
Added on: 12-19-2023
AI model reveals links between structural and functional brain characteristics
November 2023
University of Cambridge, Cambridge, United Kingdom
In this study, the researchers have demonstrated that putting physical constrains on an artificially intelligent system, similarly to how the human brain develops and functions within physical and biological constraints, enables it to develop features of complex organisms’ brains to solve problems. They created an artificial system aimed at modelling a simplified version of the brain and applied physical constraints. The system developed some key characteristics like those found in human brains. This AI system may start to uncover how these constraints shape differences between people’s brains and impact the differences observed in people experiencing cognitive or mental health difficulties.
Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
Danyal Akarca, Jascha Achterberg
Added on: 04-02-2024
A benchmark dataset for machine learning in ecotoxicology
October 2023
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning and a strong biological and ecotoxicological background, which can be a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splitting were used. Therefore, in this study, the researchers provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species, as well as chemical properties and molecular representations. The researchers propose concrete challenges to the community, including extrapolation across taxonomic groups, to learn more about the potential and limitations of machine learning in ecotoxicology.
A benchmark dataset for machine learning in ecotoxicology
Christoph Schür
Added on: 01-26-2024
Mathematical model of fluid flow in organ-chips
October 2023
University of Minho, Guimarães, Portugal
The aim of this study is to develop a numerical model capable of reproducing the fluid flow behaviour within an organ-on-a-chip (OoC) system. Therefore, the validation of a numerical model for an OoC microfluidic device was undertaken. This comprised the assessments of microparticles flowing through a physical OoC model. High-speed microscopy images of the flow, using a blood analogue fluid, were analysed and compared with the numerical simulation. Furthermore, oxygen transport was simulated and evaluated for different Reynolds numbers.
The results predicted by the numerical model and the ones outputted by the experimental model were in good agreement. The successful validation of the numerical model against experimental data shows its accuracy and reliability in simulating the fluid flow within the OoC. This can expedite the OoC design process.
Numerical evaluation and experimental validation of fluid flow behavior within an organ-on-a-chip model
Violeta Carvalho
Added on: 12-02-2023
Machine learning model identifies cancer-specific enhancer-gene interactions
2023
Weill Cornell Medicine, New York, USA
In this study, a computer model called Differential Gene Targets of accessible chromatin (DGTAC) was developed. It helps identify specific genetic areas that promote the growth of malignant cells, which is important for the development of anti-cancer drugs. The model uses data from 371 patients with different types of cancer to identify these areas. It only needs a small amount of tissue from the patients to make reliable predictions.
The model uses specific data (ATAC-seq and RNA-seq) to predict how genes are activated. It also takes into account other information such as the distance of the genetic sequences from their transcription start sites and how strongly they are activated. An important point in the predictions is a special error value that is calculated for each sample.
In tests with different cancer cells, the model was able to identify new areas that influence the activity of 602 cancer genes. In addition, the predictions of the model were checked and confirmed in experiments. The model can also distinguish different types of genetic regions, which previous methods could not.
The results of this study help us to better understand the genetic causes of diseases triggered by faulty gene activity. In addition, the model shows how it can be useful in developing tailored drugs for individual patients through its patient-specific approach.
Recapitulation of patient-specific 3D chromatin conformation using machine learning
Ekta Khurana
Added on: 09-26-2023
In vitro model for bone remodelling with microfluidics
2023
Eindhoven University of Technology, Eindhoven, Netherlands
Healthy bone tissues are maintained in a dynamic equilibrium by the continuous process of bone remodelling. Disruption of this process can lead to serious diseases such as osteoporosis. However, there is currently no comprehensive in vitro model that can represent the complex process of bone remodelling.
In this context, microfluidic chips represent promising approaches. In particular, their ability to create dynamic culture conditions is crucial for the in vitro formation of bone tissue. In this study, a novel scaffold-free three-dimensional microfluidic co-culture model was presented that mimicked the process of bone remodelling.
The method used human mesenchymal stem cells that differentiated into the osteoblast lineage and assembled into structured bone tissue. This tissue resembled the trabecular structures in human bone in shape and dimension. By integrating human monocytes, it was also possible to generate osteoclast-like cells that were able to interact with the bone tissue.
To mimic physiological conditions, computer-based models were developed to analyse the shear stresses and strains generated in the tissue due to fluid flow. In addition, an experimental arrangement was developed that allowed the cells to be cultured on the chip over a long period of time (up to 35 days). This arrangement thereby enabled advantages such as continuous fluid flow, minimal risk of air entrapment, easy exchange of the culture medium in the incubator and the option of real-time imaging of live cells.
The established on-chip co-culture represents a significant advance in the development of in vitro models of bone remodelling. In the future, these models will help to test the efficacy of drugs and gain a better understanding of the underlying mechanisms of bone tissue changes.
Osteogenesis and osteoclastogenesis on a chip: Engineering a self-assembling 3D coculture
M.A.M. Vis
Added on: 08-29-2023
NetBID2: computational tool for hidden driver analysis
2023
St. Jude Children’s Research Hospital, Memphis, USA
In this study, the researchers have created an updated method of analysing multi-omic data in an effort to identify hidden drivers of cancer that are not immediately obvious through traditional sequencing approaches. The computational tool, called NetBID2, was designed to find hidden drivers of disease by taking large sets of RNA sequencing data and generating a gene-gene interactome. This interactome allows researchers to track the relationships between driver candidates and their downstream effector genes, thus identifying which signalling proteins are most central to the key relationships that fuel disease. The authors demonstrate the power of NetBID2 using three hidden driver examples in normal tissues and paediatric and adult cancers.
NetBID2 provides comprehensive hidden driver analysis
Jiyang Yu
Added on: 09-14-2023
Multi species-toxicity prediction for metallic nanomaterials
2023
Beihang University, Beijing, China
The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. The objective of this study was to develop a machine learning-based regression model for aquatic toxicity prediction that takes into account physicochemical properties of MNMs, environmental factors, and different organisms with their own traits and exposure conditions. To achieve this, a model of 14 different MNMs against 51 species was developed based on published data sets, and the model was validated against data obtained from recently published literature. The model was used to analyse the importance and interaction between physicochemical properties, environmental factors and species. Feature importance and interaction analysis indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs.
The in-silico approach enables the multi species-toxicity prediction for MNMs and will help to further explore exposure pathways.
Using machine learning to predict adverse effects of metallic nanomaterials to various aquatic organisms
Ying Wang, Wenhong Fan
Added on: 02-06-2024
Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish
2023
Johns Hopkins University, Baltimore, USA
Recent advances in human stem cell-derived brain organoids promise to replicate critical molecular and cellular aspects of learning and memory and possibly aspects of cognition in vitro, thereby, creating a novel scientific discipline called “organoid intelligence” (OI).
By presenting a collaborative program to implement the vision of a multidisciplinary field of OI, the authors aim to establish OI as a form of genuine biological computing that harnesses brain organoids using scientific and bioengineering advances in an ethically responsible manner. Standardized, 3D, myelinated brain organoids can now be produced with high cell density and enriched levels of glial cells and gene expression critical for learning. Integrated microfluidic perfusion systems can support scalable and durable culturing, and spatiotemporal chemical signalling. Novel 3D microelectrode arrays permit high-resolution spatiotemporal electrophysiological signalling and recording to explore the capacity of brain organoids to recapitulate the molecular mechanisms of learning and memory formation and, ultimately, their computational potential. Technologies that could enable novel biocomputing models via stimulus-response training and organoid-computer interfaces are in development.
The strategic development of OI as a scientific discipline combined with an embedded ethics approach to analyse the ethical aspects raised by OI research, may help facilitate the development of OI-based biocomputing systems that allow faster decision-making, continuous learning during tasks, and greater energy and data efficiency.
Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish
Thomas Hartung
Added on: 06-10-2024
Framework for human stem cell organisation and variation
2023
Allen Institute for Cell Science, Seattle, USA
Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine. Here, the researchers reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour—at the level of major cellular structures that represent distinct organelles and functional machines. They generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations.
Integrated intracellular organization and its variations in human iPS cells
Susanne M. Rafelski
Added on: 06-05-2023
In silico compound identification for antimalarial therapy
2023
C. K. Tedam University of Science and Technology, Navrongo, Ghana
Malaria caused by Plasmodium falciparum, remains one of the most fatal parasitic diseases that has affected nearly a third of the world’s population. The major impediment to the treatment of malaria is the emergence of resistance of the P. falciparum parasite to current anti-malaria therapeutics. In this study, the researchers employed various in silico techniques to identify potential new inhibitors of two enzyme targets that play a crucial role in fatty acid synthesis in the Plasmodium parasite. Nine hit compounds were identified in total, and all show excellent pharmacokinetic and toxicity properties. The results indicate that the identified compounds could serve as a treatment option for malaria.
In silico identification of potential inhibitors of acyl carrier protein reductase and acetyl CoA carboxylase of Plasmodium falciparum in antimalarial therapy
James Abugri
Added on: 04-25-2023
In silico method for IVF embryo selection
2023
Weill Cornell Medicine, New York, USA
One challenge in the field of in-vitro fertilization (IVF) is the selection of the most viable embryos for transfer. Current methods have multiple disadvantages including variability, invasiveness and cost. In this retrospective study, the researchers used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Analysis and model development included the use of 10 378 embryos from 1385 patients. STORK-A predicted aneuploid versus euploid embryos within three classification tasks with high accuracy. As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for further tests.
A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study
Iman Hajirasouliha
Added on: 01-05-2023
Machine learning method for personalized prediction of brain tumor progression
2023
University of Waterloo, Waterloo, Canada
Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-invasion (PI) model has been used extensively in the literature to model the growth of these tumours, though it relies on two key parameters that are difficult to estimate in a patient-specific manner. In this paper, the researchers develop and apply a deep learning model capable of making accurate estimates of these key GBM-characterizing parameters while simultaneously producing a full prediction of the tumour progression curve. The method uses MRI data. The model was applied to a clinical dataset consisting of five patients diagnosed with GBM. For all patients, the researchers derive evidence-based estimates for each of the PI model parameters and predictions for the future progression of the tumour, along with estimates of the parameter uncertainties. This work provides a new, easily generalizable method for the estimation of patient-specific tumour parameters, which can be built upon to aid physicians in designing personalized treatments.
Deep learning characterization of brain tumours with diffusion weighted imaging
Cameron Meaney
Added on: 01-19-2023
‘Loosening of associations’ found in schizophrenia
December 2022
Tokyo Medical and Dental University, Tokyo, Japan
Schizophrenia is a mental illness that presents with thought disorders including delusions and disorganized speech. Thought disorders have been regarded as a consequence of the loosening of associations between semantic concepts. To evaluate how aberrant semantic connections are expressed through brain activity, the researchers characterized large-scale network structures of concept representations using functional magnetic resonance imaging (fMRI). They quantified various concept representations in patients’ brains from fMRI activity evoked by movie scenes using encoding modelling and subsequently constructing semantic brain networks. The results provide pathophysiological evidence for the loosening of associations in schizophrenia. This method represents a promising approach for understanding the neural basis of altered or creative inner experiences of individuals with mental illness or exceptional abilities, respectively.
Disorganization of semantic brain networks in schizophrenia revealed by fMRI
Hidehiko Takahashi
Added on: 01-19-2023
Deep brain stimulation for treatment of Alzheimer´s disease
December 2022
Charité – Universitätsmedizin Berlin, Berlin, Germany
Within this study, deep brain stimulation (DBS) is examined as an investigational treatment for patients with mild Alzheimer’s disease. In particular, the electrode placement was investigated and optimized with regard to the improvement of the cognitive function of the involved patients.
To investigate this, data obtained from 46 patients treated with DBS at seven international centres were used. The optimal stimulation site was identified using structural and functional connectivity data, as well as modulation of brain networks related to memory.
Findings were robust and could define an optimal target for Alzheimer’s Disease treatment that could refine DBS surgery and programming.
Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer’s disease
Andreas Horn
Added on: 01-06-2023
In silico mapping of flavonoid compounds for cancer therapies
December 2022
Oswaldo Cruz Foundation (Fiocruz), Eusébio, Brazil
Flavonoids are a class of natural products widely available in medicinal and dietary plants. Their pharmacological use has shown the potential to reduce the risk of different types of cancer, among other prevalent diseases. Their molecular scaffold inhibits the PD-1/PD-L1 axis, an important pathway related to the adaptive immune resistance of cancer cells already targeted for developing new cancer immunotherapy. Here, the researchers aimed to computationally predict the binding mode of flavonoid molecules with PD-1 and/or PD-L1 proteins using unbiased computational methodologies such as blind docking and supervised molecular dynamics simulation. The molecular interactions and dynamics of these predicted poses of protein-flavonoid complexes were further analysed through multiple molecular dynamics simulations. The results introduced unprecedented information on flavonoid interaction and dynamics when complexed with PD-1 checkpoint pathway proteins, and can pave the road for developing new flavonoid derivatives with selective anticancer activity.
In silico mapping of the dynamic interactions and structure-activity relationship of flavonoid compounds against the immune checkpoint programmed-cell death 1 pathway
João Hermínio Martins Da Silva
Added on: 04-25-2023
In silico identification of multi-target ligands for neurodegenerative diseases drug development
November 2022
Bulgarian Academy of Sciences, Sofia, Bulgaria
The conventional treatment of neurodegenerative diseases (NDDs) is based on the “one molecule—one target” paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as “multi-target-directed ligands” (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. In this study, more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment. Four selected hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations, resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs.
In silico identification of multi-target ligands as promising hit compounds for neurodegenerative diseases drug development
Nikolay T. Tzvetkov, Ivanka Tsakovska
Added on: 11-28-2022
Prediction of genotoxicity based on gene expression
November 2022
Vrije Universiteit Brussel, Brussels, Belgium
In this study, new prediction models for genotoxicity were developed based on a reference dataset of 38 chemicals. Human liver cells were treated with the chemicals and resulting gene expression data were obtained using qPCR. 84 genes were selected and different machine learning algorithms were used and compared with regard to their predictive accuracy. In addition, the applicability of the prediction models was investigated on a publicly available gene expression dataset generated with RNA sequencing. To facilitate data analysis, an online application was developed, combining the outcomes of two prediction models.
This research demonstrates that the combination of gene expression data with supervised machine learning algorithms can contribute towards a human-relevant in-vitro genotoxicity testing strategy.
Novel prediction models for genotoxicity based on biomarker genes in human HepaRGTM cells
Anouck Thienpont
Added on: 12-12-2022
Repair processes in the metabolism as drugs targets
November 2022
University of Wuerzburg, Wuerzburg, Germany
Repair enzymes may represent an alternative target class for selective metabolic inhibition, e.g. for cancer treatment, but pharmacological tools to test this concept are still needed.
In this study, it was demonstrated that phosphoglycolate phosphatase (PGP), a repair enzyme in glycolysis, can be targeted with a small molecular compound. Using a combination of small molecule screening, protein crystallography, molecular dynamics simulations and NMR metabolomics, a compound was identified that inhibits PGP with high selectivity by locking the phosphatase in an inactive conformation. The compound was characterized by biochemical and cellular assays.
This study provides key insights into effective PGP targeting, thereby offering an approach to control glycolysis, and at the same time demonstrates the feasibility of therapeutic approaches to target cellular metabolism.
Glycolytic flux control by drugging phosphoglycolate phosphatase
Antje Gohla
Added on: 11-21-2022
Computer modelling approach to simulate protein interactions
October 2022
The University of Kansas, Lawrence, USA
Advances in computational modelling have led to an increasing focus on larger biomolecular systems, up to the level of a cell. Protein interactions are a central component of cellular processes. Techniques for modelling protein interactions have been divided into two fields: protein docking (predicting the static structures of protein complexes) and molecular simulation (modelling the dynamics of protein association, for relatively short simulation times at atomic resolution). This study combined the two approaches to reach very long simulation times. The study makes the model more adequate for real cells, to explore cellular processes at atomic resolution. Thereby it helps to better understand molecular mechanisms of life and to use this knowledge to improve our ability to treat diseases.
Docking-based long timescale simulation of cell-size protein systems at atomic resolution
Ilya A. Vakser
Added on: 10-24-2022
Microstructural alterations in Alzheimer´s disease
October 2022
Karolinska Institute, Stockholm, Sweden
The goal of this study was to investigate neurodegeneration of the human cholinergic system using diffusion-weighted magnetic resonance imaging (MRI) across different stages of Alzheimer’s disease.
Therefore microstructural alterations of two major cholinergic pathways were investigated in individuals along the Alzheimer’s disease continuum using an in vivo model of the human cholinergic system based on neuroimaging. 402 participants, including patients with different stages of Alzheimer’s disease and healthy controls, were included in the study. In addition to MRI participants performed neuropsychological tests and cerebrospinal fluid biomarkers were analyzed.
Cholinergic white matter pathways were modelled with an enhanced diffusion neuroimaging pipeline and compared between the different stages of Alzheimer’s disease and in relation to cognitive performance.
Cholinergic white matter pathways along the Alzheimer’s disease continuum
Daniel Ferreira
Added on: 03-20-2023
CRISPR gene editing can cause cell toxicity
2022
Barcelona institute for Science and Technology, Barcelona, Spain
In this study, scientists have found that depending on the targeted spot of the human genome, CRISPR gene editing can give rise to cell toxicity and genomic instability. Using computational methods, the team analysed the most popular CRISPR library designed for human cells and detected 3,300 targeted spots that show strong toxic effects. The study also reports that around 15 per cent of human genes contain at least one toxic editing point. These unwanted effects are mediated by the tumour suppressor protein p53 and are determined by the DNA sequence near the editing point and various epigenetic factors in the surrounding region.
TP53-dependent toxicity of CRISPR/Cas9 cuts is differential across genomic loci and can confound genetic screening
Fran Supek
Added on: 11-25-2022
Deep learning for regulatory DNA design
2022
Chalmers University of Technology, Gothenburg, Sweden
The design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, the researchers prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. The ExpressionGAN tool can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions.
Controlling gene expression with deep generative design of regulatory DNA
Jan Zrimec, Aleksej Zelezniak
Added on: 12-16-2022
In silico reaction screening
2022
Hokkaido University, Sapporo, Japan
Quantum chemical calculations are mainly regarded as a method for mechanistic studies in organic chemistry, whereas their use for the simulation of unknown reactions could greatly assist in reaction development. Here, the researchers report a strategy for developing multicomponent reactions on the basis of the results of computational reaction simulations. Using in silico methods involving quantum chemical calculations, they successfully developed a suite of 48 reactions that produce compounds potentially useful for novel drug development.
In silico reaction screening with difluorocarbene for N-difluoroalkylative dearomatization of pyridines
Tsuyoshi Mita, Satoshi Maeda
Added on: 11-25-2022
Antibody combination therapy to suppress HIV-1
2022
Max Planck Institute for Dynamics and Self-Organization, Goettingen, Germany
Infusion of broadly neutralizing antibodies (bNAbs) has shown promise as an alternative to anti-retroviral therapy against HIV. A key challenge is to suppress viral escape, which is more effectively achieved with a combination of bNAbs. Here, the researchers propose a computational approach to predict the efficacy of a bNAb therapy based on the population genetics of HIV escape, which they parametrize using high-throughput HIV sequence data from bNAb-naive patients. The study shows that a cocktail of three bNAbs is necessary to effectively suppress viral escape, and predict the optimal composition of such bNAb cocktail. These results offer a rational therapy design for HIV and show how genetic data can be used to predict treatment outcomes and design new approaches to pathogenic control.
Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1
Armita Nourmohammad
Added on: 11-28-2022
Deep learning approach for deciphering protein subcellular localization
2022
Chan Zuckerberg Biohub, San Francisco, USA
Explaining the diversity and complexity of protein localization is essential to fully understanding cellular architecture. Here, the researchers present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require pre-existing knowledge, categories, or annotations. The researchers quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches.
Self-supervised deep learning encodes high-resolution features of protein subcellular localization
Loic A. Royer, Manuel D. Leonetti, Hirofumi Kobayashi
Added on: 10-24-2022
Mathematical model of gas transport in the human lung
2022
University of Stuttgart, Stuttgart, Germany
The complex interaction between lung anatomy and gas transport mechanisms complicates analysis of gas transport in the human lung.
In this study, a mathematical model of the human lung is described, which is based on the finite difference method and consists of two lung units connected in parallel. To implement this approach, morphometric data derived from the literature was used. The results show that the model was able to simulate important transport processes like diffusion, convection, and the Pendelluft phenomenon.
The model can be used to describe the gas transport process within the human acinus via simple mathematical functions.
Investigation of tracer gas transport in a new numerical model of lung acini
Christoph Schmidt
Added on: 11-27-2023
Software tool for automated assessment of sarcomeres in muscle cell cultures
2022
Leiden University Medical Center, Leiden, Netherlands
Sarcomeres are the structural units of the contractile apparatus in cardiac and skeletal muscle cells. Assessment of sarcomere length, alignment, and organization provides insight into disease and drug responses in striated muscle cells and models, ranging from cardiomyocytes and skeletal muscle cells derived from human pluripotent stem cells to adult muscle cells isolated from humans. However, quantification of sarcomere length is typically time-consuming and prone to user-specific selection bias. Automated analysis pipelines exist but these often require either specialized software or programming experience. In addition, these pipelines are often designed for only one type of cell model in vitro. Here, an easy-to-implement protocol and software tool for automated sarcomere length and organization quantification in a variety of striated muscle in vitro models was presented: Two-dimensional (2D) cardiomyocytes, three-dimensional (3D) cardiac microtissues, isolated adult cardiomyocytes, and 3D tissue-engineered skeletal muscles. Based on an existing mathematical algorithm, this image analysis software (SotaTool) automatically detects the direction in which the sarcomere organization is highest over the entire image and outputs the length and organization of sarcomeres. Videos of live cells during contraction were also analysed, thereby allowing the measurement of contraction parameters like fractional shortening, contraction time, relaxation time, and beating frequency. This protocol gives a step-by-step guide on how to prepare, image, and automatically quantify sarcomere and contraction characteristics in different types of in vitro models and provides basic validation and discussion of the limitations of the software tool.
Software tool for automatic quantification of sarcomere length and organization in fixed and live 2D and 3D muscle cell cultures In vitro
Berend J. Van Meer
Added on: 12-14-2022
In silico development of a vaccine against Candida infection
2022
Shanghai Jiao Tong University, Shanghai, China
To date, no effective therapy is available for Candida auris infections, which especially affect COVID-19 patients. The aim of this study was to characterize therapeutic targets and design vaccine candidates by solely using in silico methods. Therefore, outer membrane proteins, which are considered highly antigenic and essential for host-pathogen interactions, were investigated. The selected proteins were used to predict cytotoxic T lymphocyte and B-cell epitopes, and to design vaccines against C. auris. In particular, subtractive proteomics and immune-informatics approaches were used. The designed candidate vaccines were further evaluated using molecular docking, and immune simulation strategies.
The present study provides new and valuable epitope candidates and prompts future vaccine development against this pathogen.
Subtractive proteomics assisted therapeutic targets mining and designing ensemble vaccine against Candida auris for immune response induction
Dong-Qing Wei, Yanjing Wang
Added on: 09-25-2023
Investigation of microbiome effects on intestinal Candida overgrowth
2022
Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany
Intestinal microbiota dysbiosis can initiate overgrowth of commensal Candida species, thereby leading to disseminated candidiasis.
In this study, a systems-biology approach involving multi-omics (transcriptome/metabolome) profiling, in silico metabolic modelling, and in vitro infection biology was used to uncover how Lactobacillus rhamnosus colonization of intestinal epithelial cells mediates protection against Candida albicans infection. Human colon cells were cultivated in monolayers and colonized with Lactobacillus rhamnosus for 18 h prior to infection with C. albicans. The results demonstrate that protection by L. rhamnosus colonization is a multifactorial process that synergistically affects C. albicans growth by reshaping the metabolic environment, forcing metabolic adaptations that reduce fungal pathogenicity. Moreover, the approach provides insights into how C. albicans pathogenicity can be controlled or prevented.
Lactobacillus rhamnosus colonisation antagonizes Candida albicans by forcing metabolic adaptations that compromise pathogenicity
Bernhard Hube
Added on: 09-25-2023
AI identifies characteristic pattern in tumor cells
2022
Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
Tumors are complex cell tissues that are characterized by high variability, which makes it considerably more difficult to research and identify relevant gene sequences. The present study presents a newly developed algorithm called Icarus, which analyzes differences between cancer cells and the surrounding tissue across different cancer types and data sets. For this purpose, the machine-learning model was fed with numerous data provided to researchers worldwide by various institutions. In the first step, the program created gene signatures and a tumor classifier, through which the algorithm learned to distinguish carcinogenic from healthy cells. Subsequently, the AI was "trained" with various cancer tissue data and the performance of the model was evaluated. Ikarus showed a significantly higher analytical precision than previously developed in silico methods. The model enables not only the characterization of variable, stable cell states but also the functional annotation of individual cells, such as the prediction of differentiation potential, susceptibility to disorders and the prognosis of cell-cell interactions. In all cancer cell types, a characteristic gene sequence pattern has been identified that could provide new insights for cause research. Within this sequence, the algorithm classified certain genes as carcinogenic that have not previously been associated with the development of tumors. The model could prove to be a helpful diagnostic tool and improve therapeutic approaches.
Identifying tumor cells at the single-cell level using machine learning
Altuna Akalin, Verdran Franke
Added on: 08-09-2022
Metabolic data-dependent physiologically-based kinetic model for cardiotoxicity
2022
Wageningen University, Wageningen, Netherlands
The present study presents an in-vitro-in-silico approach to predict the effect of inter-individual and interethnic variations in the cardiotoxicity of R- and S-methadone in the Caucasian and the Chinese population. In-vitro cardiotoxicity data, and metabolic data obtained from two approaches, using either 25 Caucasian and 25 Chinese human liver microsomes or recombinant cytochrome P450 enzymes, were integrated with physiologically based kinetic (PBK) models and Monte Carlo simulations. Ultimately, the maximum concentrations of R- and S-methadone in the heart venous blood of Caucasian and Chinese populations were predicted and the chemical-specific adjustment factor (CSAF) was calculated as a parameter to quantify inter-individual differences in toxicokinetics or toxicodynamics. The novel methodology can be used to enhance cardiac safety evaluations and risk assessment of chemicals.
In vitro–in silico‑based prediction of inter‑individual and inter‑ethnic variations in the dose‑dependent cardiotoxicity of R- and S‑methadone in humans
Miaoying Shi
Added on: 07-26-2022
Retinal cell map for retinal diseases therapies
2022
National Eye Institute, Bethesda, USA
Retinal degenerative diseases affect specific regions of the retinal pigment epithelium (RPE), suggesting the presence of functionally different RPE subpopulations. To identify these subpopulations in human eyes, the researchers generated the first complete morphometric map of the RPE at single-cell resolution using artificial intelligence-based software. They identified five concentric RPE subpopulations, including a ring of RPE cells with a cell area similar to the macula in the periphery of the eye. Moreover, they found that specific RPE subpopulations are differentially susceptible to monogenic and polygenic retinal diseases. The results obtained here will allow the study of molecular and functional RPE differences responsible for regional retinal diseases and will help develop precise cell and gene therapies for specific degenerative eye diseases.
Single-cell-resolution map of human retinal pigment epithelium helps discover subpopulations with differential disease sensitivity
Kapil Bharti
Added on: 11-25-2022
Integration of transcriptomics into read-across-based risk assessment
2022
Leiden University, Leiden, Netherlands
Chemical read-across is conventionally evaluated without specific knowledge of the biological mechanisms leading to adverse outcomes in vivo. Thus the aim of this study was to integrate modes of action, thereby optimising read-across outcomes.
Exemplarily transcriptomic responses of primary human hepatocytes to various carboxylic acids were evaluated to include detailed mode-of-action data as a proof-of-concept for read-across in risk assessment. Hepatocytes were exposed to 18 structurally different valproic acid analogues for 24 h to determine biological similarity in relation to in vivo steatotic potential. Using a targeted high throughput screening assay, the differential expression of approx. 3,000 genes covering relevant biological pathways was determined.
It was shown that the transcriptomic analysis of human hepatocytes can reinforce the prediction of liver injury outcomes based on quantitative and mechanistic biological data and contribute to hazard identification.
Application of high-throughput transcriptomics for mechanism-based biological read-across of short-chain carboxylic acid analogues of valproic acid
Bob van de Water
Added on: 01-20-2023
Combination of in vitro and in silico methods for determining acute oral toxicity of chemicals
2022
Physicians Committee for Responsible Medicine, Washington, USA(1)
RTI International, Durham, USA(2)
RTI International, Durham, USA(2)
To improve the detection of chemical safety, 11.992 chemicals with acute oral toxicity information were divided into clusters of structurally similar compounds in this study. One or more ToxCast/Tox21 assays were assigned to each cluster by searching for the minimum number of assays required to record at least one positive hit below cytotoxicity for all acutely toxic chemicals in the cluster. When structure information is used to select assays to test, none of the chemicals require more than four assays, and 98% require two assays or fewer. Both the structure-based clusters and the activity from the associated assays were significantly related to the GHS (Globally Harmonized System of Classification and Labeling of Chemicals) toxicity classification of the chemicals, suggesting good reproducibility of a combination of bioactivity and structure information. Predictability is better when the in vitro test corresponds directly to the mechanism of toxicity, but many indirect tests are also promising. Given the lower cost of in vitro assays, a small assay battery that includes both general cytotoxicity assays and two or more orthogonal assays targeting the toxicological mechanism could be used to further improve performance. This approach shows the promise of combining existing in silico approaches, such as the Collaborative Acute Toxicity Modeling Suite (CATMoS), with structure-based bioactivity information as part of an efficient tiered testing strategy to determine acute oral toxicity of chemicals.
Mapping mechanistic pathways of acute oral systemic toxicity using chemical structure and bioactivity measurements
Kristie Sullivan(1), Stephen W. Edwards(2)
Stephen W. Edwards et al. Frontiers in Toxicology 2022 [61]
Physicians Committee for Responsible Medicine [62]
Added on: 04-19-2022
In-silico model for nanoparticle toxicity for water fleas
2022
Leiden University, Leiden, Netherlands
The aim of the present study was to develop a model to predict the dose-response relationship of a variety of metal‐based nanomaterials, with focus on the shape of the dose-response curve for the commonly used organism Daphnia magna.
Models based on quasi–quantitative structure–activity relationships (QSARs) to estimate the Hill coefficient, which describes the steepness of the response curve, were developed. The model was trained on the basis of dose–response relationships of 60 data sets of 11 metal‐based nanomaterials obtained from 20 literature reports. Using the Hill equation, the relationship between the dose of the nanosuspension and the response data was calculated. Finally, a quasi–quantitative structure–activity relationship (QSAR) model was developed to estimate the calculated relationships based on specific nanomaterial properties. The model simulates the training data well, with 2.3% deviation between experimental and modelled response data. It was employed to predict the dose–response relationships of 15 additional data sets of seven metal‐based nanomaterials from 10 literature reports which were not included in model development, with an average error of 3.5%.
Development of a quasi–quantitative structure–activity relationship model for prediction of the immobilization response of Daphnia magna exposed to metal‐based nanomaterials
Willie Peijnenburg
Added on: 02-29-2024
Machine learning model identifies antibody targets
2022
University of Illinois at Urbana-Champaign, Urbana, USA
Using the information derived from 88 research publications and 13 patents, the researchers assembled a dataset of ∼8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. They demonstrated that the common (public) responses to different domains of the spike protein were quite different. Furthermore, they used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses.
A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2
Nicholas C. Wu
Added on: 09-08-2022
Machine learning models for early-stage Alzheimer's prediction
2022
Sathyabama Institute of Science and Technology, Chennai, India
In its early stages, Alzheimer's disease (AD) is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. In this study, several computational techniques have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on MRI images from 150 patients from the Open Access Series of Imaging Studies (OASIS) database. Machine learning techniques were applied to Alzheimer's disease datasets to bring a new dimension to predict the disease at an early stage. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. The proposed work shows better results, with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.
Early-stage Alzheimer's disease prediction using machine learning models
C. Kavitha
Added on: 09-14-2023
Mapping shows how the brain shrinks in Parkinson's disease
2022
Research Centre Jülich, Juelich, Germany
The neuropathological features of idiopathic Parkinson's disease (PD) are the degeneration of dopaminergic neurons in the striatum and the spread of aggregates of misfolded α-synuclein in the brain according to a specific pattern. However, the relationship between this pattern and motor and cognitive symptoms has not been clearly established. Therefore, this study investigated the spatiotemporal pattern of atrophy propagation in PD, its interindividual variability, and its relationship with clinical symptoms. Magnetic resonance (MR) images of 37 PD patients and 27 control subjects were acquired at up to 15 time points per subject and over an observation period of up to 8.8 years (mean: 3.7 years). MR images were analyzed using deformation-based morphometry (DBM) to measure region volumes and their longitudinal changes. Differences in these regional volume data between patients and control subjects and their associations with clinical symptoms were calculated.
At the beginning of the study, the researchers found that the volumes of several brain regions were smaller in the PD patients than in the control group, while some regions were enlarged in the patients' brains, presumably due to compensatory effects. Over time, the difference between the groups became larger and more pronounced: the brain volumes of the Parkinson's patients decreased almost twice as fast as those of the control group, especially in the grey matter. The temporal and occipital lobes, adjacent parts of the inferior parietal lobe and ventral parts of the frontal lobe were most affected by this volume decrease.
Detailed analysis of which parts of the brain changed over time was performed using neuroanatomical atlases, most notably the Julich Brain Atlas, which is freely available through the EBRAINS infrastructure. This detailed anatomical analysis revealed a very specific regional pattern of volume changes in Parkinson's patients that differed from that of healthy ageing. The researchers found that volume reductions in cortical areas, amygdala, and basal forebrain correlated with worsening clinical symptoms in PD patients.
Thus, longitudinal DBM appears to already map the progression of neuropathological changes in vivo, providing a tool to further explore PD.
Regional changes of brain structure during progression of idiopathic Parkinson's disease – A longitudinal study using deformation based morphometry
Peter Pieperhoff
Added on: 07-06-2022
Predicting adverse drug‒drug interactions with a deep learning model
2022
Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
Adverse drug-drug interactions (DDIs) are a major concern due to their unexpected side effects and need to be detected at an early stage of drug discovery and development. Although understanding the mechanisms of DDIs at the molecular level is critical for predicting their adverse effects, current models rely on drug structures and properties, with a prediction range limited to previously observed interactions. They do not consider the effects of DDIs on genes and cellular functions.
Here, a deep learning-based model for predicting DDIs based on signatures of drug-induced gene expression is presented. The DeSIDE DDI model consists of two parts: a feature generation model and a DDI prediction model. The feature generation model predicts the effects of a drug on gene expression by considering both the structure and the properties of the drug, whereas the DDI prediction model predicts various side effects of drug combinations.
This model can detect potentially dangerous drug pairs and serve as a drug safety monitoring system. It can also help determine the proper use of a drug in the development phase.
DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions
Hojung Nam
Eunyoung Kim et al. American Journal of Cheminformatics 2022 [70]
GitHub [71]
Technology Networks [72]
Added on: 05-13-2022
AI tool predicts who will develop pancreatic cancer based on CT images
2022
Cedars-Sinai Medical Center, Los Angeles, USA
Pancreatic ductal adenocarcinoma (PDAC) is not only the most common form of pancreatic cancer, but it is also the most lethal. However, early-stage diagnosis is challenging because there are no specific diagnostic biomarkers.
This study used electronic medical records from 36 PDAC patients diagnosed with cancer in the past 15 years who underwent CT scans six months to three years before their diagnosis. These CT images were considered normal at the time they were taken.
The AI tool was trained to analyze these prediagnostic CT images and compare them to CT images of 36 people who had not developed cancer. The researchers reported that the model identified, with 86% accuracy, the people who were eventually diagnosed with pancreatic cancer and those who were not.
The AI model detected differences on the surface of the pancreas between people with cancer and healthy controls. These texture differences could be the result of molecular changes that occur when pancreatic cancer develops.
Accordingly, the method could enable early detection of PDAC, giving more people the opportunity to have their tumor completely removed by surgery.
Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images
Debiao Li
Added on: 05-16-2022
Machine-assisted discovery toward sustained neural regeneration
2022
Princeton University, Princeton, USA(1)
The State University of New Jersey, Piscataway, USA(2)
The State University of New Jersey, Piscataway, USA(2)
Shortly after spinal cord injury, a secondary inflammatory cascade creates dense scar tissue that can inhibit or prevent nerve tissue regeneration. The enzyme ChABC degrades scar tissue after spinal cord injury and promotes tissue regeneration. However, it is highly unstable at human body temperature and loses all activity within a few hours. In this study, ChABC was stabilized by formulation through the use of artificial intelligence (AI) and robotics such that its activity was prolonged by a large amount.
Synthetic copolymers are able to wrap around enzymes such as ChABC and stabilize them in hostile microenvironments. To stabilize the enzyme, researchers used an AI-driven approach with liquid robots to synthesize numerous copolymers and test their ability to stabilize ChABC and maintain its activity at human body temperature for up to a week.
Machine-assisted discovery of chondroitinase ABC complexes toward sustained neural regeneration
Michael A. Webb(1), Adam J. Gormley(2)
Added on: 09-12-2022
Mathematical model for the evolution of ageing
2022
Max Planck Institute for Evolutionary Biology, Plön, Germany
According to one classical ageing theory, ageing develops because selection for certain characteristics decreases over the course of the reproductive period. Ageing would then be the consequence of decreasing selective power with increasing age. However, recent research has shown that this apparent inevitability depends on certain basic assumptions, which by no means always have to be given. In this study, the researchers developed a dynamic mathematical model that is no longer based on the assumption of certain presuppositions. They had their model reproduce the evolutionary process of living beings over and over again. Their theoretical analysis found that even under these dynamic conditions, the evolution of ageing always develops in a stable manner. They also found that as a consequence of ageing, the selective power decreases with reproductive age. Thus, on the one hand, they were able to confirm the classical mathematical theory of ageing: Selection power decreases with age. On the other hand, however, they showed that its logic must be reversed: The selective power weakens with age because ageing evolves, and not vice versa.
The selection force weakens with age because ageing evolves and not vice versa
Stefano Giaimo
Added on: 03-10-2022
Novel in silico tool for studying autoimmune diseases related to COVID-19
2022
Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea
The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. The researchers developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. They identified 133 and 648 human proteins harbouring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of the pipeline, the authors predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Finally, they developed a web application (https://ahs2202.github.io/3M/) to interactively visualize these results and made the pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyse any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, these immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.
CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins
Jihwan Park
Added on: 09-08-2022
Algorithm identifies unknown driver mutations in cancer cells
2022
German Cancer Research Center (DKFZ), Heidelberg, Germany
The development and spread of malignant tumors is associated with the increased occurrence of mutations. In the coding area of the genome, cancer-causing gene segments can already be identified as far as possible. The non-coding area, which includes important regulatory sequences, has so far remained unexplored due to methodological limitations. The distinction between driver mutations and neutral "passenger" mutations is particularly difficult. The present study describes a newly developed algorithm (sigDriver) that detects changes in the genetic material and evaluates them with regard to their cancer driver potential. The research group investigated three characteristic mutation signatures associated with the emergence of hotspots. For this purpose, the genetic material of a total of 3813 tumors was analyzed, whose entire genome had been sequenced as part of the International Cancer Genome Consortium (ICGC), The Cancer Genome Atlas program, as well as in a study on pediatric tumors. The new method follows an automated search-then-annotate approach; i.e. all mutations that the algorithm classified as carcinogenic were analyzed by means of differential expression analysis and annotation. The algorithm unerringly identified all already known hotspots, as well as presumed new drivers in the coding as well as non-coding area and enables a valid differentiation of driver and passenger mutations. The method can help to uncover further, unknown cancer drivers (especially in the regulatory field) in larger patient cohorts with the same type of cancer and is freely available to researchers worldwide.
Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions
Marc Zapatka, John K. L. Wong
Added on: 08-12-2022
Deep learning approach analyzes vision loss in Stargardt disease
2022
National Eye Institute, Bethesda, USA
Stargardt disease (ABCA4-associated retinopathy) is the most common form of macular degeneration in adolescence, affecting the centre of the retina, the macula. It leads to progressive loss of central visual acuity. Here, using patient data (optical coherence tomography of the eyes), the researchers demonstrated a Deep Learning-based method for characterizing photoreceptor degeneration over time in ABCA4-associated retinopathy. This approach allowed fully automated assessment of the progression of conventional biomarkers (e.g., ETDRS-based analysis of photoreceptor laminae thinning) and contour line-based analysis of photoreceptor degeneration over time. In addition, the age of loss of light-sensing cells was shown to depend on genotype, and estimates were provided for 31 variants, including 16 variants that have not previously been quantitatively analyzed for clinical severity.
Photoreceptor degeneration in ABCA4-associated retinopathy and its genetic correlates
Brian P Brooks, Brett G Jeffrey
Added on: 02-03-2022
Human physiologically based kinetic model
2022
Wageningen Food Safety Research, Wageningen, Netherlands
The predictive performance of a generic human physiologically based kinetic (PBK) model based on in-vitro and in-silico input data was assessed and optimized. Therefore, a dataset was created of 38,772 Cmax predictions for 44 compounds by applying different combinations of in vitro and in silico approaches for chemical parameterization. The predicted values were compared to reported human in vivo data derived from the literature. While the current model overestimated Cmax values, an underestimation did not occur. The results provide crucial insights into the predictive performance of PBK models based on in-vitro and in-silico input and the influence of different input approaches on the model predictions. This will promote the transition towards next-generation (animal-free) testing strategies for chemical safety evaluations by converting in vitro toxicity data into in vivo dose-response information.
Predictive performance of next generation human physiologically based kinetic (PBK) model predictions based on in vitro and in silico input data
Ans Punt
Added on: 04-22-2022
In silico analysis identifies possible COVID-19 cytokine storm drugs
2022
Spanish National Cancer Research Centre, Madrid, Spain
Among the causes of mortality triggered by SARS-CoV-2 infection, the development of an inflammatory “cytokine storm” (CS) plays a determinant role. Here, the researchers used transcriptomic data from the bronchoalveolar lavage fluid (BALF) of COVID-19 patients undergoing a CS to obtain gene signatures associated with this pathology. Using these signatures, they interrogated the Connectivity Map (CMap) dataset that contains the effects of over 5000 small molecules on the transcriptome of human cell lines and looked for molecules whose effects on transcription mimic or oppose those of the CS. As expected, molecules that potentiate immune responses such as PKC activators are predicted to worsen the CS. In addition, the researchers identified the negative regulation of female hormones among pathways potentially aggravating the CS, which helps to understand the gender-related differences in COVID-19 mortality. Regarding drugs potentially counteracting the CS, they identified glucocorticoids as a top hit, which validates their approach as this is the primary treatment for this pathology. Interestingly, the analysis also reveals a potential effect of MEK inhibitors in reverting the COVID-19 CS, which is supported by in vitro data that confirms the anti-inflammatory properties of these compounds.
An in silico analysis identifies drugs potentially modulating the cytokine storm triggered by SARS-CoV-2 infection
Oscar Fernandez-Capetillo
Added on: 03-16-2022
Machine learning model for risk assessment of corona intensive care patients
2022
Charité - Universitätsmedizin Berlin, Berlin, Germany
Clinically established risk assessments, such as SOFA and APACHE II scores, show limited performance in predicting the survival of critically ill COVID-19 patients. In order to optimize the treatment and allocation of intensive care resources, plasma proteomes of two independent patient cohorts (Germany and Austria) were analyzed in order to predict the outcome (death vs. survival) in severe cases. In time series samples, 14 inflammatory proteins were identified that increased significantly in patients with fatal outcomes. In survivors, a decrease in these proteins in plasma was observed in parallel. The proteomic predictors showed high accuracy in the outcome prediction, while the established methods evaluated in parallel performed significantly worse. However, time series samples are time-consuming and labour-intensive and therefore unsuitable for clinical diagnostics and therapy decisions. Therefore, the research group developed a machine learning model whose predictions are based on single-time samples taken at the first point in time at the maximum treatment stage (WHO grade 7) of the patients. The AI analyzes individual protein pairs and calculates the probability of a lethal outcome by evaluating individual deviations from the overall population. The model identified 15 proteins of the coagulation system and 8 proteins of the complement cascade as highly relevant in a severe form with a fatal outcome. Based on proteomics data from the patient cohort from Germany, the AI correctly predicted the outcome for 18 out of 19 patients who survived and for 5 out of 5 patients who died in the Austrian cohort. The results show that the plasma proteome comprehensively reflects the host's response to COVID-19 and significantly improves the clinical diagnosis of corona-risk patients. The proteome analysis and development of further predictors can prove helpful in improving the diagnosis and therapy choice for other diseases.
A proteomic survival predictor for COVID-19 patients in intensive care
Florian Kurth
Added on: 08-11-2022
Patient study to investigate dynamic processes in the brain
2022
Max Planck Institute of Psychiatry, Munich, Germany
So far, little is known about the dynamic processes in the brain during acute stress, as research usually focuses on which areas are active at a given time.
Here, 217 subjects with and without affective disorders such as depression and anxiety disorders were observed over the entire period of a stressful situation (solving a math problem under time pressure). In addition to magnetic resonance imaging images, the researchers measured the stress hormone cortisol and heart rate.
The dynamic response of the networks in the subjects' brains during the stressful situation varied. Not only were changes found in the communication between brain regions, but also a dynamic process: different networks acted differently during acute stress. From this, the scientists could determine how susceptible a person was to a negative mood and how this increased his or her risk of mental illness. The results of the study thus show how different brain regions interact and how their communication changes over the course of the situation. These findings could be significant for the development of individual diagnoses and therapies.
Spatiotemporal dynamics of stress-induced network reconfigurations reflect negative affectivity
Elisabeth B. Binder, Anne Kühnel
Added on: 11-21-2022
Virtual Da Vinci Skills Simulator
2022
Lahey Hospital and Medical Center, Burlington, USA
Robotic-assisted surgery requires extended skills to safely perform various surgeries on patients. For one of the best-known robotic surgery control consoles, the Da Vinci surgical system, a virtual learning platform is available. Different levels provide a wide skills training, including EndoWrist manipulation, camera and clutching, fourth arm integration, system settings, needle control and driving as well as usage and control of the footswitch panels. Surgeon trainees can practice without being supervised as the saved metrics allow assessing skills and tracking progress. Besides education, the simulator can be used as a warm-up right before a (complicated) surgery.
Da Vinci Skills Simulator
Augustus Gleason
Augustus Gleason et al. Journal of Robotic Surgery 2022 [92]
Intuitive Surgical, Inc. [93]
Mimic Technologies [94]
Added on: 06-20-2022
COVID-19 can cause vascular damage to the heart
December 2021
Georg-August-Universität Göttingen, Göttingen, Germany(1)
Medizinische Hochschule Hannover (MHH), Hannover, Germany(2)
Medizinische Hochschule Hannover (MHH), Hannover, Germany(2)
For the first time, the researchers have used phase-contrast X-ray tomography to characterize the 3D structure of cardiac tissue from patients who succumbed to COVID-19. By extending conventional histopathological examination by a third dimension, the delicate pathological changes of the vascular system of severe COVID-19 progressions can be analysed, fully quantified and compared to other types of viral myocarditis and controls. Cardiac samples were scanned at a laboratory setup as well as at a parallel beam setup at a synchrotron radiation facility. The vascular network was segmented by a deep learning architecture suitable for 3D datasets, trained by sparse manual annotations. Pathological alterations of vessels were observed, indicative of an elevated occurrence of intussusceptive angiogenesis. The corresponding distributions show that the histopathology of COVID-19 differs from both influenza and typical coxsackievirus myocarditis.
3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography
Tim Salditt(1), Danny Jonigk(2)
Added on: 03-16-2022
Drugs can affect the gut microbiota in different ways
December 2021
European Molecular Biology Laboratory (EMBL), Heidelberg, Germany(1)
Sorbonne Université, Paris, France(2)
University of Leipzig Medical Center, Leipzig, Germany(3)
Sorbonne Université, Paris, France(2)
University of Leipzig Medical Center, Leipzig, Germany(3)
During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, the researchers show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. They quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shifts the metabolome and microbiome towards a healthier state. Different relationships between antibiotics, cardiometabolic drug dosage, improvement in clinical markers and microbiome composition are presented. Taken together, this computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using this framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.
Combinatorial, additive and dose-dependent drug–microbiome associations
Peer Bork(1), Karine Clément(2), Michael Stumvoll(3)
Added on: 04-04-2022
Eye exam can predict heart attack
December 2021
The University of Edinburgh, Edinburgh, United Kingdom
There is increasing evidence that retinal vascular complexity (measured as fractal dimension, Df) may provide earlier insights into coronary artery disease (CAD) progression before conventional biomarkers can be detected. Here, we present a genome-wide association study (GWAS) aimed at elucidating the genetic component of Df and analyzing its association with CHD. To this end, Df was extracted from retinal fundus images and genotyping information of approximately 38,000 participants from the UK Biobank. The researchers detected 9 loci associated with Df that have been previously reported in studies of pigmentation, retinal width and tortuosity, hypertension, and CAD. Significant negative genetic correlation estimates confirmed the inverse association between Df and CAD and between Df and myocardial infarction (MI). Based on these findings, a model for predicting myocardial infarction combining clinical information, Df, and a polygenic CAD risk score was developed using a random forest algorithm. The results shed new light on the genetic basis of Df, revealing a common control with CAD and highlighting the advantages of its application in individualized MI risk prediction.
Decreased retinal vascular complexity is an early biomarker of MI supported by a shared genetic control
Miguel O. Bernabeu
Added on: 07-06-2022
Personalised brain models to improve depression treatment
December 2021
Aix-Marseille Université, Marseille, France(1)
Ewha Womans University, Seoul, South Korea(2)
University of Calgary, Calgary, Canada(3)
Ewha Womans University, Seoul, South Korea(2)
University of Calgary, Calgary, Canada(3)
Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient. The engagement of specific fibre tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here a computational framework that can explore each individual's brain response characteristics was provided, elicited by selective stimulation of fibre tracts. A novel personalised in silico approach was used, the Virtual Big Brain, which makes use of high-resolution virtual brain models at an mm-scale and explicitly reconstructs more than 100,000 fibre tracts for each individual. Each fibre tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient. This study provides evidence for the capacity of personalised high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalised optimization of brain stimulation.
High-resolution virtual brain modeling personalizes deep brain stimulation for treatment-resistant depression: Spatiotemporal response characteristics following stimulation of neural fiber pathways
Viktor K. Jirsa(1), Sora An(2), Andrea B. Protzner(3)
Added on: 05-12-2022
Investigation of the role of epigenetics in amyotrophic lateral sclerosis
November 2021
Hannover Medical School, Hannover, Germany
This study investigates the role of epigenetics in amyotrophic lateral sclerosis (ALS). Therefore, ALS patient-derived induced pluripotent stem cells (iPSCs) and healthy control-derived iPSCs were generated and characterized. Neural progenitor cells from healthy control cell lines and ALS cell lines, carrying a mutation in the fused in sarcoma (FUS) gene, were differentiated into motor neurons. These motor neurons showed typical ALS pathology with cytoplasmic FUS aggregates. Expression and promoter methylation of the FUS gene and expression of DNA methyltransferases were analyzed and compared to healthy control cell lines.
The described approach can be used to investigate epigenetic modification as novel therapeutic targets.
Methylation and expression of mutant FUS in motor neurons differentiated from induced pluripotent stem cells from ALS patients
Susanne Petri
Added on: 08-23-2022
Largest open-source database for bone marrow cell images developed
November 2021
Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
Every day, cytologists around the world use optical microscopes to analyse and classify samples of bone marrow cells thousands of times. This method to diagnose blood diseases was established more than 150 years ago, but it suffers from being very complex. Here, the researchers developed the largest open-access database on microscopic images of bone marrow cells to date. The database consists of more than 170,000 single-cell images from over 900 patients with various blood diseases. On top of the database, the researchers have developed a neural network that outperforms previous machine learning algorithms for cell classification in terms of accuracy, but also in terms of generalizability. This study is a step toward automated evaluation of bone marrow cell morphology using state-of-the-art image-classification algorithms. The researchers aim to further expand their bone marrow cell database to capture a broader range of findings and to prospectively validate their model. The database and the model are freely available for research and training purposes – to educate professionals or as a reference for further AI-based approaches, e.g. in blood cancer diagnostics.
Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set
Carsten Marr
Added on: 05-12-2022
Predicting protein interactions with artificial intelligence
November 2021
University of Texas Southwestern Medical Center, Dallas, USA(1)
University of Washington, Seattle, USA(2)
University of Washington, Seattle, USA(2)
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown and there are likely many interactions not yet identified. This study takes advantage of the advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modelling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. A combination of RoseTTAFold and AlphaFold was used to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identifying 1505 possibly interacting proteins and building structural models for 106 previously unidentified compounds and for 806 compounds that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.
Computed structures of core eukaryotic protein complexes
Qian Cong(1), David Baker(2)
Added on: 01-28-2022
Single cell transcriptomics to reveal pathological pathways in ALS
November 2021
University of Exeter, Exeter, United Kingdom
Amyotrophic lateral sclerosis is a neurodegenerative disease driven by the loss of motor neurones. Here SOD1 E100G amyotrophic lateral sclerosis patient-derived induced pluripotent stem cells were used to perform single-cell transcriptomics analysis to identify signalling pathways related to the pathological outcome in dysfunctional neurones. The results showed several pathways and transcriptional factors leading to gene expression dysregulation, building an ALS-relevant transcriptional network map. SMAD2, a downstream effector of TGF-beta, was elucidated as a critical factor in SOD motor neurone degeneration. Moreover, TGF-beta was activated in different variations of amyotrophic lateral sclerosis, both familiar and sporadic cases. According to these findings, inhibition of TGF-beta improved diseased SOD1 motor neurone survival. Overall, the researchers demonstrate the utility of single-cell transcriptomics to uncover pathological pathways in neurodegenerative diseases, and this allows them to identify several SOD1-associated targets in perturbed motor neurone transcriptional networks.
Single-cell transcriptomics identifies master regulators of neurodegeneration in SOD1 ALS iPSC-derived motor neurons
Akshay Bhinge
Added on: 11-29-2021
Transcriptomic analysis of hiPSC-derived cardiomyocytes uncovers key maturation signalling pathways
November 2021
A*STAR Institute of Molecular and Cell Biology, Singapore, Singapore
Human pluripotent stem-cell-derived cardiomyocytes represent an exciting tool for disease modelling and cell therapy. However, there are still severe limitations in their applications. Here, a human hexokinase1-GFP metabolic reporter cell line was used to discriminate between fetal and mature phenotypes of differentiated cardiomyocytes and perform transcriptomic analysis to identify key gene regulatory pathways involved in cardiomyocyte maturation. The results showed that the interferon-signalling pathway was differentially regulated between both cell types. Furthermore, cardiomyocytes treated with Interferon-gamma had increased JAK-STAT signalling and improved maturation, both in gene expression profiles and functional physiological outcomes. Consequently, inhibition of the JAK-STAT pathway with ruxolitinib resulted in the inhibition of these functional improvements. Overall, the researchers uncover key regulatory pathways implicated in vitro in human cardiomyocytes maturation which could be potentially used to improve differentiation protocols and increase the relevance of stem cell-based cardiac models.
Upregulation of the JAK-STAT pathway promotes maturation of human embryonic stem cell-derived cardiomyocytes
Boon-Seng Soh, Shi-Yan Ng
Added on: 11-29-2021
Transformational machine learning method may speed up drug discovery
November 2021
University of Cambridge, Cambridge, United Kingdom
The researchers have developed a new approach to machine learning (ML) that ‘learns how to learn’ and out-performs current ML methods for drug design, which in turn could accelerate the search for new disease treatments. The method, called transformational machine learning (TML) learns from multiple problems and improves performance while it learns. TML could accelerate the identification and production of new drugs by improving the machine learning systems which are used to identify them.
Transformational machine learning: Learning how to learn from many related scientific problems
Ross D. King
Added on: 03-10-2022
Machine learning models predict antibiotic resistance spread
October 2021
Cornell University, Ithaca, USA
Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer (HGT), although their relative contributions are unclear. Here, the researchers apply machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits in recent HGT events. They find that high-probability not-yet detected antibiotic resistance genes transfer events are almost exclusive to human-associated bacteria. This approach is robust at predicting the HGT networks of pathogens, including Acinetobacter baumannii and Escherichia coli, as well as within localized environments, such as an individual’s gut microbiome.
Functions predict horizontal gene transfer and the emergence of antibiotic resistance
Ilana Lauren Brito
Added on: 02-23-2022
Particulate matter damages cognitive performance
October 2021
University of Rostock, Rostock, Germany
In the present study, the effects of particulate matter on lung function and cognitive performance of 49,705 people were investigated in long-term cohorts. The people all lived in areas with relatively low levels of air pollution. Furthermore, socio-demographic characteristics such as age, gender, education, existing pre-existing conditions and much more were taken into account in the evaluation of the results. The Cogstate Brief Battery (CBB), a validated computer-aided method, was used to assess the cognitive performance of the participants. The results showed that higher exposure to particulate matter is significantly associated with slower cognitive processing time (CPT). By means of mediation analyses, it was investigated whether the neurotoxicological pollutants reach the brain directly via the olfactory nerve or the bloodstream, or are indirectly mediated by lung function. This showed that particularly fine particles predominantly follow the direct path and even small amounts are sufficient to induce cognitive impairment. Furthermore, the hypothesis was confirmed that air pollutants enter the lungs by inhalation, which impairs lung function and can lead to pneumonia. On the basis of the results, further ways in which pollutants can specifically damage organs are now to be investigated.
Long-term exposure to fine particulate matter, lung function and cognitive performance: A prospective Dutch cohort study on the underlying routes
Benjamin Aretz
Added on: 08-17-2022
Physiologically based kinetic model for cardiotoxicity prediction
October 2021
Wageningen University and Research, Wageningen, Netherlands
In this study, the applicability of an invitro-in silico approach to predict human cardiotoxicity of the herbal alkaloid ibogaine and its metabolite noribogaine were investigated. Physiologically based kinetic (PBK) models were developed using in silico-derived parameters and biokinetic data obtained from in vitro human liver microsomal incubations and human colorectal cells (Caco-2) transport studies. Human induced pluripotent stem cell-derived cardiomyocytes combined with a multi-electrode array assay were used to determine in vitro concentration-dependent cardiotoxicity. To evaluate the model, the results were compared with the human in vivo data reported in clinical studies. A comparison of the predictions with the available in vivo data demonstrated the adequate performance of the developed model.
A new approach methodology (NAM) for the prediction of (nor)ibogaine-induced cardiotoxicity in humans
Ivonne M. C. M. Rietjens
Added on: 04-22-2022
Vascular damaging SARS-CoV-2 proteins identified
October 2021
Tel Aviv University, Tel Aviv, Israel
COVID patients have a very high incidence of vascular disease and blood clots, which can lead to a heart attack or stroke. In the present study, researchers succeeded in identifying proteins of the SARS-CoV-2 virus that cause vascular damage. The researchers used viral RNA extracted from blood samples from patients. To analyze viral protein expression in human tissue, the researchers used an in vitro vascular endothelial model cultured from human umbilical vein endothelial cells (HUVEC). Five of the 29 SARS-CoV-2 proteins induced significant mechanical and functional damage to blood vessel cells. Furthermore, an increased tendency to clotting blood was observed. Using a newly developed computer-aided method, the researchers were able to predict the effect of the viral proteins on other tissue types based on the newly obtained data. The identification of endothelial-damaging proteins and the AI-based forecasts represent a promising basis for further biomedical research to investigate the exact enzyme-induced pathomechanisms of the coronavirus and to develop suitable drugs more quickly.
Effect of SARS-CoV-2 proteins on vascular permeability
Ben Meir Maoz
Added on: 08-08-2022
Data tool may uncover novel class of GPCRs
2021
Queen’s University Belfast, Belfast, United Kingdom(1)
University of London, London, United Kingdom(2)
University of London, London, United Kingdom(2)
In this study, the researchers have developed a computer-aided data tool that could uncover a novel class of G protein-coupled receptors (GPCRs) and lead to treatment for a range of illnesses. GPCRs are drug targets in many therapeutic areas such as inflammation, infertility, metabolic and neurological disorders, viral infections and cancer. Currently over a third of drugs act via GPCRs. Recent studies have uncovered the existence of allosteric sites that drugs can bind to and provide several therapeutic benefits. The researchers developed a computer-aided protocol to map allosteric sites in GPCRs with a view to start a rational search for allosteric drugs, presenting the opportunity for new solutions and therapies for a range of diseases. According to the team, the computer modelling tool will predict novel sites of binding for potential drugs that are more selective, leading to more effective drug targeting, increasing therapeutic efficacy and reducing side effects. Specifically, the data tool or protocol will uncover a novel class of compounds – allosteric drugs in GPCRs.
Probe confined dynamic mapping for G protein-coupled receptor allosteric site prediction
Irina G. Tikhonova(1), Peter J. McCormick(2)
Added on: 02-23-2022
In-silico model to predict springtail toxicity
2021
Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
Soil pollution is a critical environmental challenge. In this study, two quantitative structure activity relationship (QSAR) models for the prediction of reproductive toxicity induced by organic compounds in the springtail Folsomia candida were developed.
Therefore, 28 days No Observed Effect Concentration (NOEC) data were used that were collected from publicly available databases. A dataset with 54 compounds including plant protection products, industrial chemicals, household and cosmetic ingredients, drugs, and other substances was assembled. 80% of the data was used to train the models, while 20% were used to validate them. The models were developed using partial least squares regression (PLS) and the Monte Carlo technique with open-source tools.
Both QSAR models gave good predictive performance and could serve for the ecological risk assessment of chemicals for terrestrial organisms.
QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida
Diego Baderna
Added on: 02-29-2024
New AI method could aid the development of T cell-based cancer therapies
2021
MD Anderson Cancer Center, Houston, USA(1)
University of Texas Southwestern Medical Center, Dallas, USA(2)
University of Texas Southwestern Medical Center, Dallas, USA(2)
In this study, the researchers have developed an artificial intelligence (AI) technique that can identify which cell surface peptides produced by neoantigens are recognized by the immune system. By applying this method, called pMTnet, to human tumour genomics data, the authors discovered that neoantigens were generally more immunogenic than self-antigens, but human endogenous retrovirus E (a special type of self-antigen that is reactivated in kidney cancer) is more immunogenic than neoantigens. They further discovered that patients with more clonally expanded T cells that exhibit better affinity against truncal rather than subclonal neoantigens had a more favourable prognosis and treatment response to immunotherapy in melanoma and lung cancer but not in kidney cancer. Thus, pMTnet could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies which may help develop treatments such as cancer vaccines and T-cell based therapies.
Deep learning-based prediction of the T cell receptor–antigen binding specificity
Alexandre Reuben(1), Tao Wang(2)
Added on: 02-23-2022
Robotic system for real-time analysis of inhaled submicron and microparticles
2021
University of Pittsburgh, Pittsburgh, USA
Vitamin E acetate (VEA) has been strongly associated with the onset of lung injury caused by the use of electronic cigarettes (EC) or vaping products. To understand whether VEA affects the disposition profile of inhaled particles, a biologically inspired robotic system was developed to quantitatively analyze submicron and microparticles produced by e-cigarettes in real-time while mimicking clinically relevant breathing and vapour topography exactly as in humans. This allowed the researchers to observe that the addition of even small amounts of VEA was sufficient to alter the size distribution and significantly increase the total number of particles inhaled by ECs. In addition, the usefulness of the biomimetic robot for studying the influence of nicotine and respiratory profiles in obstructive and restrictive lung diseases was demonstrated.
Accordingly, the platform is capable of providing information on exposure to ingredients and additives of e-liquid cigarettes, thus facilitating health risk assessment.
A robotic system for real-time analysis of inhaled submicron and microparticles
Kambez H. Benam
Added on: 10-20-2021
Synthesis of imagined speech processes from minimally invasive recordings of neural activity
2021
Maastricht University, Maastricht, Netherlands(1)
University of Bremen, Bremen, Germany(2)
University of Bremen, Bremen, Germany(2)
Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and notably improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high-quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which might not directly translate to imagined speech processes. Here, an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions was presented. Using a participant implanted with stereotactic depth electrodes, the authors were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. While reconstructed audio is not yet intelligible, the real-time synthesis approach represents an essential step toward investigating how patients will learn to operate a closed-loop speech neuroprosthesis based on imagined speech.
Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity
Christian Herff(1), Miguel Angrick(2)
Added on: 03-01-2022
Advancing personalized cancer research with machine learning
2021
The Barcelona Institute of Science and Technology, Barcelona, Spain
Researchers led by ICREA researcher Dr. Núria López-Bigas at IRB Barcelona have developed a tool, based on machine learning methods, that evaluates the potential contribution of all possible mutations in a gene in a given type of tumour to the development and progression of cancer.
The new tool has been integrated into the IntOGen platform, developed by the same group and designed to be used by the scientific and medical community in research projects, and into the Cancer Genome Interpreter, also developed by this group and which is more focused on clinical decision-making by medical oncologists.
BoostDM currently works with the mutational profiles of 28,000 genomes analysed from 66 types of cancer. The scope of BoostDM will grow as a result of the foreseeable increase in publicly accessible cancer genomes.
The tool has already generated 185 models to identify mutations in a specific gene in a given type of cancer. For example, it has produced a model that has identified all the possible mutations in the EGFR gene that trigger tumour development in some lung cancers, and another model for the same gene in cases of glioblastoma.
In silico saturation mutagenesis of cancer genes
Nuria Lopez-Bigas, Abel Gonzalez-Perez, Ferran Muiños
Added on: 10-04-2021
AI-based platform for predicting and decoding protein structures
2021
DeepMind, London, United Kingdom
In order to optimize and accelerate the decoding and prediction of protein structures, the company DeepMind has developed the AI-based computer network AlphaFold. To predict protein structure, AlphaFold uses sequential as well as structural information from various data sets. The platform is based on a deep convolutional neural network trained with millions of known protein structures from the experimental Protein Data Bank (PDB). To develop a deep learning algorithm, the system analyses the primary amino acid sequence of a protein while taking into account evolutionary, physical, and geometric information that influences the spatial arrangement of atoms in a protein. Thanks to the integrated machine learning system, the system is able to independently identify missing contexts and optimizes the protein structure predictions in a continuous training process. The results of the present study show that by combining bioinformatics and physical approaches, AlphaFold can predict the three-dimensional structure of individual proteins extremely precisely and with almost experimental accuracy, and also enables a detailed representation of complex protein structures. The proof of concept of the method was carried out as part of the 14th Critical Assessment of Protein Structure Prediction (CASP14).
Highly accurate protein structure prediction with AlphaFold
John Jumper
Added on: 10-19-2023
Bioengineered optogenetic model of human neuromuscular junction
2021
Columbia University, New York, USA(1)
Gladstone Institutes, San Francisco, USA(2)
Gladstone Institutes, San Francisco, USA(2)
Functional human tissues derived from patient-specific induced pluripotent stem cells (hiPSCs) hold promise for controlled and systematic research into the progression, mechanisms, and treatment of musculoskeletal diseases. Here, the researchers describe a standardized method for producing an isogenic, patient-specific human neuromuscular junction (NMJ) that enables automated quantification of NMJ function to diagnose disease using a small blood serum sample and evaluate novel therapeutic modalities. By combining tissue engineering, optogenetics, microfabrication, optoelectronics, and video processing, a novel platform has been created for the precise study of human NMJ development and degeneration. The study demonstrates the utility of this platform for the detection and diagnosis of myasthenia gravis, an antibody-mediated autoimmune disease that disrupts NMJ function.
Bioengineered optogenetic model of human neuromuscular junction
Gordana Vunjak-Novakovic(1), Olaia F. Vila(2)
Added on: 10-04-2021
Computational method to analyse brain dynamics
2021
Universitat Pompeu Fabra, Barcelona, Spain
Nowadays, functional magnetic resonance imaging generates satisfactory representations of the brain. However, brain dynamics during different states remain unclear. Here, an analysis method using dimensionality reduction is developed to decode imaging data and elucidate spatiotemporal dynamics of brain activity in different states. The results elucidated nonlinear differences between different states and these data allowed the researchers to efficiently classify them. Moreover, further analysis of a subject group revealed a shared topology between individuals constrained by the brain state rather than the differences between participants. Overall, the researchers revealed the intrinsic manifold that describes brain dynamics and enables decoding and classifying different brain states.
Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep
Joan Rué-Queralt
Added on: 12-01-2021
Simulating drug concentrations in PDMS organ chips
2021
Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, USA
Microfluidic organ-chip cell cultures are often prepared from polydimethylsiloxane (PDMS) because it is biocompatible, transparent, elastomeric, and permeable to oxygen. However, hydrophobic small molecules can be absorbed on PDMS, which complicates the prediction of drug responses. Here, we describe a combined experimental and computational approach that simulates spatial and temporal drug concentration profiles in 3D under continuous dosing in dual-channel microfluidic organ chips lined with bronchial epithelium and pulmonary microvascular endothelium. This strategy involves the development of a simulation of drug absorption in a PDMS organ chip and experimental quantification of the diffusion and distribution coefficients of the drug. The computational model is then used to estimate the drug concentrations experienced by the cells at each time point in the microfluidic channels of the chip. Applied the method to simulate the concentrations of the antimalarial drug amodiaquine when administered continuously under flow in human lung airway chips. This strategy can estimate the loss of substance due to PDMS absorption in any device composition and should therefore help improve experimental design and analysis of dose-response studies and toxicity studies in PDMS organ chips.
Simulating drug concentrations in PDMS microfluidic organ chips
Donald E. Ingber
Added on: 09-08-2022
Computational model to simulate tumorous cell cycle dependent ion current modulation
2021
Graz University of Technology, Graz, Austria
The A549 cell line, derived from non-small cell lung cancer (NSCLC), is a widely used model for the study of lung cancer and the development of anticancer drugs. In this work, the authors present for the first time an electrophysiological model of the A549 human lung adenocarcinoma cell line. The model accounts for the kinetics of the major ion channels contributing to the total membrane current and the resting membrane potential of the cells. Based on experimental data using the whole-cell patch-clamp technique and an extensive literature review, the kinetics of each channel was modelled using a hidden Markov model, and the number of ion channels represented was estimated by fitting the macroscopic currents to the recorded whole-cell currents. The model was parameterized taking into account the specific ion channel activities of the A549 cells obtained from literature data and includes the major functionally expressed ion channels in the plasma membrane of the A549 cells known to date, and also takes into account the respective voltage and calcium dependencies. This approach now allows, for the first time, the simulation of channel interaction, activation and inhibition and, most importantly, the prediction of membrane potential changes for parts of the cell cycle. The availability of this first A549 in silico model 1.0 provides a deeper understanding of the potential roles and interactions of ion channels in tumor development and progression and may aid in the testing, verification, and validation of research hypotheses in lung cancer electrophysiology.
A549 in-silico 1.0: A first computational model to simulate cell cycle dependent ion current modulation in the human lung adenocarcinoma
Christian Baumgartner, Theresa Rienmüller, Sonja Langthaler
Added on: 07-29-2021
Defined approaches on skin sensitisation
Validated Method 2021
OECD Guidelines for the Testing of Chemicals, Paris, France
A Defined Approach (DA) consists of a selection of information sources (e.g in silico predictions, in chemico, in vitro data) used in a specific combination, and resulting data are interpreted using a fixed data interpretation procedure (DIP) (e.g. a mathematical, rule-based model). DAs use methods in combination and are intended to overcome some limitations of the individual, stand-alone methods. The first three DAs included in this Guideline use combinations of OECD validated in chemico and in vitro test data, in some cases along with in silico information, to come to a rules-based conclusion on potential dermal sensitisation hazard. The DAs included in this Guideline have shown to either provide the same level of information or be more informative than the murine Local Lymph Node Assay (LLNA; OECD TG 429) for hazard identification (i.e. sensitiser versus non-sensitiser). In addition, two of the DAs provide information for sensitisation potency categorisation that is equivalent to the potency categorisation information provided by the LLNA. Validated and regulatory accepted under OECD test No. 497.
Defined approaches on skin sensitisation
OECD
Added on: 06-23-2021
Genetic variability of the SARS-CoV-2 pocketome
2021
University of Toronto, Toronto, Canada
The goal of the study was to identify highly conserved binding sites on multiple coronavirus species in order to find putative targets for the development of pan-coronavirus drugs. The authors used an algorithm to systematically map druggable binding pockets on the experimental structure of 15 SARS-CoV-2 proteins and to analyse their variation across 27 coronaviruses and across thousands of SARS-CoV-2 samples from COVID-19 patients. They found the two most conserved druggable sites and present the data on a public web portal (https://www.thesgc.org/SARSCoV2_pocketome/), where users can interactively navigate individual protein structures and view the genetic variability of drug-binding pockets in 3D.
Genetic variability of the SARS-CoV-2 pocketome
Matthieu Schapira
Added on: 10-21-2021
Imaging algorithm predicts Alzheimer's onset with 99% accuracy
2021
Vytautas Magnus University, Kaunas, Lithuania
One of the possible Alzheimer’s first signs is mild cognitive impairment (MCI), which is the stage between the expected cognitive decline of normal ageing and dementia. Functional magnetic resonance imaging (fMRI) can be used to identify the regions in the brain which can be associated with the onset of Alzheimer’s disease. The earliest stages of MCI often have almost no clear symptoms, but in quite a few cases can be detected by neuroimaging.
The researchers have developed a deep learning-based method that can predict the possible onset of Alzheimer’s disease from brain images.
For the model, a modification of well-known fine-tuned ResNet 18 (residual neural network) was used to classify functional MRI images obtained from 138 subjects. The images fell into six different categories: from healthy through the spectre of mild cognitive impairment (MCI) to Alzheimer’s disease. In total, 51,443 and 27,310 images from The Alzheimer’s Disease Neuroimaging Initiative fMRI dataset were selected for training and validation.
The model was able to effectively find the MCI features in the given dataset, achieving a classification accuracy of 99% for early MCI vs. AD, late MCI vs. AD, and MCI vs. early MCI, respectively.
Analysis of features of Alzheimer’s Disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network
Robertas Damaševičius
Added on: 10-07-2021
In-silico trial of aneurism treatment
2021
University of Leeds, Leeds, United Kingdom
In-silico trials rely on virtual populations and interventions simulated using patient-specific models. In this study, a performance evaluation of so-called flow diverters, which are stents used to treat aneurysms, has been carried out. The study includes intracranial aneurysms of 164 virtual patients with 82 distinct anatomies under normotensive and hypertensive conditions. Computational fluid dynamics was used to quantify flow reduction after treatment with a flow-diverting stent.
The predicted flow-diversion success rates replicate the values previously reported in three clinical trials. Moreover, the model allows to perform virtual experiments and sub-group analyses to discover new insights on treatment failure, e.g. in the presence of side-branches or hypertension.
Finally, the model holds the potential to assess individual patients risks of complications and treatment failure and could support clinical decisions.
In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials
Alejandro F. Frangi
Added on: 07-14-2023
Multi-omics profiling predicts allograft function after lung transplantation
2021
Medical University of Vienna, Vienna, Austria
Lung transplantation carries the highest mortality rate among all solid organ transplants because of the risk of chronic lung allograft dysfunction (CLAD). The mechanisms leading to CLAD are not well understood to date. A better understanding of the contribution of the pulmonary microbiome, metabolome, lipidome, and cellular profiles to the development of CLAD requires a holistic assessment of posttransplant dynamics. Here, an exploratory cohort study of bronchoalveolar lavage samples from lung recipients and donors examined the remodelling of the pulmonary milieu after transplantation with the primary goal of identifying time-dependent factors in the process and the secondary goal of describing the causes of lung function deterioration. Variation in cell composition, microbial diversity, lipid and metabolite profiles, and the spirometry parameter FEV1 (forced expiratory volume during the first second) served as statistical endpoints. The alveolar microbiome was analyzed by 16S rRNA sequencing, cellular composition by flow cytometry, and metabolome and lipidome profiling.
The authors found that microbial composition after lung transplantation is primarily determined by environmental and recipient-specific factors, independent of the donor microbiome, and identified selected microbial species that correlated with underlying lung disease even after transplantation. Using a computational model, they were able to predict the evolution of lung function based on multi-omics datasets, with microbial profiles showing particularly high predictive power. According to the authors, the exploratory study design contributes to the knowledge of graft adaptation and thus may be helpful in identifying novel therapeutic approaches to prevent dysfunction in lung allografts.
Multi-omics profiling predicts allograft function after lung transplantation
Sylvia Knapp
Added on: 09-12-2022
Non-invasive investigation of brain states using magnetic resonance imaging
2021
University of Heidelberg, Mannheim, Germany
In this study, network control theory (NCT) was used to investigate transitions between whole-brain neural states measured by functional magnetic resonance imaging (fMRI) during a well-established working memory task. 178 healthy individuals and 24 individuals with schizophrenia were included in the study. Individuals with schizophrenia showed altered network control properties.
Individual prefrontal dopamine receptor expression in each participant was estimated based on genotyping. The hypothesis that the stability of brain states should be related to dopamine receptor function, was tested by functional blocking the receptors using the drugs amisulpride and risperidone in vivo. The obtained data suggest that engagement of working memory involves brain-wide switching between activity states and that the steering of these network dynamics is influenced by dopamine receptor function.
In summary, the utility of NCT for the non-invasive investigation of the mechanistic underpinnings of (altered) brain states and their transitions during cognition was shown.
Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia
Urs Braun
Added on: 10-26-2022
Patient-derived organoid-based radiosensitivity model
2021
Korea Institute of Radiological and Medical Sciences, Seoul, South Korea
In this study, patient-derived tumor organoids were used to determine the correlation between the irradiation response of individual patient-derived rectal cancer organoids and the results of actual radiotherapy of the included 33 patients. Histology and next-generation sequencing analysis confirmed that patient-derived tumor organoids closely recapitulated original tumors, both pathophysiologically and genetically.
A prediction model was developed based on a machine learning algorithm using clinical and experimental radioresponse data. Radiation responses in patients were positively correlated with those in patient-derived tumor organoids. The machine learning-based prediction model for radiotherapy results was demonstrated to have a prediction accuracy of more than 89%.
A patient-derived organoid-based radiosensitivity model for the prediction of radiation responses in patients with rectal cancer
Ui Sup Shin, Younjoo Kim
Added on: 06-23-2022
Screening identifies existing drugs as potential COVID-19 therapies
2021
The Scripps Research Institute, La Jolla, USA
The authors screened a drug repurposing library of over 12000 compounds against two high-throughput, high-content imaging infection assays with human cell lines to find possible COVID-19 drug candidates. They identified 90 existing drugs or drug candidates with antiviral activity against SARS-CoV-2. From these, four are clinically approved drugs and nine compounds are in other stages of development with strong potential to be repurposed as oral drugs for COVID-19.
Drug repurposing screens identify chemical entities for the development of COVID-19 interventions
Thomas F. Rogers, Malina A. Bakowski
Added on: 10-21-2021
Big Data and AI to compare in vitro and in vivo tumours
2021
Johns Hopkins University, Baltimore, USA
Failure to adequately characterize cell lines, and understand the differences between in vitro and in vivo biology, can have serious consequences on the translatability of in vitro scientific studies to human clinical trials. This project focuses on MCF-7 cells (Michigan Cancer Foundation-7), a human breast adenocarcinoma cell line that is commonly used for in vitro cancer research. In this study, the key similarities and differences in gene expression networks of MCF-7 cell lines compared to human breast cancer tissues are explored. Two MCF-7 data sets (ARCHS4 including 1032 samples and Gene Expression Omnibus GSE50705 with 88 estradiol-treated MCF-7 samples) and one human breast invasive ductal carcinoma (BRCA) data set (The Cancer Genome Atlas, including 1212 breast tissue samples) are used. Weighted Gene Correlation Network Analysis (WGCNA) and functional annotations of the data show that MCF-7 cells and human breast tissues have only minimal similarity in biological processes, although some fundamental functions, such as cell cycle, are conserved. Scaled connectivity—a network topology metric—also show drastic differences in the behaviour of genes between MCF-7 and BRCA data sets. Finally, canSAR is used to compute ligand-based druggability scores of genes in the data sets, and the results suggest that using MCF-7 to study breast cancer may lead to missing important gene targets.
Similarities and differences in gene expression networks between the breast cancer cell line Michigan Cancer Foundation-7 and invasive human breast cancer tissues
Alexandra Maertens
Added on: 06-30-2021
Biomarker detects severe COVID-19 early on
2021
University of Zurich, Zurich, Switzerland
Disease progression in COVID-19 varies among patients infected with the SARS-CoV-2 virus. While most patients experience harmless symptoms, some patients suffer from severe illness. To assess whether the immune profile of ICU COVID-patients is specific for SARS-CoV-2 or is driven by a general inflammation also seen in non-SARS-CoV-2 pneumonia, blood samples from both kinds of patients and controls were taken. The samples were analyzed via longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis to characterize their immune pattern. Results show that, besides some similarities in immune profile, in SARS-CoV-2 infections a special sort of T killer cells can serve as a predictive biomarker to assess the severeness of the disease even at an early point of time, leading to better monitoring and care of the patients.
Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia
Burkhard Becher
Added on: 05-17-2022
Brain-computer interface technique to assist neurorehabilitation
2021
University of Bath, Bath, United Kingdom
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so shared control strategies were tried as an alternative solution. In this paper, a brain-actuated robotic arm system based on a novel shared control model with a hybrid BCI scheme was proposed. Specifically, a shared controller was built, which dynamically integrated the human intention with machine autonomy and intelligently optimized the robotic arm control process based on the actual control context. The adoption of the hybrid BCI scheme with SI and PI in this study aimed to extend the dimensionality of BCI control and optimize the BCI resources (e.g. decoding computing power, GUI occupation) for the system. The experiment results showed in the current system, all eleven subjects could pick the desired target from
multiple objects under shared control and ten could complete the pick-place task. Moreover, the experiment results also demonstrated that shared control outperformed the pure BCI control, indicating shared control may be a promising method for brain-actuated systems.
This technology could improve the activities of daily living of people with disabilities.
A brain-actuated robotic arm system using noninvasive hybrid brain–computer interface and shared control strategy
Dingguo Zhang
Linfeng Cao et al. Journal of Neural Engineering 2021 [146]
Physicians Committee for Responsible Medicine [147]
Added on: 09-12-2022
Brain-computer interface turns mental handwriting into text
2021
Stanford University School of Medicine, Stanford, USA
Using an implanted sensor to record the brain signals associated with handwriting, scientists have developed a brain-computer interface (BCI) designed to restore the ability to communicate in real-time in people with spinal cord injuries and neurological disorders such as amyotrophic lateral sclerosis (ALS).
By implanting two small sensors on a patient’s brain, researchers were able to decipher the brain activity associated with trying to write letters by hand. A machine-learning algorithm was used to identify letters as the patient attempted to write them, then the system displayed the text on a screen.
Other BCIs for restoring communication exist; however, they have shown to be imprecise and have several limitations. In this study, the participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general purpose autocorrect. Researchers hope this technology may one day help restore the ability to communicate with patients with similar problems.
High-performance brain-to-text communication via handwriting
Francis R. Willett
Added on: 07-02-2021
Computer-based simulation for drug development
Company 2021
VeriSIM Life, San Francisco, USA
In the present study, a patented biosimulation software platform (BIOiSIM) for optimizing drug development is presented. Pharmacokinetic (PK) predictions based on traditional in vivo studies are time-consuming, costly and characterized by high susceptibility to error. By simulating (personalized) plasma concentrations, the new computer software makes it possible to calculate a data set with all relevant information about the absorption, distribution, metabolism, and excretion of a new active substance (ADME) in up to 5 hours. Based on the results of the study, the predictions on the efficacy and toxicity of new drugs have been demonstrably improved by the analysis and optimization of PK-relevant parameters, in particular the specific plasma partition coefficients (Kp) for organs and tissues. The AI-supported method thus reduces the health risk for subjects in clinical trials and, in parallel, generates a valid database of global drug dispositions for future drug development through the integration of mechanistic models and machine learning.
A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning integrated modeling platform
Jyotika Varshney
Added on: 08-02-2022
In-silico model for T. platyurus acute toxicity
2021
Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
The crustacean Thamnocephalus platyurus is increasingly used in ecotoxicity testing. The aim of this study was to develop computational models to predict toxicity in T. platyurus. Therefore, Quantitative Structure-Activity Relationship (QSAR) models were established for the prediction of acute toxicity.
Publicly available LC50 data for 72 organic compounds were used for model development, and the principles of QSAR modelling recommended by the Organization for Economic Cooperation and Development (OECD) were followed.
Despite the availability of only a small data set, the models gave promising results and identified descriptors with an important impact on toxicity towards T. platyurus. The additional descriptor analyses also provide a mechanistic interpretation of the results.
Ecotoxicological QSAR modeling of the acute toxicity of organic compounds to the freshwater crustacean Thamnocephalus platyurus
Diego Baderna
Added on: 03-11-2024
Machine learning predicts treatment response of COVID-19 patients
2021
Imperial College London, London, United Kingdom
This is the first study that examines daily changing clinical parameters of COVID-19 patients and uses AI to understand the clinical response to the rapidly changing needs of patients in intensive care units.
While the AI model was used to a retrospective cohort of patient data collected during the pandemics' first wave, the study demonstrates the ability of AI methods to predict patient outcomes using routine clinical information used by clinicians in intensive care units.
The new findings show that the AI model identified factors that determined which patients were likely to get worse and not respond to interventions such as proning. The researchers found that during the first wave of the pandemic, patients with blood clots or inflammation in the lungs, lower oxygen levels, lower blood pressure and lower lactate levels were less likely to benefit from being proned. Overall, proning improved oxygenation in only 44% of patients.
According to the authors, this approach of analysing each patient's data day by day, rather than just at admission, could be used to improve clinical practice guidelines.
Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom
Aldo A. Faisal, Brijesh V. Patel
Added on: 06-18-2021
Recovery of speech in stroke patients predicted by computer simulation
2021
Boston University, Boston, USA(1)
The University of Texas at Austin, Austin, USA(2)
The University of Texas at Austin, Austin, USA(2)
Predicting language therapy outcomes in bilinguals with aphasia (BWA) remains challenging due to the multiple pre-and post-stroke factors that determine the deficits and recovery of their two languages. Computational models that simulate language impairment and treatment outcomes in BWA can help predict therapy response and identify the optimal language for treatment. Here, the BiLex computational model is used to simulate the behavioural profile of language deficits and treatment response of a retrospective sample of 13 Spanish-English BWA who received therapy in one of their languages. Specifically, their pre-stroke naming ability and post-stroke naming impairment in each language were simulated, and their treatment response in the treated and the untreated language. BiLex predicted treatment effects accurately and robustly in the treated language and captured different degrees of cross-language generalization in the untreated language in BWA. A cross-validation approach further demonstrated that BiLex generalizes to predict treatment response for patients whose data were not used in model training. These findings support the potential of BiLex to predict therapy outcomes for BWA and suggest that computational modelling may be helpful to guide individually tailored rehabilitation plans for this population.
Predicting language treatment response in bilingual aphasia using neural network-based patient models
Claudia Peñaloza(1), Uli Grasemann(2)
Added on: 07-01-2021
SoftWipe: a tool for assessing the quality of scientific software
2021
Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
Scientific software from all areas of scientific research is pivotal to obtaining novel insights. Yet the coding standards adherence of scientific software is rarely assessed, even though it might lead to incorrect scientific results in the worst case. Therefore, the authors have developed an open-source tool and benchmark called SoftWipe, that provides a relative software coding standards adherence ranking of 48 computational tools from diverse research areas. SoftWipe can be used in the review process of software papers and to inform the scientific software selection process so scientists can choose the best in silico method for their studies.
The SoftWipe tool and benchmark for assessing coding standards adherence of scientific software
Alexandros Stamatakis
Added on: 06-21-2021
X-ray lightsource identifies candidates for COVID drugs
2021
Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
The P11 beamline of the PETRA III research light source specializes in structural biology studies. Here, the three-dimensional structure of proteins can be imaged with atomic precision. Using this method, which employs protein crystallography, several candidates for drugs against the SARS-CoV-2 coronavirus have been identified. They bind to an important protein of the virus and could thus be the basis for a drug against COVID-19. In a so-called X-ray screening, the researchers quickly tested almost 6,000 active substances that already exist for the treatment of other diseases. A total of 37 substances were identified that bind to the main protease of the SARS-CoV-2 virus. Seven of these substances inhibit the activity of the protein and thus slow down the multiplication of the virus. The active substances Calpeptin and Pelitinib clearly showed the highest antiviral effects with good cell compatibility. This drug screening also revealed a new binding site on the main protease of the virus which drugs can target.
X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease
Alke Meents, Sebastian Günther
Added on: 04-20-2021
AI to decode the language of cancer and Alzheimer's disease
2021
University of Cambridge, Cambridge, United Kingdom
Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. And dysfunction is seen as a trigger for cancer and neurodegenerative diseases such as Alzheimer's disease. To understand how protein sequence determines phase behaviour and to develop an algorithm to predict LLPS-prone sequences(liquid-liquid phase separation), datasets of proteins with different LLPS propensities were created. The DeePhase model showed high performance in both distinguishing LLPS-prone proteins from structured proteins and identifying them within the human proteome. Overall, the results shed light on the physicochemical factors that modulate protein condensation and provide a molecular principles-based platform for predicting protein phase behavior.
Learning the molecular grammar of protein condensates from sequence determinants and embeddings
Tuomas P. J. Knowles
Added on: 04-19-2021
Deep learning predicts early cancer onset
2021
Max-Planck-Institut für Molekulare Genetik, Berlin, Germany
The EMOGI (Explainable Multi-Omics Graph Integration) is a machine learning method to identify cancer genes. Based on human patient data, the deep learning algorithm combines data for mutations, copy number changes, DNA methylation and gene expression with the protein-protein interaction for a more accurate prediction of early molecular signs of cancer than other methods known to date. Genetic alterations, as well as non-genetic causes, drive tumorigenesis and some non-mutated genes interact with known cancer genes. 165 novel cancer genes were proposed by this method. EMOGI may open new possibilities for therapeutic substance identification in personalized precision oncology. The method is not restricted to oncology, it can be used in other complex diseases with genetic and non-genetic biomarkers like metabolic disorders to identify disease patterns.
Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms
Annalisa Marsico
Added on: 04-26-2021
Dynamic model of SARS-CoV-2 spike protein reveals vaccine targets
2021
Max Planck Institute of Biophysics, Frankfurt, Germany
The spike protein of the SARS-CoV-2 virus is key for infection and the primary antibody target. However, the spike is covered by highly mobile glycan molecules that could impair antibody binding. To identify accessible epitopes, the researchers performed molecular dynamics simulations of an atomistic model of a glycosylated spike embedded in a membrane. By combining extensive simulations with bioinformatics analyses, they recovered known antibody binding sites and identified several epitope candidates as targets for further vaccine development. This computational epitope-mapping procedure is general and should thus prove useful for other viral envelope proteins which structures have been characterized.
Computational epitope map of SARS-CoV-2 spike protein
Gerhard Hummer
Added on: 05-11-2021
Four subtypes of Alzheimer's disease identified by artificial intelligence
2021
Lund University, Lund, Sweden(1)
McGill University, Montréal, USA(2)
McGill University, Montréal, USA(2)
Previously, the pattern of spread of tau pathology in Alzheimer's disease (AD) was thought to be fairly uniform, but recent work has shown considerable variability in the distribution. Here, tau positron emission tomography scans of 1,612 individuals were performed and a computer program and artificial intelligence were used to look for distinctive patterns in the distribution of tau proteins. This revealed that there were four clearly distinguishable patterns for the distribution of tau proteins. The subtypes exhibited different demographic and cognitive profiles and had different longitudinal outcomes. In addition, network diffusion models implied that pathology in the different subtypes originates and propagates through different corticolimbic networks. The results suggest that a reexamination of the term "typical AD" based on a classification of tau pathology would be useful.
Four distinct trajectories of tau deposition identified in Alzheimer’s disease
Oskar Hansson(1), Jacob W. Vogel(2)
Added on: 05-11-2021
Machine learning method to design better antibody drugs
2021
ETH Zurich, Basel, Switzerland
The researchers have created a machine learning method that supports the optimization phase of antibody drugs, potentially helping to develop more effective therapeutics. The standard antibody optimization approach allows the identification of the best antibody from a group of a few thousand. The researchers are now using machine learning to increase the initial set of antibodies to be tested to several million. They provided the proof-of-concept for their new method using antibody cancer drug Herceptin, which has been on the market for 20 years. After screening more than 70 million antibody DNA sequences, the scientists characterized 55 unique antibody variants, some of which bound better to the target protein and one variant was even better tolerated in the body than Herceptin itself. The scientists are now applying their artificial intelligence method to optimize antibody drugs that are in clinical development.
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Sai T. Reddy
Added on: 10-21-2021
Risk genes for haemorrhoidal diseases identified
2021
Christian-Albrechts-University of Kiel, Kiel, Germany(1)
CIC bioGUNE - BRTA, Derio, Spain(2)
CIC bioGUNE - BRTA, Derio, Spain(2)
Haemorrhoidal disease (HEM) affects a large amount of the population around the globe, however, the genetic predisposition is rarely understood. In order to determine genetic correlations, an analysis using the software METAL was performed including over 200.000 HEM patients and over 700.000 healthy individuals, By this, more than 102 gene loci were identified which seem to be linked to the disease. These genes' expression is enriched in blood vessels, gastrointestinal tissues as well as in pathways associated with smooth muscles, epithelial and endothelial development and morphogenesis. This provides new insights on HEM genetic predisposition and may help to identify individuals at risk in an early state.
Genome-wide analysis of 944 133 individuals provides insights into the etiology of haemorrhoidal disease
Andre Franke(1), Mauro D'Amato(2)
Added on: 05-05-2021
Three new multiple sclerosis subtypes identified using AI
2021
University College London, London, United Kingdom
Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. To classify MS subtypes based on pathological features, the artificial intelligence tool SuStaIn (Subtype and Stage Inference) was applied to MRI scans of the brain acquired in previously published studies. A training dataset from 6322 MS patients was analysed to define MRI-based subtypes and an independent cohort of 3068 patients was used for validation. Based on the earliest abnormalities, the authors define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. The findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.
Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
Arman Eshaghi
Added on: 04-15-2021
Virtual Reality platform for identification of the cause of rare diseases
2021
University of Vienna, Vienna, Austria
Networks offer a powerful way to visualize and analyze complex systems. However, protein interactions in the human body constitute such a complex system that can hardly be visualized. The immersive virtual reality (VR) platform VRNetzer can solve this problem by facilitating the thorough visual, and interactive, exploration of large networks. The platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, the researchers show how VRNetzer can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development.
The VRNetzer platform enables interactive network analysis in Virtual Reality
Jörg Menche
Added on: 05-11-2021
3D computer models to study brain mechanics
2021
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
At FAU in Erlangen, 3D brain computer models have been generated that are composed of small cubes representing the different brain areas. With the help of these models, it is possible to look at the mechanics of individual brain areas. This could be used, for example, to simulate operations, but also to improve disease diagnoses. In the case of many brain diseases such as epilepsy, schizophrenia, Alzheimer's disease and Parkinson's disease, symptoms only become apparent at a very late stage. Thanks to the models, these diseases could be detected earlier. Additionally, this method is used to identify substitute materials for soft tissues, such as hydrogels, which have similar mechanical properties to natural tissues. Thus, it also contributes to the field of tissue engineering, i.e. tissue construction and cultivation.
Silvia Budday
Added on: 03-23-2021
AI method for generating proteins for a fast drug development
2021
Chalmers University of Technology, Gothenburg, Sweden
ProteinGAN is artificial intelligence (AI) software that can ‘learn’ natural protein sequence diversity and enables the generation of functional protein sequences. It learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino-acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Unlike the very slow classical protein engineering, ProteinGAN allows researchers to go from computer design to working protein in just a few weeks, driving down development costs. The new AI-based approach is important for developing efficient industrial enzymes and new protein-based therapies, such as antibodies and vaccines.
Expanding functional protein sequence spaces using generative adversarial networks
Aleksej Zelezniak
Added on: 05-11-2021
AI-based analysis system for the diagnosis of breast cancer
2021
Charité Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany(1)
Technische Universität Berlin, Berlin, Germany(2)
Technische Universität Berlin, Berlin, Germany(2)
The study describes a new tissue-section analysis system for diagnosing breast cancer based on artificial intelligence (AI). Two developments make this system unique: For the first time, morphological, molecular and histological data are integrated into a single analysis. Secondly, the system provides a clarification of the AI decision process in the form of heatmaps. Pixel by pixel, these heatmaps show which visual information influenced the AI decision process and to what extent, thus enabling doctors to understand and assess the plausibility of the results of the AI analysis. This represents a decisive and essential step forward for the future regular use of AI systems in hospitals.
Morphological and molecular breast cancer profiling through explainable machine learning
Frederick Klauschen(1), Klaus-Robert Müller(2)
Added on: 05-11-2021
Cloud computing to investigate the link between the visual system and neurodegeneration
2021
Indiana University, Bloomington, USA
The degree to which glaucoma has effects in the brain beyond the eye and the visual pathways is unclear. To clarify this, researchers investigated white matter microstructure (WMM) in 37 tracts of patients with glaucoma, monocular blindness, and controls. Data were collected among the ophthalmologic patient populations of two Universities in Japan and the Netherlands. For analysing the reproducibility, the platform brainlife.io was used. White matter tracts were subdivided into seven categories ranging from those primarily involved in vision (the visual white matter) to those primarily involved in cognition and motor control. In the vision tracts, WMM was decreased as measured by fractional anisotropy in both glaucoma and monocular blind subjects compared to controls, suggesting neurodegeneration due to reduced sensory inputs. A test-retest approach was used to validate these results. The pattern of results was different in monocular blind subjects, where WMM properties increased outside the visual white matter as compared to controls. This pattern of results suggests that whereas in the monocular blind loss of visual input might promote white matter reorganization outside of the early visual system, such reorganization might be reduced or absent in glaucoma. The results provide indirect evidence that in glaucoma unknown factors might limit the reorganization as seen in other patient groups following visual loss.
White matter alterations in glaucoma and monocular blindness difer outside the visual system
Franco Pestilli, Sandra Hanekamp
Added on: 07-01-2021
Computational model to predict pathological von Willebrand factor unravelling
2021
Lehigh University, Bethlehem, USA
The unravelling of von Willebrand factor is critical in clot formation during vascular injury. However, without injury, it can lead to certain pathologies. Here, a computational model of the globular-to-unravelled transition rate of von Willebrand factor subjected to different blood flow conditions was used to evaluate the role of blood flow in the trigger of disrupted unravelling in healthy conditions. The results identified the elongational flow of strain rate which, after periodic exposure of von Willebrand factor, could induce undesired unravelling. Overall, the researchers propose a simulation workflow to analyze the impact of blood flow in different biological processes and elucidate key mechanisms that could drive pathological behaviour.
Predicting pathological von Willebrand factor unraveling in elongational flow
Edmund Webb III
Added on: 11-30-2021
Machine learning calculates affinities of drug candidates and targets
2021
Massachusetts Institute of Technology, Cambridge, USA
A new technology combining chemistry and machine learning could aid researchers during the drug discovery and screening process. The new technique, called DeepBAR, quickly calculates the binding affinities between drug candidates and their targets. The approach yields precise calculations in a fraction of the time compared to previous methods. The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.
DeepBAR: a fast and exact method for binding free energy computation
Bin Zhang
Added on: 05-11-2021
Neandertal gene variants influence progression of COVID-19
2021
Karolinska Institutet, Stockholm, Sweden(1)
Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany(2)
Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany(2)
The authors show that a haplotype on chromosome 12, which is associated with a ∼22% reduction in relative risk of becoming severely ill with COVID-19 when infected by SARS-CoV-2, is inherited from Neandertals. This haplotype is present at substantial frequencies in all regions of the world outside Africa. The genomic region where this haplotype occurs encodes proteins that are important during infections with RNA viruses.
A genomic region associated with protection against severe COVID-19 is inherited from Neandertals
Hugo Zeberg(1), Svante Pääbo(2)
Added on: 05-11-2021
Novel method of generating sensation using a brain computer interface
2021
University of Southern California, Los Angeles, USA
Restoring loss of limb function, including sensation, is an important challenge for patients after spinal cord injury, stroke, or limb amputation. In this study, patients already scheduled for surgical implantation for another clinical application had a mini-electrocorticography grid with mECoG bipolar electrodes implanted over the hand area of the primary somatosensory cortex. Then, the area of the brain corresponding to sensations in the hand was systematically stimulated, and the location and description of each sensation were provided by the patient, successfully demonstrating the utility of this novel sensory brain-computer interface (BCI).
Mapping of primary somatosensory cortex of the hand area using a high-density electrocorticography grid for closed-loop brain computer interface
Daniel R. Kramer
Daniel R. Kramer et al. Journal of Neural Engineering 2021 [185]
Physicians Committee for Responsible Medicine [186]
Added on: 07-01-2021
Biological activity-based modeling identifies SARS-CoV-2 drug candidates
2021
National Center for Advancing Translational Sciences (NCATS), Rockville, USA
Computational approaches for drug discovery, such as quantitative structure-activity relationship, rely on structural similarities of small molecules to infer biological activity but are often limited to identifying new drug candidates in the chemical spaces close to known ligands. Here the researchers report a biological activity-based modeling (BABM) approach, in which compound activity profiles established across multiple assays are used as signatures to predict compound activity in other assays or against a new target. This approach was validated by identifying candidate antivirals for Zika and Ebola viruses based on high-throughput screening data. Furthermore, BABM models were applied to predict 311 compounds with potential activity against SARS-CoV-2.
Biological activity-based modeling identifies antiviral leads against SARS-CoV-2
Wei Zheng, Ruili Huang
Added on: 05-11-2021
Blood biomarker discovery for autism spectrum disorder
2021
The Johnson Center for Child Health and Development, Austin, USA
Using machine learning tools to analyse hundreds of proteins, UT Southwestern researchers have identified a group of biomarkers in blood that could lead to an earlier diagnosis of children with autism spectrum disorder (ASD) and, in turn, earlier and more effective therapies.
For the study, serum samples from 76 boys with ASD and 78 from typically developing boys, all ages 18 months to 8 years, were examined.
More than 1,100 proteins were examined using the SomaLogic SOMAScanTM analysis platform. A panel of nine proteins was identified as optimal for predicting ASD using three computational methods. methods. All nine proteins in the biomarker panel were significantly different in boys with ASD compared with typically developing boys and were significantly correlated with ASD severity as measured by ADOS (Autism Diagnostic Observation Schedule) total scores. The researchers evaluated the biomarker panel's quality using machine learning.
Blood biomarker discovery for autism spectrum disorder: A proteomic analysis
Laura Hewitson
Added on: 03-05-2021
High-throughput transcriptomics platform for screening environmental chemicals
2021
U.S. Environmental Protection Agency, Durham, USA
To accelerate the pace of chemical risk assessments, there is a crucial need to develop new approach methodologies (NAMs) that efficiently provide information about chemical hazards. In the present study, the researchers aimed at leveraging the power of a new whole-transcriptome sequencing assay called TempO-Seq and using it for comprehensive and efficient bioactivity screening of environmental chemicals. The researchers used human cell line MCF7 as the in vitro biological model. The experimental design integrated a small collection of known chemicals to develop robust laboratory and bioinformatics workflows for generating high-throughput transcriptomics data by TempO-Seq in concentration-response that could be scaled up towards testing of 1000s of chemicals in parallel. All bioinformatics workflows were generated using open-source tools to facilitate uptake of the technology. The newly generated assay was validated and compared with existing methods. In conclusion, the study has resulted in a novel and scalable in vitro transcriptomics workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.
High-throughput transcriptomics platform for screening environmental chemicals
Joshua A Harrill
Added on: 12-22-2021
In silico screening of existing drugs reveals three candidates against SARS-CoV-2
2021
Drexel University, Philadelphia, USA
In this study, the researchers present a novel strategy for in silico molecular modelling, screening for potential drugs that may interact with multiple main proteins of SARS‐CoV‐2. Targeting multiple viral proteins is a novel drug discovery concept in that it enables the potential drugs to act on different stages of the virus’ life cycle, thereby potentially maximizing the drug potency. The authors screened 2631 US Food and Drug Administration (FDA)‐approved small molecules against 4 key proteins of SARS‐CoV‐2 that are known as attractive targets for antiviral drug development. In total, 29 drugs that could actively interact with 2 or more target proteins were identified, with 5 drugs being common candidates for all 4 key host proteins and 3 of them possessing the desirable molecular properties.
Virtual screening FDA approved drugs against multiple targets of SARS‐CoV‐2
Hualou Liang
Added on: 05-11-2021
Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
2021
Harvard Medical School, Boston, USA
An artificial intelligence (AI)-based method called DRIAD (Drug Repurposing In Alzheimer’s Disease) is used to screen currently available medications as possible treatments for Alzheimer’s disease. According to the researchers, the method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for the neurodegenerative condition. It could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action.
Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
Artem Sokolov, Mark W. Albers
Added on: 05-11-2021
PathoFact identifies pathogens faster and more accurately
2021
Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg
The authors have developed a new bioinformatics tool based on metagenomic data that can help them identify pathogens much faster and more accurately than it was ever possible with conventional diagnostic methods. Using high-throughput methods, they sequence all of the genome fragments obtained from samples that potentially contain pathogenic organisms. The new bioinformatics tool, PathoFact, compares these gene sequences against an integrated database. PathoFact identifies the genes of microorganisms that are responsible for their pathogenic potential or – in the case of bacteria – their antibiotic resistance. Based on this knowledge, the researchers can determine which pathogens are responsible for an infection and, in future clinical practice, suggest suitable treatments. PathoFact furthermore helps scientists to better understand the influence of microorganisms in the onset of chronic diseases such as Parkinson’s disease or rheumatoid arthritis.
PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data
Paul Wilmes
Added on: 05-11-2021
Population-based determination of cellular differentiation
2021
Max Planck Institute of Molecular Physiology, Dortmund, Germany
During development, cells must specialize their function in a well-defined timeline: the formation of different tissues must be coordinated from a pile of cells. A research group has now developed a new theoretical concept that shows how cells specialize in the right proportions in a coordinated manner through their communication with each other, and thus how new structures are formed and maintained.
Robustness and timing of cellular differentiation through population-based symmetry breaking
Aneta Koseska
Added on: 05-11-2021
Computational tool differentiates between data from cancer cells and normal cells
2021
The University of Texas MD Anderson Cancer Center, Houston, USA
In an effort to address a major challenge when analysing large single-cell RNA-sequencing datasets, researchers have developed a new computational technique to accurately differentiate between data from cancer cells and the various normal cells found within tumor samples.
The new tool, dubbed CopyKAT (copy number karyotyping of aneuploid tumors), allows to more easily examine the complex data obtained from large single-cell RNA-sequencing experiments, which deliver gene expression data from many thousands of individual cells.
CopyKAT uses that gene expression data to look for aneuploidy, or the presence of abnormal chromosome numbers, which is common in most cancers. The tool also helps identify distinct subpopulations, or clones, within the cancer cells.
By applying this tool to several datasets, the authors showed that with about 99% accuracy, the tool could unambiguously identify tumor cells versus the other immune or stromal cells present in a mixed sample.
The team first benchmarked its tool by comparing results to whole-genome sequencing data, which showed high accuracy in predicting copy number changes. In three additional datasets from pancreatic cancer, triple-negative breast cancer and anaplastic thyroid cancer, the researchers showed that CopyKAT was accurate in distinguishing between tumor cells and normal cells in mixed samples.
Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes
Nicholas E. Navin
Added on: 02-11-2021
Influence of toxic metals on neurodegenerative diseases
2021
University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
The aim of this study was to investigate the influence of toxic metals present in the environment on the molecular mechanisms involved in the development of the neurodegenerative diseases (ND) amyotrophic lateral sclerosis (ALS), Parkinson’s Disease (PD) and Alzheimer’s disease (AD). Moreover, it investigated the capability of in silico toxicogenomic data-mining for distinguishing the probable mechanisms of mixture-induced toxic effects. The linkage between neurodegenerative diseases and toxic metals (Pb, MeHg (neurotoxic, organic form of mercury), Cd, As) was explored by analysing the chemical–gene/protein interactions obtained from the Comparative Toxicogenomics Database (CTD; http://CTD.mdibl.org). The CTD data mining analysis revealed the genes connected to each of the investigated metals and linked to the development of the selected neurodegenerative diseases.
SOD2 gene was noted as the mutual gene for all the selected ND. Oxidative stress, folate metabolism, vitamin B12, and apoptosis were noted as the key disrupted molecular pathways that contribute to the neurodegenerative disease’s development. The results emphasize the role of oxidative stress, particularly SOD2, in neurodegeneration triggered by environmental toxic metal mixture and give a new insight into common molecular mechanisms involved in ALS, PD and AD pathology.
Elucidating the influence of environmentally relevant toxic metal mixture on molecular mechanisms involved in the development of neurodegenerative diseases: In silico toxicogenomic data-mining
Danijela Đukić-Ćosić
Added on: 02-05-2024
Prolonged binding results in higher drug efficacy
2021
Goethe University Frankfurt, Frankfurt, Germany
There is increasing evidence that the efficacy of a drug correlates with the residence time of a pharmaceutical substance at its specific binding site. The signalling protein FAK (Focal Adhesion Kinase) plays a role in cancer and inhibition of this kinase induces slowing down breast cancer cells, thus also slowing metastasizing. Analysis of different FAK inhibitors showed that the most effective ones show prolonged residence time at the FAK signalling protein binding site. The binding of effective inhibitors induces a conformational change which explains the prolonged binding. The binding behaviour of the inhibitors can be modelled in computer simulations. The combination of biochemical and molecular biological as well as in silico analyses is a new and promising method for optimizing pharmaceutical active ingredients in the future.
Structure-kinetic relationship reveals the mechanism of selectivity of FAK inhibitors over PYK2
Stefan Knapp
Added on: 03-11-2022
Single-cell test of cancer drugs
2021
University of Chinese Academy of Sciences, Beijing, China
The researchers paired a powerful algorithm with Raman spectroscopy, which involves using a laser to excite photons in a sample to reveal structural information, including interactions. They examined how rapamycin, an anti-cancer drug, changed the metabolic activity in a human cancer cell line and in yeast. The method is able to rapidly and precisely track and distinguish changes in lipid and protein metabolic-inhibitory effect of rapamycin. The method takes just days compared to traditional tests that can take much longer to see if an individual patient's cells will respond favourably to a drug. It is also very precise, as it can distinguish cancer cell responses to drugs at the single-cell and single-organelle resolution, which is crucial for understanding why the drug is - or is not – effective.
D2O-probed Raman microspectroscopy distinguishes the metabolic dynamics of macromolecules in organellar anticancer drug response
Jian Xu, Maryam Hekmatara
Added on: 01-29-2021
Artificial intelligence study to map risks of ovarian cancer
2021
University of South Australia, Adelaide, Australia
Ovarian cancer is usually diagnosed very late because symptoms are vague and few causes are known. A new project from the University of South Australia will map health data from 273,000 women in the UK Biobank database over the next 4 years to determine genetic, dietary, and physical risks of ovarian cancer.
The machine learning model, which automatically analyzes the data to identify risk patterns, will predict which women will develop ovarian cancer in the next 15 years.
The focus will be on metabolomics, as the scientists involved believe that changes in lipid metabolism are biomarkers for ovarian cancer. Hormonal data and biomarkers in the blood will also be examined to better predict risk.
This project could help diagnose ovarian cancer earlier, improving survival rates.
Elina Hyppönen
Added on: 07-29-2021
A new strategy for HCV vaccine development
December 2020
Helmholtz Centre for Infection Research, Braunschweig, Germany(1)
TWINCORE Center of Experimental and Clinical Infection Research, Hannover, Germany(2)
TWINCORE Center of Experimental and Clinical Infection Research, Hannover, Germany(2)
One reason why no vaccine against the hepatitis C virus (HCV) has been found to date is that there are numerous virus variants, some of which differ by more than 30% from each other. In this study, the researchers have succeeded in developing a test system that precisely measures the protective effect of an immune response against the large spectrum of HCV pathogens. First, the researchers first examined neutralising antibodies from the blood of 104 HCV-positive patients. Next, they used bioinformatics methods to divide the viruses into six different groups, known as clusters. Although there is no obvious genetic connection between the viruses in the same neutralisation clusters, they behave very similarly in terms of their susceptibility to antibodies. Thus, it is sufficient to use a test virus from each of the clusters as an example to measure how well the antibodies protect against different HCV variants. Furthermore, vaccination against representatives of the virus clusters may be sensible to build up a broadly protective immune response.
Hepatitis C reference viruses highlight potent antibody responses and diverse viral functional interactions with neutralising antibodies
Alice C. McHardy(1), Thomas Pietschmann(2)
Added on: 05-11-2021
BioNTech use artificial intelligence to develop COVID-19 therapeutics and vaccines
CompanyDecember 2020
InstaDeep, London, United Kingdom
The biopharmaceutical company BioNTech and the Artificial Intelligence (AI) product developer InstaDeep collaborate by using AI and machine learning to discover decoy proteins with a potentially higher binding affinity than that of the human receptor, which the coronavirus uses to attack cells. Such proteins would prevent the sequence of events leading to the release of the viral genome and viral replication. In the future, the two companies will work together to discover and develop novel immunotherapies like mRNA-based vaccines.
Designing a prospective COVID-19 therapeutic with reinforcement learning
Marcin J. Skwark
Added on: 02-05-2021
Detection of hidden tumors by imaging and machine learning
December 2020
National Cancer Center, Kashiwa, Japan
The diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor lesions like GIST are covered by a mucosal layer and thus can be overseen. To overcome this issue, near-infrared hyperspectral imaging (NIR-HSI) was combined with machine learning. 12 gastric GIST lesions were surgically resected and imaged ex vivo with a near-infrared (NIR) hyperspectral camera. The site of the GIST was defined by a pathologist using the NIR image to prepare training data for normal and GIST regions. A machine learning algorithm was then used to predict normal and GIST regions. Accuracy was around 86%, therefore NIR-HSI analysis may have the potential to distinguish deep lesions. An endoscope is planned in order to use the method in vivo during standard endoscopy.
Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging
Toshihiro Takamatsu
Added on: 02-09-2021
In silico analysis for antiviral therapies against SARS-CoV-2
December 2020
University of Tübingen, Tübingen, Germany
The authors used a computational approach to discover new metabolic antiviral targets against SARS-CoV-2. An integrated host-virus genome-scale metabolic model of human alveolar macrophages and SARS-CoV-2 identified the enzyme guanylate kinase as one potential target, showing that its knock-out prevents virus growth, while not affecting the host. Since guanylate kinase inhibitors are described in the literature, the authors propose the assessment of their potential therapeutic effects for SARS-CoV-2 infections.
FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2
Andreas Dräger, Alina Renz
Added on: 01-06-2021
Ligand selection strategy improves drug discovery targeting viruses
December 2020
University of Vienna, Vienna, Austria
Virus-specific proteases are essential for the cellular virus replication and are thus the focus for drug discovery strategies. Using a ligand selection strategy, chemical electrophilic probes were identified that target highly specifically the active site of these enzymes. Thus, even in live-cell models and complex cell samples, these probes can be used to detect proteases like the two SARS-CoV-2 proteases analysed in this study. Screening for identification of customized inhibitors is another application of this method. These drugs are more likely to be effective when used to treat infections. The method is suitable for high-throughput screening and for discovering inhibitors for a wide range of target proteins also beyond coronavirus proteases. A patent has been filed for the method.
A ligand selection strategy identifies chemical probes targeting the proteases of SARS‐CoV‐2
Thomas Böttcher
Added on: 01-29-2021
AI for detecting cerebral aneurysms with CT angiography
November 2020
Huazhong University of Science and Technology, Wuhan, China
A highly sensitive deep learning-based algorithm for automated detection of cerebral aneurysms on CT angiography images was introduced. A total of 1068 CT angiograms were evaluated used for the training and the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5%. Moreover, eight new aneurysms that had been overlooked in the initial reports were detected. Using this algorithm radiologists’ performance in detecting aneurysms improved, especially for less experienced radiologists.
Deep learning for detecting cerebral aneurysms with CT angiography
Xi Long
Added on: 11-12-2020
AI predicts schizophrenia symptoms in at-risk population
November 2020
National Institute of Mental Health and Neuro Sciences, Bangalore, India(1)
University of Alberta, Edmonton, Canada(2)
University of Alberta, Edmonton, Canada(2)
First-degree relatives of schizophrenia patients have up to a 19 per cent risk of developing schizophrenia during their lifetime, compared with the general population risk of less than one per cent. The tool EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction) can predict a diagnosis of schizophrenia with 87 per cent accuracy by examining patient brain scans. Functional magnetic resonance images of 57 healthy first-degree relatives (siblings or children) of schizophrenia patients were analyzed. The method accurately identified the 14 individuals who scored highest on a self-reported schizotypal personality trait scale.
The next step is to test the accuracy of the tool on nonfamilial individuals with schizotypal traits and to track assessed individuals over time to learn whether they develop schizophrenia later in life.
Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives
Ganesan Venkatasubramanian(1), Sunil Vasu Kalmady(2)
Added on: 02-11-2021
Grapes, green tea, cacao and chocolate against COVID-19
November 2020
North Carolina State University, Raleigh, USA
The ability of different plant flavonoids to bind to and inhibit the main protease (Mpro) of SARS-CoV-2 was analysed via in silico docking simulations and in vitro inhibitory experiments. Five compounds were identified to have anti-Mpro activity. Crude extracts from green tea, cacao, chocolate, and two muscadine grapes, which are rich in flavan-3-ols and proanthocyanidins, also showed inhibitory effects on the Mpro activity. Based on these data, the authors propose that increased consumption of these common products can enhance COVID-19 prevention.
Docking characterization and in vitro inhibitory activity of flavan-3-ols and dimeric proanthocyanidins against the main protease activity of SARS-CoV-2
De-Yu Xie
Added on: 01-04-2021
New therapeutic approach for bone marrow diseases
November 2020
CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
Mutations of calreticulin (CALR) are one of the main disease drivers of myeloproliferative neoplasms (MPN), a group of malignant diseases of the bone marrow. In-silico-docking studies, i.e. computer-assisted simulation of the disease mechanism, identified a group of chemicals that block a crucial interaction. It was shown in a cell model that hematoxylin, the most potent of the tested compounds, acts as a novel CALR inhibitor. Mutated CALR cells are selectively killed by using this drug. Thus, a decisive step towards the therapy of this type of blood cancer has been achieved and patients with primary myelofibrosis (PMF), who often develop myeloid leukaemia, could greatly benefit from it.
Hematoxylin binds to mutant calreticulin and disrupts its abnormal interaction with thrombopoietin receptor
Robert Kralovics
Added on: 12-18-2020
Predicting the toxicity of nanoparticles for safer industrial materials
November 2020
Jožef Stefan Institute, Ljubljana, Slovenia
The prediction of diseases associated with nanomaterials is currently hampered by an incomplete understanding of the underlying mechanisms. As part of the EU project "SmartNanoTox", it has now been found that for special materials, the long-term inflammatory response of the lung to a single nanoparticle exposure can be attributed to two previously unknown key cellular events. First, a new quarantine process, i.e. the deposition of excreted particles enveloped by biological molecules on the cell surface; second, the so-called nanomaterial cycle, which conditions the uptake and excretion of nanoparticles between different alveolar lung cell types. With the help of a few in vitro measurement data in combination with in silico modelling, the scientists were able to predict the acute or chronic toxicity of nanoparticles and thus the course of inflammatory reactions in the lung for 15 different materials.
Prediction of chronic inflammation for inhaled particles: the impact of material cycling and quarantining in the lung epithelium
Janez Štrancar, Hana Kokot
Added on: 12-14-2020
Train AI to adapt like human brains
November 2020
Salk Institute for Biological Studies, La Jolla, USA
The prefrontal cortex (PFC) enables humans’ ability to flexibly adapt to new environments and circumstances. Disruption of this ability is often a hallmark of prefrontal disease. Neural network models have provided tools to study how the PFC stores and uses information, yet the mechanisms underlying how the PFC is able to adapt and learn about new situations without disrupting preexisting knowledge remain unknown. Here a neural network architecture called DynaMoE is used to show how hierarchical gating can naturally support adaptive learning while preserving memories from prior experience. Furthermore, the authors show how damage to the network model recapitulates disorders of the human PFC.
A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex
Ben Tsuda, Terrence J. Sejnowski
Added on: 12-21-2020
Artificial intelligence helps in the search for biomarkers for Alzheimer's
October 2020
University of Pennsylvania School of Medicine, Philadelphia, USA
Researchers of 12 research centers will collaborate to determine more precise diagnostic biomarkers and drug targets for Alzheimer´s disease. For the project, the teams will apply advanced artificial intelligence (AI) methods to integrate and find patterns in genetic, imaging, and clinical data from over 60,000 Alzheimer's patients. The project's first objective will be to find a relationship between the three modalities (genes, imaging, and clinical symptoms), in order to identify the patterns that predict Alzheimer's diagnosis and progression -- and to distinguish between several subtypes of the disease.
The investigators will then use those findings to build a predictive model of cognitive decline and Alzheimer's disease progression, which can be used to steer treatment for future patients.
Using advanced AI to discover diagnostic biomarkers and drug targets for Alzheimer’s
Christos Davatzikos
Added on: 11-12-2020
Deep learning makes synthetic biology comprehensible
October 2020
Massachusetts Institute of Technology, Cambridge, USA
A set of machine learning algorithms was developed, that can analyse reams of RNA-based "toehold" sequences and predict which ones will be most effective at sensing and responding to a desired target sequence. These achievements could be helpful to better understand the fundamental principles of RNA folding. This method demonstrates the power of combining computational with synthetic biology to develop new and more powerful bioinspired technologies. It also leads to new insights into the fundamental mechanisms of biological control. The algorithms could be applied to other problems in synthetic biology and could accelerate the development of biotechnological tools.
A deep learning approach to programmable RNA switches
James J. Collins
Added on: 11-11-2020
New cancer diagnostics: A glimpse into the tumor in 3D
October 2020
TU Wien, Vienna, Austria
The authors describe a novel approach that allows pathologists to three-dimensionally analyse malignant tissues, including the tumour-host tissue interface. The visualization technique utilizes a combination of ultrafast chemical tissue clearing and light-sheet microscopy to obtain virtual slices and 3D reconstructions of up to multiple centimetre-sized tumour resectates. Since the imaging of several thousands of optical sections is a fast process, it is possible to analyse a larger part of the tumour than by mechanical slicing. As this also adds further information about the 3D structure of malignancies, the technology will probably become a valuable addition for histological diagnosis in clinical pathology.
3D histopathology of human tumours by fast clearing and ultramicroscopy
Hans-Ulrich Dodt, Inna Sabdyusheva Litschauer
Added on: 11-06-2020
Personalized medicine to study the role of microorganisms in chronic diseases
October 2020
Macquarie University, Sydney, Australia(1)
PANDIS, Sydney, Australia(2)
PANDIS, Sydney, Australia(2)
An increasing number of studies links microorganisms to chronic diseases such as tumours and brain diseases like Alzheimer's or Parkinson's disease. PANDIS is an Australian consortium of patients, clinicians and scientists investigating the role of microorganisms in chronic diseases. The main focus is on diseases caused by ticks.
The formulation of data points and the use of bioinformatics allows sorting pathogenic from productive and anaerobic from aerobic microbes. The data are merged with information about the individual lipid and nutrient sources and the nutrient profile of the patient to obtain a detailed profile of the microbiome. RNA metagenomics is used to detect both known and unknown microorganisms. In addition, the concentrations of different biomarkers in the blood of patients are determined to obtain specific signatures for subtypes of infections. All the data obtained are stored in a database and analysed using artificial intelligence.
Personalized medicine model aiming to improve diagnosis and treatment of chronic diseases
Gilles Guillemin(1), Catherin Stace(2)
Added on: 11-13-2020
Sorting out viruses with machine learning
October 2020
Osaka University, Osaka, Japan
The authors developed a label-free method for identifying respiratory viruses based on machine-learning classification. Over 99% accuracy was demonstrated for five different virus species. This work may lead to fast and accurate screening tests for diseases like COVID-19 and influenza.
Digital pathology platform for respiratory tract infection diagnosis via multiplex single-particle detections
Tomoji Kawai
Added on: 12-17-2020
State-of-the-art AI methods used to study Alzheimer’s
October 2020
USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Los Angeles, USA
In the National Institutes of Health-funded initiative "Ultrascale Machine Learning to Empower Discovery in Alzheimer's Disease Biobanks" (AI4AD), 11 research centres have joined forces to use artificial intelligence and machine learning to support Alzheimer's research into causes and treatments. Experts from computer science, genetics, neurosciences and imaging sciences are involved.
The project's first objective is to identify genetic and biological markers that predict Alzheimer's diagnosis and distinguish between several subtypes of the disease. To accomplish this, the research team will apply sophisticated AI and machine learning methods to a variety of data types, including tens of thousands of brain images and whole-genome sequences. The investigators will then relate these findings to the clinical progression of Alzheimer's, including in patients who have not yet developed dementia symptoms. The AI methods will be trained on large databases of brain scans to identify patterns that can help detect the disease as it emerges in individual patients.
State-of-the-art AI methods used to study Alzheimer’s
Paul Thompson
Added on: 11-19-2020
Tumor diagnostics with a combination of machine learning and biochip
October 2020
University of California Irvine, Irvine, USA
Performing single-cell analysis is essential to identify and classify cancer cell types and study cellular heterogeneity. This study combines powerful machine learning techniques with easily accessible inkjet printing and microfluidics technology and integrates a nanoparticle-printed biochip for single-cell analysis. The biochip is simple to prototype, miniaturized and cost-effective, potentially capable of differentiating between a variety of cell types in a label-free manner. Feature classifiers are established and their performance metrics are evaluated. The biochip’s ability to discriminate noncancerous cells from cancerous cells at the single-cell level and to classify cancer sub-type cells is demonstrated. It is envisioned that such a chip has potential applications in single-cell studies, tumour heterogeneity studies, and perhaps in point-of-care cancer diagnostics.
A machine learning-assisted nanoparticle-printed biochip for real-time single cancer cell analysis
Rahim Esfandyarpour
Added on: 11-11-2020
X-ray analysis of the movement of malaria and toxoplasmosis pathogens
October 2020
Molecular Biology Laboratory (EMBL), Hamburg, Germany
The study provides new insights into the molecular machinery by which certain parasites travel through the human organism. It analyses the so-called gliding movement of malaria and toxoplasmosis parasites. In biological terms, gliding refers to the type of movement during which a cell moves along a surface without changing its shape. Using X-ray crystallography, the researchers analysed the molecular structure of myosin essential light chains that actively contributes to parasite gliding.
Structural role of essential light chains in the apicomplexan glideosome
Christian Löw
Added on: 11-06-2020
Flu may increase the spread of COVID-19
2020
Max Planck Institute for Infection Biology, Berlin, Germany
A population-based model of SARS-CoV-2 transmission, combined with mortality incidence was used to study the first months of the corona pandemic in Europe. The results show that the decrease of COVID-19 cases in spring was not only related to countermeasures but also to the end of the flu season. Influenza may have increased transmission of the coronavirus by an average of 2 to 2.5-fold. The results of the study suggest that the coming flu epidemic will have an amplifying impact on the COVID-19 pandemic. The researchers emphasize the potential importance of flu vaccinations as possible extra protection against COVID-19.
Influenza may facilitate the spread of SARS-CoV-2
Matthieu Domenech de Celles
Added on: 09-24-2020
Organ-on-a-Chip platform for pharmacogenetic predictions
Company 2020
Javelin Biotech, Woburn, USA
Javelin Biotech and Pfizer will work together on a platform to predict the pharmacokinetics of new drug candidates. They intend to study compounds for absorption, distribution, metabolism and excretion (ADME) using an organ-on-a-chip platform containing various human tissues (e.g. kidney, intestine, liver) and circulating culture media. The model will also be coupled to a computational algorithm that will translate the obtained data into a physiologically relevant model of human pharmacokinetics.
Javelin, Pfizer to develop organ-on-a-chip platform
info@javelinbio.com
Added on: 10-13-2020
Patient-derived cells and brain organoids uncover treatment targets in Parkinson’s disease
2020
University of Luxembourg, Luxembourg, Luxembourg
Mutations in PARK7 lead to the development of early-onset Parkinson’s disease (PD). The authors of this study identified an exonic splicing mutation in PARK7 linked to PD and studied the effect of this mutation in patient-derived cellular models. The mutation resulted in impaired splicing, reduced production of DJ-1 protein, and consequent mitochondrial dysfunction. Using precise bioinformatics algorithms, the researchers performed an automated drug screen and identified a combination of two substances that rescued the aberrant splicing and neuronal loss in patient-derived brain organoids. The results suggest that precision medicine targeting specific molecular signatures could be an effective strategy for PD and possibly other neurodegenerative diseases.
A patient-based model of RNA mis-splicing uncovers treatment targets in Parkinson’s disease
Rejko Krüger
Added on: 09-25-2020
The roles of glycans in the SARS-CoV-2 spike protein
2020
University of California San Diego, San Diego, USA
The study provides new insights into the SARS-CoV-2 spike protein and its glycan envelope. By means of molecular dynamics simulations, an important structural role of the N-glycans at specific positions (N165 and N234) could be revealed. Biolayer interferometry experiments showed that the binding of the spike protein to the angiotensin-converting enzyme 2 (ACE2) is reduced when glycans are no longer present at these positions. On this basis, new strategies to combat COVID-19 could be developed. In addition, computer programs can identify regions of the SARS-CoV-2 membrane spike proteins that are ensheathed by glycans to a lesser extent and are therefore suitable as potential targets.
Beyond shielding: The roles of glycans in the SARS-CoV-2 spike protein
Rommie E. Amaro
Added on: 10-01-2020
Artificial intelligence algorithm for prostate cancer diagnosis in core needle biopsies
Validated Method 2020
UPMC Cancer Pavilion, Pittsburgh, USA
An artificial intelligence (AI) algorithm could successfully diagnose prostate cancer in stained digitized slides of core needle biopsies in a clinical study. The algorithm was able to accurately assess both the stage of the disease and clinically relevant findings, such as perineural invasion. It is already routinely used in the clinic.
An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study
Liron Pantanowitz
Added on: 09-25-2020
Brain areas decoding acoustic and visual communication cues
2020
University of Dundee, Dundee, United Kingdom
Visual speech through lip movements is a key component of communication, but the brain mechanisms that process these visual and auditory cues are still unclear. Here, a multifactorial whole-brain magnetoencephalography (MEG) classification of volunteers was used to identify brain areas activated during different tests of auditory and visual communication. The results showed which areas processed auditory and visual mediated word identities. However, only two brain areas were identified that were activated along with auditory and visual cues and were clearly separated from other areas that represented sensory-mediated word identity. Overall, the researchers elucidate which brain areas are activated by two different types of communication and suggest that word comprehension may be more specific to communication channels than is currently thought.
Shared and modality-specific brain regions that mediate auditory and visual word comprehension
Anne Keitel
Added on: 11-30-2021
Combination of machine learning and brain imaging create better diagnostics for mental illness
2020
Hamamutsu University School of Medicine, Hamamatsu City, Japan(1)
The University of Tokyo, Tokyo, Japan(2)
The University of Tokyo, Tokyo, Japan(2)
A computer algorithm was trained on MRI brain scans (magnetic resonance imaging) of 206 autism, schizophrenia and psychosis patients as well as people with no mental health concerns. A total of six different algorithms were used to discriminate between the different MRI images of the patient groups. This allowed associating different psychiatric diagnoses with variations in the thickness, surface or volume of areas of the brain on the MRI images. After a training period, the algorithm was tested with brain scans of another 43 patients. The machine's diagnosis matched the psychiatrists' assessments with high reliability and up to 85 per cent accuracy.
Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis
Hidenori Yamasue(1), Shinsuke Koike(2)
Added on: 09-22-2020
Improved resolution of cryo-electron microscopy
2020
Los Alamos National Laboratory, Los Alamos, USA
The authors improved the quality of the 3D molecular structure maps generated with cryo-electron microscopy by using a density modification method. The theoretical basis of the method is the same as one that has been used in the past to improve structural images from X-ray crystallography. The density-modification procedure was applied to 104 datasets and improved the correlation between the experimental maps and the known atomic structure, increasing the visibility of details in many of the maps.
Improvement of cryo-EM maps by density modification
Thomas C. Terwilliger
Added on: 10-15-2020
A prognostic model for overall survival in sporadic Creutzfeldt‐Jakob disease
2020
University of Münster, Münster, Germany
The authors developed the first prognostic model for overall survival of Creutzfeldt‐Jakob disease patients based only on readily available information from 1226 patients. The model integrates patients’ age, sex, codon 129 genotype, and specific biomarker in the cerebrospinal fluid (CSF tau data). The developed score chart serves as a hands‐on prediction tool for clinical practice.
A prognostic model for overall survival in sporadic Creutzfeldt‐Jakob disease
Nicole Rübsamen
Franc Llorens et al. Alzheimer's & Dementia: The Journal of the Alzheimer's Associatio [257]
Bionity [258]
Added on: 07-22-2020
Computational platform to make deep learning analysis of human genomics data
2020
Max Delbrueck Center for Molecular Medicine, Berlin, Germany
Deep learning methods are computational methods that extract knowledge in a data-driven fashion from large datasets and are used to address a variety of biological questions. Unfortunately, most deep learning methods lack the flexibility to adapt to new data and test new hypotheses. In the present study, the researchers aimed at tackling this issue by using a more flexible approach by separating the pre-processing and dataset specification from the modelling part. The researchers generated Janggu, a library using python programming language that facilitates deep learning for genomics applications, aiming to ease data acquisition and model evaluation. Janggu includes dataset objects that manage the extraction and transformation of information from a range of commonly used file types. Further, these dataset objects are directly compatible with popular and published deep learning libraries which in turn reduces the software engineering effort. Finally, the genomics modelling results may be visualized and exported through commonly used formats. The researchers then validated the use of Janggu on datasets coming from different types of human cells. The Janggu tool is readily applicable and flexible to address a range of questions allowing users to more effectively concentrate on testing biological hypotheses.
Deep learning for genomics using Janggu
Altuna Akalin, Wolfgang Kopp
Added on: 12-20-2021
New organ-on-a-chip system to study the intestine
2020
Utrecht University, Utrecht, Netherlands
In this study, a new intestinal organ-on-a-chip model was developed. The fluid flow was calculated to accurately mimic the shear stress. The results show improved differentiation of the employed CaCo2-cells (a cell line of human colorectal adenocarcinoma) to intestinal epithelial cells (tighter monolayer formation, higher p-cresol metabolic capacity, improved brush border activity and villi formation).
A theoretical and experimental study to optimize cell differentiation in a novel intestinal chip
Rosalinde Masereeuw
Nicky Langerak et al. Frontiers in Bioengineering and Biotechnology 2020 [260]
Utrecht University [261]
Added on: 11-20-2020
Software detects disease-causing gene mutations
2020
Berlin Institute of Health, Berlin, Germany
VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of DNA variant data with a focus on rare disease genetics. The analysed genetic variants are automatically annotated with population frequencies, molecular impact, and presence in multiple databases. VarFish can be used for a fast and accurate diagnosis of rare genetic diseases and has been proven very helpful in enabling researchers and physicians to quickly and accurately diagnose participants for clinical trials.
VarFish: comprehensive DNA variant analysis for diagnostics and research
Manuel Holtgrewe
Added on: 09-17-2020
Tissue engineering and in silico method for the study of cartilage degradation
2020
Charité – Universitätsmedizin Berlin, Berlin, Germany
This study aimed to combine in vitro and in silico modelling based on a tissue-engineering approach using mesenchymal condensation to mimic cytokine-induced cellular and matrix-related changes during cartilage degradation. Scaffold-free cartilage-like constructs (SFCCs) were produced based on self-organizing mesenchymal stromal cells (mesenchymal condensation) and characterized regarding their cellular and matrix composition. SFCCs were treated with interleukin-1β (IL–1β) and tumor necrosis factor α (TNFα) for 3 weeks to simulate OA-related matrix degradation. In addition, an existing mathematical model based on partial differential equations was optimized and transferred to the basic settings to simulate the distribution of IL–1β, type II collagen degradation and cell number reduction. By combining in vitro and in silico methods, the authors aimed to develop a valid, efficient alternative approach to examine and predict disease progression and the effects of new therapeutics.
Macroscale mesenchymal condensation to study cytokine-driven cellular and matrix-related changes during cartilage degradation
Annemarie Lang
Added on: 09-28-2021
Automatic generation and analysis of engineered human myocardium
2020
University Medical Center Göttingen, Goettingen, Germany
In the present study, a protocol for automatic generation, maturation and analysis of artificial human myocardium (EHM) is presented. A multi-tier plate enables the parallel long-term cultivation of up to 48 EHM models from pluripotent stem cells (PSC)-derived cardiomyocytes and fibroblasts. This plate can be included in a fully automated screening process that enables video-optical monitoring and analysis of contraction force and heart rate. The method is therefore suitable for carrying out different test series at the same time, or for pursuing different approaches in drug and disease studies. In summary, the protocol-generated EHM models can help accelerate time-consuming and complex studies, explore pathological mechanisms in greater depth, and identify potential drug candidates for drug development. The method has so far been successfully applied to human embryonic stem cells (HES) and iPSC-derived cardiomyocytes, subtype-specific, i.e. atrial and ventricular, and commercially available cardiomyocyte preparations were used.
Generation of engineered human myocardium in a multi-well format
Malte Tiburcy, Wolfram-Hubertus Zimmermann
Added on: 04-11-2024
Molecular dynamics simulations for drug development
2020
Albert-Ludwigs-Universität, Freiburg, Germany
In molecular dynamics (MD) simulations, the interactions of atoms and molecules and the resulting spatial calculations are estimated and presented step by step. In this way, molecular processes such as protein folding and protein-drug binding can be described, which is important for drug development. Until now, these processes could not be calculated exactly, because the simulation of atomic interactions requires a temporal resolution in the order of femtoseconds, but many processes take several seconds or longer (e.g. binding and dissolving of drugs). Using the "targeted" MD (dcMD), the system dynamics was simplified and successfully tested on the BinAC high-performance computer; the dynamics of binding and dissociation processes could be predicted for several seconds up to half a minute. In addition, the required computing power was significantly reduced.
Multisecond ligand dissociation dynamics from atomistic simulations
Gerhard Stock, Steffen Wolf
Added on: 06-23-2020
Webtool to map chemical effects on the human body
2020
National institute of environmental health sciences, Durham, USA
This study presents a webtool called Tox21BodyMap. It incorporates the results of 9270 chemicals tested in the Tox21 research consortium in 971 high-throughput screening assays. Based on these results, it comprises target organs in relation to the concentration of the chemical and affected gene expression patterns. It enables the user to investigate the potential bioactivity of a selected chemical.
Tox21BodyMap: a webtool to map chemical effects on the human body
Nicole C. Kleinstreuer
Added on: 11-25-2020
Abstract representations of events arise from mental errors
2020
University of Pennsylvania, Philadelphia, USA
Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. This study explores the perspective whether higher-order associations arise from natural errors in learning and memory. Using the free energy principle, which bridges information theory and Bayesian inference, the authors derive a maximum entropy model of people’s internal representations of the transitions between stimuli. The results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources.
Abstract representations of events arise from mental errors in learning and memory
Danielle S. Bassett
Added on: 06-26-2020
Applying knowledge-driven mechanistic inference to toxicogenomics
2020
University of Colorado, Boulder, USA
When considering toxic chemicals in the environment, a mechanistic, causal explanation of toxicity may be preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. The researchers present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, the researchers experimentally validated some of their predictions for other chemicals without an established mechanism of toxicity. This mechanistic inference framework is an advantageous tool for predictive toxicology and the first of its kind to produce a mechanistic explanation for each prediction. MechSpy can be modified to include additional mechanisms of toxicity and is generalizable to other types of mechanisms of human biology.
Applying knowledge-driven mechanistic inference to toxicogenomics
Ignacio J.Tripodi
Added on: 02-14-2022
Human embryo stem cells commit to specialization surprisingly early
2020
The Francis Crick Institute, London, United Kingdom
Working with human embryonic stem cells and mathematical models, a new class of genes was identified, which are responsible for regulating one of the earliest stages of human development. Once these GATA3-genes are activated experimentally, embryonic stem cells are quickly committed to differentiation. Additionally they trigger a positive feedback loop, which helps them stay active. In turn, this ensures that the cells remain differentiated, and do not switch back into stem cell state. Stem cells were derived from embryos donated by people undergoing IVF. The donated embryos were obsolete during fertility treatments and would have been wasted.
GATA3 mediates a fast, irreversible commitment to BMP4-driven differentiation in human embryonic stem cells
Santos, Silvia D.M.
Added on: 05-14-2020
Human genetic data and in-silico-approaches reveal new cancer mechanisms
2020
Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
Using highly sensitive analytical methods (whole-genome sequencing, whole-exome sequencing, RNAseq) and in silico methods, human-relevant, clinically highly relevant data were obtained, showing a much higher incidence of chromothripsis than previously assumed. In addition, it has been shown that these genetic events do not only occur at an early stage but also in late stages or in relapses. The prevalence of genomic instability was previously estimated at a few per cent, but in fact, it occurs in half of the tumour samples investigated. Chromosomes break and the parts are either lost or incorrectly reassembled. These events play an important role in the development of cancer.
The landscape of chromothripsis across adult cancer types
Aurélie Ernst
Added on: 06-25-2020
In silico trial to test COVID-19 candidate vaccines
2020
University of Catania, Catania, Italy
The application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. The presented in silico platform showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approaches to design an effective vaccine, based on a monoclonal antibody. The Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2.
In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
Francesco Pappalardo
Added on: 06-25-2020
New molecular libraries for compound screening
2020
Helmholtz-Zentrum Berlin, Berlin, Germany
A new compound library was set up to accelerate the development of medicines. It consists of 1103 organic molecules that can be used as building blocks of new active ingredients. The compound library is available to researchers worldwide and may also play a role in the search for SARS-CoV-2 drugs.
F2X-Universal and F2X-Entry: structurally diverse compound libraries for crystallographic fragment screening
Manfred S. Weiss
Added on: 07-22-2020
Using bioinformatics for Covid-19 clarification
2020
Heinrich Heine University, Düsseldorf, Germany
A meta-analysis was performed with RNA sequence data from three Sars-CoV-2 studies in human lung epithelial cells. The expression of the angiotensin I converting enzyme 2 (ACE2) correlated with the expression of several genes, like the transmembrane serine protease 4 (TMPRSS4) and genes from the viral life cycle and interferon response. This dataset can contribute to a better understanding of the molecular mechanisms underlying COVID-19.
Meta-analysis of transcriptomes of SARS-Cov2 infected human lung epithelial cells identifies transmembrane serine proteases co-expressed with ACE2 and biological processes related to viral entry, immunity, inflammation and cellular stress
James Adjaye
Added on: 05-25-2020
In silico and in vitro detection of mitochondrial toxicity
2020
University of Vienna, Vienna, Austria
A model was developed for the prediction of mitochondrial toxicity of various substances by combining structure-based methods with in silico methods. The focus was on the human mitochondrial complex I of the respiratory chain, which is known to be blocked by the pesticide rotenone and its analogue deguelin. Based on the common blocking mechanism, virtual screening of databases (DrugBank and Chemspace library) was performed, improved with machine learning and thus, other possibly toxic substances were identified. The complex I inhibitors were verified via testing in LUHMES cell cultures.
Identification of mitochondrial toxicants by combined in silico and in vitro studies – A structure-based view on the adverse outcome pathway
Gerhard F. Ecker
Added on: 09-28-2020
In vitro and in silico nanotoxicology assessment
2020
Swiss Federal Laboratories for Materials Science and Technology, St Gallen, Switzerland
To assess the risk of nanoparticles a combination of in vitro and in silico models is proposed. In silico models are evaluated for their applicability to available data. Recommendations for the use of in vitro data in risk assessment methodologies are provided.
An integrated pathway based on in vitro data for the human hazard assessment of nanomaterials
Peter Wick
Added on: 07-09-2020
Quantum imaging reveals biomolecules
2020
Fraunhofer-Institut für Angewandte Optik und Feinmechanik IOF, Jena, Germany(1)
Fraunhofer-Institut für Physikalische Messtechnik IPM, Freiburg, Germany(2)
Fraunhofer-Institut für Physikalische Messtechnik IPM, Freiburg, Germany(2)
Biosubstances such as proteins, lipids or other chemical elements can be distinguished by their characteristic molecular oscillations. The system uses the quantum mechanical effect of the photon entanglement, which can then be detected and assembled into an image.
In this way, it is possible to determine how certain proteins, lipids or other substances are distributed on a cellular level. For example, some types of cancer have a characteristic accumulation or expression of certain proteins. This would make it possible to detect and combat the disease more efficiently. A more precise knowledge of the distribution of biosubstances could also lead to great progress in drug research.
Andreas Tünnermann(1), Karsten Buse(2)
Added on: 05-26-2020
Drug discovery platform enables ultra-large virtual screens
2020
Harvard Medical School, Boston, USA
VirtualFlow is a highly automated and versatile open-source platform that is able to prepare and efficiently screen ultra-large libraries of compounds. Using VirtualFlow, a very large and freely available ready-to-dock ligand libraries, was prepared, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, the authors screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind with high affinity to target proteins.
An open-source drug discovery platform enables ultra-large virtual screens
Haribabu Arthanari, Christoph Gorgulla
Added on: 05-25-2020
Drug identification with the help of virtual reality
2020
University of Bristol, School of Chemistry, Bristol, United Kingdom
With the help of virtual reality, proteins can be 'docked' and the drugs binding to them can be manipulated in atomic detail using interactive molecular dynamics simulations in VR (iMD-VR). Many drugs work by binding to proteins and thus stopping their action. For example, by binding to a specific viral protein, a drug can stop the reproduction of the virus.
An important part of drug development is to find small molecules that bind tightly to certain proteins and to understand the functions of this binding, which contribute to the development of better drugs. With this three-dimensional iMD-VR approach, drug molecules have been docked to proteins and it has been possible to predict exactly how they bind. Among the systems studied were drugs for influenza and HIV.
Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking
David Glowacki
Added on: 05-27-2020
Identification of genes significant for cancer through analysis of sequence databases and publications
2020
Johns Hopkins University, Baltimore, USA
Sequencing is sufficiently inexpensive and rapid that researchers have at their disposal thousands of tumor tissues with RNA-Seq data, providing unprecedented insight into the transcriptional landscape of cancer. However, the sheer volume of data has proven challenging when it comes to deriving biological meaning. Many types of analysis, rely to some degree on a priori knowledge of the pathways, the biological role, or the molecular function of genes. In the present study, the researchers aimed at drawing attention to the fact that a substantial portion of genes statistically associated with cancer biology lack annotations adequate for understanding their role in cancer pathology. The researchers performed database explorations for genes associated with an unfavourable outcome in cancer using the Human Protein Pathology Atlas which contains a correlation of mRNA and clinical outcome for almost 8,000 cancer patients. The study showed that a range of biologically relevant genes is not associated with known published pathways. They also tend not to be linked with known dominant mutations. Further, the researchers did draw gene network maps to point to biological areas which are generally understudied. Overall, the study concludes that there is little relationship between the relative biological importance of genes and the literature dedicated to specific genes, suggesting instead that most genes, after their initial discovery, attract limited attention, while other genes attract disproportionate attention due, at least in part, to social trends and the tendency of the scientific community to be a “small-world”. In the future, the data-driven analysis should help to pinpoint biological "terra incognita" which should be considered as challenges to tackle.
Functionally enigmatic genes in cancer: using TCGA data to map the limitations of annotations
Channing J. Paller
Added on: 12-22-2021
Mechanism of blood formation in leukaemia deciphered by single-molecule microscopy
2020
Universität Osnabrück, Osnabruck, Germany(1)
University of Helsinki, Helsinki, Finland(2)
University of York, York, United Kingdom(3)
University of Helsinki, Helsinki, Finland(2)
University of York, York, United Kingdom(3)
To date it has been unclear how individual mutations trigger signal activation at the molecular level and thus lead to serious diseases of the haematopoietic system, i.e. leukaemia.
Using single-molecule microscopy on living cells, the researchers have now been able to show, amongst other things, that the receptors are linked to form pairs by the messenger substances. Up to now it was assumed that the receptors were already present as inactive pairs even without messenger substances. However, from their new observations on high-resolution fluorescence microscopes, the researchers concluded that pair formation itself is the basic switch for activating signal transduction in the cell. Thus, direct microscopic visualisation of individual receptors under physiological conditions, simulations and molecular modelling in combination were able to clarify a controversy that has preoccupied this research area for more than 20 years.
Mechanism of homodimeric cytokine receptor activation and dysregulation by oncogenic mutations
Jacob Piehler(1), Ilpo Vattulainen(2), Ian S. Hitchcock(3)
Added on: 06-09-2020
A transcriptome-wide association study of tissue-dependent gene expression
2020
University of Bristol, Bristol, United Kingdom
Applying an approach called Mendelian randomization, researchers integrated the genomic dataset from the Genotype-Tissue Expression (GTEx) project, which contains transcription information for over 32,000 genes based on a range of 80 to 491 samples from 48 different human tissues, with the results from 395 genome-wide association studies. This integration constructed a genome-wide atlas of tissue-dependent regulatory mechanisms, providing increased understanding of the determinants of complex diseases.
A transcriptome-wide mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome
Tom G. Richardson
Tom G. Richardson et al. Nature Communications 2020 [292]
Physicians Committee for Responsible Medicine [293]
Added on: 05-26-2020
Computational models for prediction of ligand-transporter interaction
2020
University of Vienna, Vienna, Austria
Transporters expressed in the liver play a major role in drug pharmacokinetics and are a key component of the physiological bile flow. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury. Therefore, predicting the interaction profile of small molecules with transporters expressed in the liver may help to prioritize compounds in an early phase of the drug development process. Based on a comprehensive analysis of the data available in the public domain, the authors developed a set of classification models which allow predicting—for a small molecule—the inhibition of and transport by a set of liver transporters considered to be relevant by FDA, EMA, and the Japanese regulatory agency. The in silico models were validated by cross-validation and external test sets. Finally, models were implemented as an easy to use web-service which is freely available at https://livertox.univie.ac.at.
Vienna LiverTox Workspace—a set of machine learning models for prediction of interactions profiles of small molecules with transporters relevant for regulatory agencies
Melanie Grandits
Added on: 06-23-2021
Robotic fluidic coupling and monitoring of vascularized 2-channel organ chips
2020
Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, USA
Here, an "Interrogator" instrument was described that uses fluid robotics, a customised software package and an integrated mobile microscope to enable automated culture, perfusion, media addition, fluidic linkage, sample collection and in situ microscopic imaging of up to 10 organ chips in a standard tissue culture incubator. The automated interrogator platform was able to maintain the viability and organ-specific functions of 8 different vascularised 2-channel organ chips (intestine, liver, kidney, heart, lung, skin, blood-brain barrier (BBB) and brain) for 3 weeks in culture when fluidically coupled intermittently through their media reservoirs and endothelium-lined vascular channels using a common blood replacement medium. When an inulin tracer was perfused through the human body-on-chip (HuBoC) fluidic network with multiple organs, it was possible to accurately predict the quantitative distribution of this tracer using a physiologically based in silico model of the experimental system (Multi-Compartmental Reduced Order, MCRO). This automated culture platform allows non-invasive imaging of cells in human organ chips and repeated sampling from both the vascular and interstitial compartments without compromising fluidic coupling, which should facilitate future HuBoc studies and pharmacokinetic analyses in vitro.
Robotic fluidic coupling and interrogation of multiple vascularized organ chips
Donald E. Ingber
Added on: 11-09-2021
VISION - an in vitro- and in silico inhalation toxicology analysis platform
2020
Fraunhofer IBMT, Sulzbach, Germany(1)
Universität des Saarlandes, Homburg, Germany(2)
Universität des Saarlandes, Homburg, Germany(2)
The research project "VISION" aims to develop and validate an in-vitro/in-silico analysis platform for inhalation toxicology studies. The platform combines a microfluidic organ culture system, more precisely an in vitro lung-liver model, and bioinformatic analyses of disease mechanisms. The in vitro systems will be developed to simulate the lung barrier and the metabolism process in the liver and, with their integration into microfluidic systems, will be used to determine specific effects of pollutants or therapeutic agents after pulmonary uptake. Using these methods, well-founded data sets will be generated as a basis for the development of the in-silico model.
VISION – Ein mikrofluidisches Chipsystem als Alternative zu Tierversuchen
Heiko Zimmermann(1), Robert Bals(2)
Added on: 10-16-2020
Astrophysics and AI key to early dementia diagnosis
December 2019
Brighton and Sussex Medical School, Brighton, United Kingdom
A computer program, developed at Brighton and Sussex Medical School together with astrophysicists, helps in early diagnosis of dementia. Using patient data from general practitioners, 70 indicators related to the onset of dementia and recorded in the five years prior diagnosis were found. This machine learning model identified 70% of dementia cases before the physician.
Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine- learning and standard epidemiological approaches
Elizabeth Ford
Elizabeth Ford et al. BMC Medical Informatics and Decision Making 2019 [297]
Technology Networks [298]
Added on: 07-07-2020
Combined in-silico and in-vitro technique detects carcinogenesis caused by bacteria
October 2019
Max Planck Institute for Infection Biology, Berlin, Germany
Using a combination of human intestinal epithelial cell culture and computer modelling, it has been possible for the first time to prove that bacteria or their products can cause cancer.
E. coli toxin colibactin can bind to particularly narrow, AT-rich regions of human DNA, as a computer simulation showed, and cause mutations there. The resulting mutation occurs frequently in certain forms of colon cancer. Thus, a kind of genetic fingerprint could be identified, which bacteria leave behind in the DNA. This mutation signature can already be identified in still healthy cells and opens up promising avenues for further studies in the field of cancer prevention.
Colibactin DNA damage signature indicates causative role in colorectal cancer
Thomas F. Meyer
Added on: 06-24-2020
Using deep learning artificial intelligence to model cognitive function
October 2019
Brain Institute, Florida Atlantic University, Boca Raton, USA
By putting neural networks in robots, researchers are able to build models of complex cognitive functions such as perception, attention, and curiosity, experiment on them, and immediately get feedback directly from the neural network about decision-making behaviour. In this novel approach dubbed “robopsychology,” they have demonstrated the potential for synergistic research utilizing behavioural, computational, and neural techniques that could be crucial for advancing treatments and prevention strategies for mental conditions such as schizophrenia and Alzheimer´s disease.
Using deep learning artificial intelligence in robots to model cognitive function
William Hahn
Added on: 07-08-2020
Using deep learning to better understand blood disorders
October 2019
Helmholtz Zentrum München, München, Germany(1)
LMU Munich, Munich, Germany(2)
LMU Munich, Munich, Germany(2)
The authors created a data set containing 18,000 images of individual leukocytes taken from 100 patients diagnosed with acute myeloid leukaemia and from 100 control patients. The specimens were digitized and used to train a deep learning convolutional neural network for leukocyte classification. The network classifies the most important cell types with high accuracy and identifies pathologies with human-level performance. This approach holds the potential to be used as a classification aid for examining much larger numbers of cells in a smear than can usually be done by a human expert. This will allow clinicians to recognize malignant cell populations with lower prevalence at an earlier stage of the disease.
Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks
Carsten Marr(1), Karsten Spiekermann(2)
Christian Matek et al. Nature Machine Intelligence 2019 [303]
Cancer Imaging Archive [304]
Chemie.de [305]
Added on: 12-17-2020
Living Heart Project: A virtual heart for improved drug testing
2019
Universität Hohenheim, Stuttgart, Germany
Human iPSCs are differentiated into cardiomyocytes and the response of single cells and cell assemblies to a specific drug is investigated. In order to map cell behaviour, the cells are attached to the electrodes of a device that measures the propagation of action potentials (APs) or changes thereof in response to a pharmaceutical. The AP propagation through the heart muscle cells physiologically leads to heart contraction. Therefore by measuring the AP propagation, it can be tested whether certain drugs could lead to cardiac rhythm disturbances. The obtained data is fed to a mathematical computer model that can predict the possible side effects of new drugs before they are tested in volunteers. With this method, drug tests can be made faster, safer and cheaper.
Comparison of in vitro and computational experiments on the relation of inter-beat interval and duration of repolarization in a specific type of human induced pluripotent stem cell-derived cardiomyocytes
Georg Rast
Added on: 05-25-2020
Black phosphorus used for artificial intelligence
2019
RMIT University, Melbourne, Australia
Layered black phosphorus (BP), a promising 2D material, tends to oxidize under ambient conditions. New opportunities arise from intrinsic BP defects: It is the only material with the ability to distinguish between UV-A and UV-B radiation, thus having tremendous implications for skin health management. The same setup is utilized to show an optically stimulated mimicry of synaptic behaviour. It mimics action potentials, analogous to the neuronal activities triggered by optical stimuli in biological neurons. As such, few-layers BP renders mimicry of different synaptic functions in biological neurons and light-sensitive cells, such as retinal ganglion cells. This provides new possibilities in neuromorphic computing. Furthermore, it is shown that serially connected devices can perform digital logic operations using light. Thus, a BP-based electronic chip mimicking the human brain that uses light to create and modify memories could be created.
Multifunctional optoelectronics via harnessing defects in layered black phosphorus
Taimur Ahmed, Sumeet Walia
Added on: 07-09-2020
New combined method for imaging neuronal brain activity
2019
Max Planck Institute for Human Development, Berlin, Germany(1)
Princeton University, Princeton, USA(2)
Princeton University, Princeton, USA(2)
Participants' brains were studied by functional magnetic resonance imaging signals after they had learned a decision-making task. During a subsequent resting phase, the replay of activity patterns in the hippocampus reflected the order of previous task-state sequences at a higher speed. Thus, sequential hippocampal reactivation might participate in human decision-making. The results support the importance of sequential reactivation in the human hippocampus for nonspatial decision-making and establish the feasibility of investigating such rapid signals with fMRI (functional magnetic resonance imaging), despite substantial limitations in temporal resolution. To date, a non-invasive, human-based method to measure fast brain activities was lacking. By combining this MRI technique with an algorithm for the detection of activity pattern, a method was developed for studying human brain processes.
Sequential replay of nonspatial task states in the human hippocampus
Nicolas W. Schuck(1), Yael Niv(2)
Added on: 07-09-2020
3D model elucidates acetaminophen toxicity in hepatic cells
2019
Maastricht University, Maastricht, Netherlands
Inflammation may have a role in the acetaminophen-mediated toxicity of hepatic cells. Here, a 3D co-culture of human primary hepatocytes and Kupffer cells was treated with acetaminophen to evaluate its toxicity with or without lipopolysaccharide challenge. The results showed that acetaminophen can inhibit the expression of genes involved in metabolic homeostasis and anti-oxidant functions. Moreover, higher doses of acetaminophen hindered Fc fragment receptor genes even without lipopolysaccharide. Also, toll-like receptor 4 expression was elevated after double treatment, which could reduce Kupffer cells phagocytosis and disrupt cytokine expression. Furthermore, co-exposure to inflammatory challenge and acetaminophen led to a pro-inflammatory cytokine profile. Overall, the researchers validate a new 3D model for drug testing and elucidate inflammation-mediated drug toxicity mechanisms of acetaminophen.
Human 3D multicellular microtissues: An upgraded model for the in vitro mechanistic investigation of inflammation-associated drug toxicity
J Jiang
Added on: 11-28-2021
Computer prediction of antiproliferative activity of steroidal drugs
2019
University of Novi Sad, Novi Sad, Serbia
Computational analysis of the effectivity of antiproliferative drugs derived from 17α-picolyl and 17(E)-picolinylidene against human ER-breast adenocarcinoma cells is presented. With these tools, it is possible to rank the compounds based on their anticancer activity, lipophilicity, ADME profile and one can predict their antiproliferative activity. These results show that the methods presented in this study will allow for better selection, synthesis and rational design of new potential drugs.
Toward steroidal anticancer drugs: Non-parametric and 3D-QSAR modeling of 17-picolyl and 17-picolinylidene androstanes with antiproliferative activity on breast adenocarcinoma cells
Strahinja Z Kovačević
Strahinja Z Kovačević et al. Journal of Molecular Graphics and Modelling 2019 [313]
EURL ECVAM [314]
Added on: 07-27-2021
RETERO project: Electronic fish surrogates for hydropower facility risk assessment
2019
Otto-von-Guericke-Universität, Magdeburg, Germany
Hydropower facilities like water vortex power plants interrupt fish migration corridors. In order to examine the fish injury and mortality rates related to hydropower plants, live fish are commonly used. To reduce and replace these live fish, electronic fish surrogates are designed with pressure and inertial sensors to evaluate the thresholds of hydropower-induced damage. Combined with numerical simulations and a fish behaviour model, prediction of injury and mortality of fish when passing through turbines and hydraulic structures which are important data for regulatory compliance studies can be possible.
Roberto Leidhold
Added on: 03-10-2022
A computational model of epithelial solute and water transport along a human nephron
2019
Duke University, Durham, USA
In this study, the first computational model of solute and water transport from Bowman space to the papillary tip of the nephron of a human kidney was developed. The nephron is represented as a tubule lined by a layer of epithelial cells, with apical and basolateral transporters that vary according to cell type. The model is formulated for steady state, and consists of a large system of coupled ordinary differential equations and algebraic equations. Model solution describes luminal fluid flow, hydrostatic pressure, luminal fluid solute concentrations, cytosolic solute concentrations, epithelial membrane potential, and transcellular and paracellular fluxes. It was found that if the transporter density and permeabilities were assumed to be the same between human and rat nephrons (with the exception of a glucose transporter along the proximal tubule and the H+-pump along the collecting duct), the model yields segmental deliveries and urinary excretion of volume and key solutes that are consistent with human data. The model predicted that the human nephron exhibits glomerulotubular balance, such that proximal tubular Na+ reabsorption varies proportionally to the single-nephron glomerular filtration rate. To simulate the action of a novel diabetic treatment, the Na+-glucose cotransporter 2 (SGLT2) along the proximal convoluted tubule was inhibited. Simulation results predicted that the segment’s Na+ reabsorption decreased significantly, resulting in natriuresis and osmotic diuresis.
A computational model of epithelial solute and water transport along a human nephron
Anita T. Layton
Added on: 07-29-2024
Computational assessment of tumor heterogeneity
2019
Stanford University School of Medicine, Stanford, USA
The computational model presented in this study allows assessing tumor heterogeneity and clonal replacement throughout treatment. It was used to study five human breast cancer tumors treated with HER2-targeted therapy. This model is able to show that two of these tumors underwent clonal replacement and that the resistant subclones were present before the beginning of the treatment and their rate of resistance-related genomic changes. This model is a valuable new tool when trying to understand the development of treatment resistance.
Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy
Christina Curtis
Added on: 07-20-2021
Automated confocal high-throughput imaging for Organs-on-Chips
Company 2019
AstraZeneca IMED Biotech Unit, Cambridge, United Kingdom
The authors created an end-to-end, automated workflow to capture and analyse confocal images of multicellular Organ-Chips to assess detailed cellular phenotype across large batches of chips. The automation of this process not only reduced acquisition time but also minimised process variability and user bias. The authors established a framework of statistical best practices for Organ-Chip imaging in drug discovery and testing. The workflow was tested with benzbromarone, whose mechanism of toxicity has been linked to mitochondrial damage with subsequent induction of apoptosis and necrosis, and staurosporine, an inducer of apoptosis. The hepatotoxic effects of an active AstraZeneca drug candidate were also assessed, illustrating the method’s applicability in drug safety assessment beyond testing tool compounds. Finally, the authors demonstrated that this approach could be adapted to Organ-Chips of different shapes and sizes via an application of a Kidney-Chip.
Introducing an automated high content confocal imaging approach for Organs-on-Chips
Samantha Peel
Added on: 10-13-2020
Computational prediction of key processes in tumor progression
December 2018
Duke University School of Medicine / Duke Cancer Institute, Durham, USA(1)
Rice University, Houston, USA(2)
Rice University, Houston, USA(2)
In this study, the authors focus on trying to model the epithelial-mesenchymal transition and the formation of cancer stem cells in tumor progression by using a computational simulation. Using this method, they identify a signaling pathway that shows to be important in tumor organoid formation.
Toward understanding cancer stem cell heterogeneity in the tumor microenvironment
Gayathri R. Devi(1), José Nelson Onuchic(2), Herbert Levine(2), Mohit Kumar Jolly(2)
Added on: 07-03-2021
Machine learning approach for breast cancer diagnostics
December 2018
University of Pennsylvania, Philadelphia, USA
A machine learning approach coupled with multimodal ultrasound images is applied to improve breast cancer diagnosis. Several properties obtained from the quantitative evaluation of proven solid breast lesions were used to select statistically significant features that can be valuable for diagnostics. This method not only allows to achieve high efficacy for breast cancer diagnosis, but can also identify the weakly learned cases that can disturb the correct diagnosis.
Machine learning to improve breast cancer diagnosis by multimodal ultrasound
Chandra M Sehgal
Added on: 07-30-2021
Nextstrain: real-time tracking of pathogen evolution
December 2018
Fred Hutchinson Cancer Research Center, Seattle, USA
Nextstrain is an open-source project to harness the scientific and public health potential of pathogen genome data. It provides a continually-updated view of publicly available data for certain important pathogens such as SARS-CoV-2, influenza, Ebola, and Zika viruses, with powerful analytics and visualizations showing pathogen evolution and epidemic spread. Nextrstrain’s goal is to aid epidemiological understanding and improve outbreak response.
Nextstrain: real-time tracking of pathogen evolution
James Hadfield
Added on: 11-06-2020
The Virtual Cancer Patient
December 2018
Technische Universität Darmstadt, Darmstadt, Germany
The project "The Virtual Cancer Patient" takes into account the problem that every individual cancer patient has a unique disease due to individual genetic changes. The aim of the project is to create a network from human gene and protein data, which can use algorithms to assess in advance whether a particular therapy can help individual patients.
Der virtuelle Krebspatient
Heinz Koeppl
Added on: 04-30-2020
Artifical intelligence tools to improve immunotherapy response
November 2018
University of Oxford, Oxford, United Kingdom
The study provides an outline to machine learning (ML) and artificial intelligence tools allowing analysis of complex morphological phenotypes together with multiomics datasets. Computational tools will help to derive complete, standardized, and reproducible datasets to facilitate the individualized prediction of immunotherapy response. Recognition of computational pathology by professional medical societies will be essential to meet future clinical demands for optimal patient care.
Precision immunoprofiling by image analysis and artificial intelligence
Viktor H. Koelzer
Added on: 07-06-2021
In silico and in vitro assessment of tumor mutational burden
November 2018
University Hospital Heidelberg, Heidelberg, Germany
Assessment of Tumor Mutational Burden (TMB) is essential for response stratification of cancer patients treated with immune checkpoint inhibitors. TMB approximates the number of neoantigens that potentially are recognized by the immune system. As far as now, TMB assessment has been done by whole-exome sequencing which implementation in diagnostics is hampered by tissue availability as well as time and cost constraints. In the present study, the researchers have used in silico and sequencing analysis of commercially available panels of genes in cancer patients tissues. The data suggest that TMB approximation using gene panel sequencing of tumor tissue is feasible and can be implemented in routine diagnostics.
Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels
Albrecht Stenzinger
Added on: 07-27-2021
Heart-on-a-chip technology to combat heart disease
CompanyOctober 2018
Insilico Medicine, Rockville, USA(1)
TARA Biosystems Inc., Hong Kong, China(2)
TARA Biosystems Inc., Hong Kong, China(2)
The biotechnological companies Insilico Medicine and TARA Biosystems are working together using artificial intelligence and heart-on-a-chip technology to speed up drug discovery and lower drug cost in the fight against heart disease. Pairing artificial intelligence and heart-on-a-chip technology allows researchers to predict the therapeutic use of hundreds of
new drugs and test them on actual human heart cells before they enter clinical trials.
Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry
Alex Zhavoronkov(1), Misti Ushio(2)
Added on: 05-25-2020
Statistical model to predict intracranial aneurysm rupture
October 2018
George Mason University, Fairfax, USA
Intracranial aneurysms [IAs] are nowadays increasingly diagnosed incidentally and physicians need to weigh the natural risk of aneurysm rupture against the risks of treatment and their complications when deciding on a treatment strategy. In the present study, the researchers aimed at validating a previously developed statistical model with 249 aneurysms from patient cohorts. The researchers used imaging data and patient information to perform patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The statistical model's capacity to predict outcomes was compared to the standard methodology. The model showed good performance and demonstrated its potential of use for clinical risk assessment.
External validation of cerebral aneurysm rupture probability model with data from two patient cohorts
Felicitas J. Detmer
Added on: 11-27-2021
A mathematical model to predict immune system response to CAR-T cell therapy
2018
Friends Select School, Philadelphia, USA(1)
Villanova University, Villanova, USA(2)
Villanova University, Villanova, USA(2)
CAR-T cell therapy is a novel therapy which can be used to treat blood cancers. However, CAR-T cells can cause a life-threatening side-effect called Cytokine Release Syndrome (CRS). The study uses a mathematical simulation to quantify the dynamics of 9 major cytokines for CAR-T cell therapy. The simulation results from this work can be used to generate hypotheses to optimize cytokine inhibition approaches in future experimental research to improve response to CAR-T cell therapy.
A model-based investigation of cytokine storm for T-cell therapy
Yiming Pan(1), Nan Fang(1), Brooks Hopkins(2), Zuyi Hang(2), Matthew Tucker(2)
Added on: 07-06-2021
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
2018
Applied Bioinformatics Laboratories, New York University School of Medicine, New York, USA(1)
Department of Population Health and the Center for Healthcare Innovation and Delivery Science, New York, USA(2)
Department of Population Health and the Center for Healthcare Innovation and Delivery Science, New York, USA(2)
A deep convolutional neural network (inception v3) was trained on more than 1.600 whole-slide images obtained from The Cancer Genome Atlas to automatically classify them into adenocarcinoma, squamous cell carcinoma or normal lung tissue with 97% accuracy. Furthermore, the network can predict the ten most commonly mutated genes in adenocarcinoma with an accuracy of 73 to 86%. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. This approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Aristotelis Tsirigos (1), Narges Razavian(2)
Added on: 04-21-2020
In silico model of calcium dynamics in cardiomyocytes explains aternans formation
2018
University of Oxford, Oxford, United Kingdom
Repolarization alternans, the alternation of long and short action potential durations (APD), has been linked to the incidence of ventricular fibrillation and sudden cardiac death. Multiple heart diseases are associated with an increased vulnerability to alternans. It is known that cardiac remodelling in heart failure and other diseases alters both intracellular calcium release and reuptake within cardiomyocytes. Reduced reuptake capacity has been linked to alternans vulnerability. In the present study, the researchers aimed at characterizing how altered properties of the sarcoplasmic reticulum (SR) calcium release modulate alternans vulnerability. The researchers adapted Heijman–Rudy computer models of ventricular myocyte to obtain precise control over SR release dynamics and magnitude, allowing for the evaluation of these properties in alternans formation and suppression. The data shows that sufficiently increased calcium release may surprisingly prevent alternans via a mechanism linked to the functional depletion of junctional SR during release. The model also allowed to provide a detailed explanation of alternans formation. In conclusion, the study shows how altered dynamics and magnitude of SR calcium release modulate alternans vulnerability which could be exploited to develop strategies to reduce arrhythmia occurrence.
Modulation of cardiac alternans by altered sarcoplasmic reticulum calcium release: a simulation study
Jakub Tomek
Added on: 12-02-2021
RASAR - accurate in silico toxicology assessment
2018
Johns Hopkins University, Baltimore, USA
The study describes a read-across structure-activity relationships (RASAR) in silico method capable of analyzing large chemical databases. Based on structural similarities and known properties of chemical substances, RASAR groups multiple chemicals together and predicts putative toxic effects with an accuracy of 80-95%, which is significantly higher than the standard toxicological animal experiments.
Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility
Thomas Hartung
Added on: 09-01-2020
In silico model to identify specific cancer epitopes
2018
Broad Institute of MIT and Harvard, Cambridge, USA
The study presents an in silico approach build on cancer patients omics to identifying specific epitopes only expressed in cancer cells. This approach should be considered for prospective personalized cancer vaccine development.
Intron retention is a source of neoepitopes in cancer
Eliezer M. Van Allen
Added on: 07-06-2021
Bioinformatics to design breast cancer therapy
2018
Tabriz University of Medical Sciences, Tabriz, Iran
Triple-negative breast cancer (TNBC) is an important subtype of breast cancer. TNBC may be a cancer testis antigen (CTA)-positive tumor, opening the door for therapy targeting the CTA. In the present study, the researchers used an immunoinformatics approach to design a peptide vaccine to combat TNBC using three peptides in order to stimulate the humoral, cellular and innate immune responses. The vaccine structure was also subjected to the molecular dynamics simulation study for structure refinement. The informatics results verified the immunogenicity and safety profile of the constructed vaccine as well as its capability for stimulating both the cellular and humoral immune responses. The proposed vaccine may be considered for the immunotherapy of TNBC.
In silico design of a triple-negative breast cancer vaccine by targeting cancer testis antigens
Yadollah Omidi
Added on: 07-27-2021
In silico 3D model of solid tumors to screen for optimal immunotherapy
2018
University Medical Center Heidelberg, Heidelberg, Germany
Most solid tumors in cancer are resistant to immunotherapy because of the immunosuppressive effect of the tumor microenvironment. In the present study, the researchers developed an in silico 3D model of human solid tumor tissue that can comprise over a million cells, including the different categories of cells usually present in solid tumors, and over clinically relevant timeframes. This model could be informed by individual patient data to generate individual in silico tumor explants. The stratification of growth kinetics of these explants could reasonably predict survival in a cohort of patients. Further, the model was used to simulate the effect of chemotherapy, immunotherapies, and cell migration inhibitors alone and in combination to try to find optimal treatment strategies. This platform can complement other patient-specific ex vivo models and can be used for high-throughput screening of combinatorial immunotherapies.
High-throughput screening of combinatorial immunotherapies with patient-specific in silico models of metastatic colorectal cancer
Jakob Nikolas Kather, Niels Halama
Added on: 09-13-2021
Label-free and real time monitoring of cell viability in 3D tumour spheroids
2018
The University of Edinburgh, Edinburgh, United Kingdom
Here an electrical impedance tomography is used to measure cell viability in a 3D model of tumour spheroids that allows them to do label-free monitoring of drug testing. They demonstrate both "in vivo" and "in silico" that their method works. With this method they could measure in real time the loss of cell viability in a 3D model of breast cancer.
Electrical impedance tomography for real-time and label-free cellular viability assays of 3D tumour spheroids
Jiabin Jia
Added on: 07-02-2021
Mathematical analysis of blood flow dynamics in patients to predict aneurysm
2018
Southern Medical University, Guangzhou, China
The anterior communicating artery (AcomA) is accounting for > 25% of all intracranial aneurysm populations. Hemodynamics are essential to understand AcomA aneurysm formation: wall shear stress (WSS) is the frictional force of viscous blood on the endothelial surface which is partly responsible for aneurysm formation. In the present study, the researchers aimed at identifying which hemodynamic parameters can characterize AcomA aneurysm formation. The researchers studied imaging data and haemodynamics parameters of AcomA from 81 patients and 118 controls during a period of three years. A mathematical analysis allowed for the identification of parameters of WSS which could predict AcomA aneurysm formation. The study describes a method that could be used as a screening tool for evaluating the probability of aneurysm formation.
The role of wall shear stress in the parent artery as an independent variable in the formation status of anterior communicating artery aneurysms
Chuan-Zhi Duan
Added on: 11-27-2021
Computerized testing of breast tissue characterization imaging techniques
2018
Federal University of Technology - Paraná, Curitiba, Brazil
A combination of x-ray fluorescence and scattering spectroscopy is applied to characterise breast tissues. These techniques were tested with a Monte Carlo computational study. The results show that this combination is able to produce images to map breast tissue samples and they can be complementary for breast tissue characterisation.
Characterization of breast tissues combining x-ray fluorescence and scattering spectroscopy: a Monte Carlo computational study
Marcelo Antoniassi
Added on: 07-30-2021
Deep brain stimulation improves speech performance in a Parkinson context
2018
RWTH Aachen University, Aachen, Germany
Parkinson's disease is a neurodegenerative disorder that leads to motor deficits, including speech. Although currently there is no effective treatment to stop the disease, deep brain stimulation in the subthalamic nucleus and globus pallidus internus has been described as an effective therapy. Here, a neural model is developed to evaluate the effects in speech performance of different dopamine levels in the striatum and activity levels in the subthalamic nucleus and the globus pallidus internus through syllable repetition task simulation. The results show that a decrease of dopamine levels in the striatum leads to different degrees of syllable sequencing errors, as seen in Parkinson's disease, which could be counteracted by a reduction in the activity in the subthalamic nucleus or the globus pallidus internus. The model developed in this study relates the reduction in syllable sequencing errors to the inhibition of the subthalamic nucleus and globus pallidus internus, which may bring new insights into the mechanisms by which deep brain stimulation improves speech performance in Parkinson's patients.
Inhibiting basal ganglia regions reduces syllable sequencing errors in Parkinson's disease: a computer simulation study
Bernd J Kröger
Added on: 09-24-2021
Depression impairs new as well as old memories
2018
Ruhr University Bochum, Bochum, Germany
Researchers created a computational model to simulate the brains of patients suffering from depression and showed that depressive episodes can impair both recent memory storage and retrieval by limiting the formation of new brain cells. In addition, it also showed that depressive episodes can erase past stored memories.
The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory
Sen Cheng
Added on: 05-25-2020
In silico model of anti-cancer target to improve drug design
2018
Zhengzhou University, Zhengzhou, China
Human PD-1 (hPD-1) is a transmembrane immunoglobulin that interacts with its ligands PD-L1 to prevent excessive T cell activation and maintain self-tissue tolerance. Cancer treatment by modulating the PD-1/PD-L1 axis has been highly promoted since PD-L1 was reported to be over-expressed in a wide variety of solid tumors which evade immune surveillance. In the present study, the researchers sought to better understand the functionality of the PD-1 molecule and its ligand, PD-L1, using detailed 3D structures and their interactions using in silico molecular dynamics simulations. Based on predictions, the researchers were able to design ligands with improved binding capacity and confirmed the results in vitro. This in silico model should be used as a tool to facilitate rational drug design of molecules that can modulate PD-1’s pathways.
The design of high affinity human PD-1 mutants by using molecular dynamics simulations (MD)
Yanfeng Gao
Added on: 09-15-2021
Machine learning helps predict schizophrenia treatment outcomes
2018
Chinese Academy of Sciences, Beijing, China
A machine learning algorithm is used to examine fMRI images of both newly diagnosed, previously untreated schizophrenia patients and healthy subjects. By measuring the connections of a brain region called the superior temporal cortex to other regions of the brain, the algorithm successfully identified patients with schizophrenia at 78 per cent accuracy. It also predicted with 82 per cent accuracy whether or not a patient would respond positively to a specific antipsychotic treatment named risperidone. The researchers hope to expand the work to include other mental illness such as major depressive and bipolar disorders.
Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity
Xiang Yang Zhang
Added on: 07-06-2020
Machine learning method for breast cancer diagnostics
2018
Radboud University Medical Center, Nijmegen, Netherlands
This method is based on machine learning techniques to gain insights into stromal changes associated with breast cancer. Using deep convolutional neural networks, the algorithm was trained to discriminate between invasive breast cancer-related stroma from benign biopsies from patients. Afterwards, the algorithm could perform correctly and even could be used to detect ductal carcinoma. These results show that algorithms can be powerful tools to classify breast biopsies and understand breast insults.
Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies
Jeroen A W M van der Laak
Added on: 07-22-2021
Mathematical modelling of one pillar of the immune system to tailor therapies
2018
University of California, Riverside, USA
A pivotal arm of innate immunity, the complement system is comprised of proteins present both in the plasma and cell membranes that mediate immune responses against invading pathogens and altered host cells. Although many complement regulators are present to protect host cells under homeostasis, the impairment of the Factor H (FH) regulatory mechanism has been associated with several autoimmune and inflammatory diseases. In the present study, the researchers aimed at developing a comprehensive computational model of the alternative and classical pathways of the complement system. The model was able to recapitulate a normal state, a state with FH impairment and two states representing FH impairment with two different treatments. The study shows time profiles for biomarkers associated with alternative pathway FH disorders, consistent with clinically observed data. These results allow visualizing how patient-tailored therapies are needed depending on the specific FH-mediated disease and the manifestations of a patient’s genetic profile in complement regulatory function.
A computational model for the evaluation of complement system regulation under homeostasis, disease, and drug intervention
Dimitrios Morikis
Added on: 10-27-2021
Bioreactor model for drug efficacy studies
2018
University of Florida, Orlando, USA
Nowadays, HER2+ resistant breast cancer is a major clinical challenge. Several studies have shown some of the pathways that are responsible for the acquisition of resistance of this type of tumors, which can be targeted with different already available drugs. Here, a classic 2D and a novel bioreactor-based 3D models are developed to study a triple combination therapy that targets the known pathways responsible for chemotherapy resistance in human breast cancer cells. The results show that treatment with paclitaxel, everolimus and dasatinib induced cell apoptosis in 2D conditions and in the bioreactor 3D dynamic model. With these results, a pharmacokinetics/dynamics mathematical model was generated to correlate the exposure to the novel combinational therapy and its anti-cancer effects. In this study, the researchers demonstrate the utility of a bioreactor-based 3D dynamic model to perform drug screening in an in vitro set-up that replicates in vivo conditions and demonstrate the efficacy of a novel combination therapy that can overcome HER2+ induced drug resistance.
Utility of a novel three-dimensional and dynamic (3DD) cell culture system for PK/PD studies: evaluation of a triple combination therapy at overcoming anti-HER2 treatment resistance in breast cancer
Sihem Ait-Oudhia
Added on: 10-09-2021
Comparison of invasive breast cancer prediction models
2018
University of Cambridge, Cambridge, United Kingdom
The purpose of this study is to compare two widely used invasive breast cancer prediction models: PREDICT and CancerMath, together with respective improvements, adding a new algorithm in the predictors. The results show that PREDICT performs better and that it is already a robust prediction model in which the addition of new predictor algorithms does not significantly improve its efficacy. In conclusion, PREDICT offers better clinical utility and is already robust enough.
Development and external validation of prediction models for 10-year survival of invasive breast cancer. Comparison with PREDICT and CancerMath
Solon Karapanagiotis
Added on: 07-29-2021
In silico prediction of lung cancer antigen 3D structure to facilitate treatment design
2018
Armed Forces College of Medicine, Cairo, Egypt
XAGE-1b is an overexpressed surface antigen in lung adenocarcinoma and was shown to be strongly immunogenic. The quest for designing immunotherapies as peptide vaccines based on XAGE-1b has been challenged by the lack of detailed structural information regarding its immunogenic properties. In this study, the researchers used a homology modelling technique and performed computer-based 3-dimensional structure models of XAGE-1b. The obtained 3D structure could explain its antigenic function and facilitate the usage of predicted peptides for experimental validation towards designing immunotherapies against lung adenocarcinoma.
Computational prediction of vaccine potential epitopes and 3-dimensional structure of XAGE-1b for non-small cell lung cancer immunotherapy
Mohammad M. Tarek
Added on: 07-27-2021
Mathematical model of amyloid beta aggreagtion
2018
Southwest Research Institute, San Antonio, USA
Aggregation of amyloid-beta peptides into oligomers and insoluble fibrils is thought to be associated with the development of Alzheimer's disease. In this study, the researchers propose a discrete mathematical model for the aggregation of amyloid-beta into toxic oligomers using chemical kinetics and population dynamics. With this model, they were able to establish the conditions to make the system equilibrium unstable and to find a formula to prevent the aggregation of amyloid-beta peptides. This model can be a powerful tool for drug designers to target amyloid-beta aggregation in neurodegeneration.
A discrete mathematical model for the aggregation of β-Amyloid
Maher A Dayeh
Added on: 08-05-2021
Theoretical model of osciallations in Parkinson's disease
2018
Chinese Academy of Sciences, Shanghai, China
Parkinson's disease is one of the most prevalent neurodegenerative disorders that commonly affects aged individuals. It is characterized by a progressive loss of dopaminergic neurons without a known trigger that reduces the dopamine levels in the striatum. However, associated phenomena have been observed in patients, like changes in oscillatory activities in the basal ganglia. Some nuclei interactions have been proposed to explain this abnormal activity, but the responsible mechanisms are unclear. Here, a model of the corticothalamic-basal ganglia mean firing rate was developed to investigate the causative mechanisms of these symptoms. The results show that changes in the properties of different nuclei can induce Parkinson's disease oscillations and different frequency bands can be observed. Additionally, the mechanisms behind these oscillations are well explained by the model and the numerical simulation results. Overall, the researchers provide new insights on the potential mechanisms that influence Parkinson's oscillations using a newly developed model that may be further used as a unifying framework to study defects in oscillations in Parkinson's disease.
The oscillatory boundary conditions of different frequency bands in Parkinson’s disease
Luonan Chen, Bing Hu
Added on: 09-24-2021
In silico screening of multi-target inhibitors for Alzheimer's disease
2018
University of Leuven, Leuven, Belgium
Alzheimer's disease is one of the most prevalent neurodegenerative diseases. One of its consequences is the dysregulation of cholinergic activity, which leads to cognitive decline. Inhibition of acetylcholinesterase is a largely used therapeutic strategy to prevent the loss of cholinergic function in Alzheimer's disease, but nowadays the field is turning to multitarget-directed ligands to affect several facets of the disease. Here, a bioinformatic approach is used to perform an "in silico" screening of potential therapeutic molecules that match these multi-target characteristics. Through a complex workflow of different discrimination steps, the researchers found four multi-target inhibitors with interesting protein-ligand stability, calculated with a new approach presented in the study. This method allows for an efficient drug screening strategy that identifies multi-target inhibitors with good ADMET profiles that have promising affinities against acetylcholinesterase and other potential hits that can be further tested in vitro for their potential clinical use.
In silico structure-based identification of novel acetylcholinesterase inhibitors against Alzheimer's disease
Muhammad Usman Mirza
Added on: 08-15-2021
Large-scale machine learning cancer genome analysis for therapeutic target identification
2018
Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
The authors describe and evaluate a combination of transcriptomics and machine-learning approaches to classifying aberrant pathway activity in tumors.This may aid identifying patients who will respond well to a certain anticancer therapy. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Potential biomarkers for choosing cancer treatment are identified.
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Casey S. Greene
Added on: 04-21-2020
Mathematical model of pathological calcium signalling in neuronal death
2018
Nanyang Technological University, Singapore, Singapore
Amyloid-beta accumulation leads to disturbed calcium signalling in neurons in Alzheimer's disease, leading to apoptosis. Here, a mathematical model is presented that combines models of amyloid deposition, calcium signalling and mitochondrial permeability transition pore-related cell apoptosis in Alzheimer's disease. Results show that without amyloid deposition, calcium levels remain at resting concentration, while in the simulated pathological situation there is an intracellular dysregulation of calcium ions and channels. This mathematical model allows the study of potential calcium-related pathological mechanisms in Alzheimer's disease that are not easily accessible experimentally.
Composite mathematical modeling of calcium signaling behind neuronal cell death in Alzheimer’s disease
Jie Zheng
Added on: 08-05-2021
Mutation load estimation model as a predictor of the response to cancer immunotherapy
2018
National Yang-Ming University, Taipei, Taiwan
Although the efficacy of immunotherapy has been demonstrated, treatment response differs from patient to patient. One tool to predict an individual patient's responses and improve therapeutic efficiency is the identification of patient's specific point mutations also called the mutation load. As far as now, techniques to identify mutation load have been too expensive and time-consuming to be used in the clinic. In the present study, the researchers have used publically available cancer genomics data to generate mathematical predictive models of mutation load for lung adenocarcinoma based only on 24 genes instead of whole-exome sequencing. The same model can be adapted to predict mutation load for melanoma and colorectal cancer. The estimated mutation load can be used to predict the clinical outcome of cancer immunotherapy with high accuracy. Using this estimation model should reduce the cost and time needed for the assessment of the mutation load and facilitate the obtention of cancer immunotherapy response prediction in the standard clinical setting.
Mutation load estimation model as a predictor of the response to cancer immunotherapy
Yi-Chen Yeh, Yu-Chao Wang
Added on: 07-26-2021
Nanostructured TiN-coated electrodes for characterization of in vitro models
2018
University Hospital Wuerzburg, Wuerzburg, Germany
Due to the increasing use of in vitro models, the precise evaluation of tissue-specific parameters of such in vitro test systems has become a crucial factor in ensuring predictable results. Impedance spectroscopy, as a non-invasive method, serves as a reliable and efficient tool for quality control, as it minimally interferes with the system during testing. In this study, a refined impedance measurement system using nanostructured titanium nitride (TiN) electrodes is presented. This advanced material was used to study tissue maturation and barrier integrity changes in an in vitro intestinal model. Reduction of the interfering signal allowed for more detailed data extraction and biological interpretation. As a result, transepithelial electrical resistance values could be determined from Caco-2 cells in vitro tissue models without further mathematical analysis based on computer simulation. The novel design of a 3D-printed measurement attachment equipped with nanostructured TiN electrodes was used to continuously monitor the barrier integrity of Caco-2 cells during a permeability assay. In summary, a novel method for improving electrode properties for impedance spectroscopy that was developed here can be easily integrated into standardized endpoint measurements for qualifying a variety of in vitro test systems.
Nanostructured TiN-coated electrodes for high-sensitivity noninvasive characterization of in vitro tissue models
Jan Hansmann
Added on: 07-07-2022
Computational study of inflammatory pathways in neurodegeneration
2018
University of Luxembourg, Luxembourg, Luxembourg
An analysis of post-mortem brains affected by Alzheimer's disease has shown a clear involvement of inflammation in the pathology of the disease. Here, a new computational approach is used to study six neuronal populations from Alzheimer's patients. The goal of this method is to identify signalling pathways that might be activated or inhibited during the pathology progress and which result in neurotoxicity. With this method, the researchers were able to describe several altered inflammatory mediators, with a considerable amount of region-specificity among them. In summary, this computational method could identify inflammatory pathways that may have a key role in the development of Alzheimer's disease and can be a powerful tool to translate omics data into therapeutical approaches.
Integrative computational network analysis reveals site-specific mediators of inflammation in Alzheimer's disease
Antonio Del Sol
Added on: 08-06-2021
Computational tool to design a handmade pulmonary valve to treat congenital heart disease
2018
National Cheng Kung University Hospital, Tainan, Taiwan(1)
National Chin-Yi University of Technology, Taichung, Taiwan(2)
National Chin-Yi University of Technology, Taichung, Taiwan(2)
Patients with congenital heart disease may be treated with percutaneous pulmonary valve implantation to treat narrowed or leaky pulmonary valves. However, commercial options are not always available for children or special subjects. Over the years, the handmade pulmonary valved conduit has provided a strategy to customize the size to the specifications of the patient. In the present study, the researchers aimed at improving the design by generating a meta-learning-based intelligent model to train the physician to determine optimal parameters for customized valve reconstruction. This model can overcome problems arising from empirical parameter determination. The customized handmade pulmonary valved conduit produced thanks to the model were validated in vitro by assessing the regurgitation fraction and the heart pump efficiency using a circulation loop system.
Customized handmade pulmonary valved conduit reconstruction for children and adult patients using meta-learning based intelligent model
Chung-Dann Kan(1), Chia-Hung Lin(2)
Added on: 12-03-2021
Mathematical and experimental approaches to improve electrochemotherapy treatments
2018
University of Padova, Padua, Italy
Through mathematical modelling of different needle spacing, it is shown that grid electrodes can be improved for electrochemotherapy to improve coverage of the treatment area. After finding the optimal distance and voltage, the researchers confirmed in a model of human breast cancer that these parameters allow for more homogeneous electroporation. Finally, it is confirmed that computational models and experimental procedures can be used to adjust the grid electrode's configuration.
Effect of electrode distance in grid electrode: numerical models and in vitro tests
Elisabetta Sieni
Added on: 07-12-2021
Dosing of cancer immunotherapy predicted using mathematical modelling
Company 2018
Roche Innovation Center, Basel, Switzerland
The success of immunocytokine-based cancer immunotherapy depends on achieving optimal concentrations of the drugs within the tumor microenvironment. The intratumoral immunocytokine concentration is a complex product of administered drug dose, treatment schedule, and anatomic/ spatial factors. In this study, the researchers utilized sequential pharmacokinetic and imaging data from patients treated with a novel tumor-targeted immunocytokine to generate a mathematical model. The model was able to predict antibody tumor uptake in patients after repeated administrations and identify an optimal dosing regimen.
Prediction of the optimal dosing regimen using a mathematical model of tumor uptake for immunocytokine-based cancer immunotherapy
Benjamin Ribba
Added on: 07-26-2021
In silico analysis of colorectal cancer patients genetic variations
2018
German Cancer Research Center (DKFZ), Heidelberg, Germany
Colorectal cancer (CRC) is the third most common cancer and the fourth leading cause of cancer mortality worldwide. Aberrant expression of genes NLRC5 and PD-L1 have been reported in CRC. In the present study, the researchers aimed at selecting potential regulatory variants in the NLRC5 and PD-L1 genes by using several online in silico tools and investigating their influence on CRC risk in a cohort of 1424 patients. The data suggests that not only a single genetic variant but also an interaction between two or more variants within genes involved in immune regulation may play important roles in the onset of CRC, providing therefore novel biological information, which could eventually improve CRC risk management but also immunotherapy in CRC.
Investigation of single and synergic effects of NLRC5 and PD-L1 variants on the risk of colorectal cancer
Calogerina Catalano
Added on: 09-18-2021
Identification of metabolic biomarkers of Alzheimer's disease
2018
National Institutes of Health (NIH), Baltimore, USA
Alzheimer disease is a multifactorial neurodegenerative disease that leads to cognitive decline and death. Despite the accumulating knowledge, the relationships between metabolic perturbations and Alzheimer's pathogenesis are poorly understood. Thus, new insights into the global perturbations in metabolism caused by or that lead to Alzheimer's disease are critical to develop better therapeutic strategies. Here, a parallel metabolic analysis of brain and blood samples from different cohort studies were performed to identify alterations that correlate the development of the pathology to the prodromal and preclinical measures of Alzheimer's progression. With machine learning, it was possible to elucidate 26 metabolites in brain samples that effectively discriminated between healthy and diseased patients. The same metabolites were analysed in blood samples to correlate them with various tests performed during the cohort studies and were found to be consistently associated with Alzheimer's severity at autopsy and Alzheimer's progression. The biological pathways to which the metabolites were related included several already known pathways relevant to Alzheimer's disease. In this study, the researchers identify a group of metabolites from blood and brain samples as potential biomarkers of Alzheimer's severity and progression that could also open the door to new therapeutical targets to tackle metabolism perturbations in the disease.
Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study
Madhav Thambisetty
Added on: 10-01-2021
Mathematical model to predict patients' response to treatment of hyperthyroidism
2018
University of Wisconsin -Whitewater, Whitewater, USA
Anti-thyroid stimulating receptor antibodies (TRAb) sometimes overproduced by the immune system can continuously stimulate the thyroid gland and make it overactive (hyperthyroidism). This autoimmune problem is called Graves’ disease and is currently treated using a compound called methimazole (MMI). In the present study, the researchers aimed at developing a mathematical model for hyperthyroidism treatment with MMI. The model could simulate the time-course of patients’ progression from hyperthyroidism to the normal condition and the obtained predictions were validated with patients data. The model allows to predict patient's treatment reaction and thus, tune the treatment accordingly.
A patient-specific treatment model for Graves’ hyperthyroidism
Balamurugan Pandiyan
Added on: 10-28-2021
Model of the brain network
2018
Charité – Universitätsmedizin Berlin, Berlin, Germany
On a platform called "The Virtual Brain", individual patient brain measurements are evaluated and personalized models are created that simulate the characteristics of the patient's brain activity. The tool allows researchers to evaluate the neural interactions involved and enables the patient's data to be used to predict neural network interactions. Unlike other computer models, this mathematical platform can process real-time human data to make better predictions about brain processes that are relevant to each individual patient and thus enables personalized medicines. In this way, individual differences in brain functions and underlying mechanisms of brain diseases can be uncovered.
Inferring multi-scale neural mechanisms with brain network modelling
Petra Ritter
Added on: 05-25-2020
Quantitative method to characterize cell morphology
December 2017
The Catholic University of America, Washington, USA
In this study, human breast cancer cells were cultured in different substrates to classify them depending on their morphology. Digital holographic microscopy coupled with epifluorescence microscopy were used to relate cell phase parameters to actin features. A machine learning method was used to classify the morphologies of cancer cells. The results showed that this method has high accuracy in classifying cell morphologies, which makes it a useful method to monitor cancer cell morphology features.
Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning
Christopher B Raub
Added on: 08-01-2021
Breast cancer profiling with transcriptomic analysis
November 2017
University of Granada, Granada, Spain
A combination of heterogeneous transcriptomics datasets is used to generate a new model to profile human breast cancer to increase the robustness of the results. This method allowed for the identification of 98 potential biomarkers that, after a classification and selection process, were reduced to 6 gene markers for breast cancer diagnosis. In summary, this study presents a new tool to classify and diagnose human breast cancer.
Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling
Daniel Castillo
Added on: 07-30-2021
A mathematical model of different multiple sclerosis variants
October 2017
University of Barcelona, Barcelona, Spain
The authors developed a mathematical model to simulate the biological processes involved in the progression of multiple sclerosis (MS) by evaluating patient data from 20 years. The results of this model were supported by pathological findings and suggested a common pathogenesis for the different MS subtypes, leading to a variety of clinical outcomes in different patient groups. This model is more meaningful to humans than animal experiments because it can predict different disease courses in patients.
Dynamics and heterogeneity of brain damage in multiple sclerosis
Pablo Villoslada
Added on: 05-25-2020
Amyloid-beta oligomerization dynamics
October 2017
University of California, Davis, USA
The amyloid-beta peptide in its oligomeric form is a major pathogenic element in the development of Alzheimer's disease. In this study, the researchers aim to quantify the binding of pyrroline-nitroxyl fluorene, an amyloid-beta toxicity blocker, and its effect on the peptide. The results show that the binding affinity depends on the oligomeric state of amyloid-beta, being easier to be bound when it is in its monomer or dimer form compared to its oligomeric forms. To further understand the dynamics of these interactions, a molecular dynamics simulation is used together with molecular docking to define conformational states that correlate to lower toxicity and aggregation propensity of amyloid-beta. This study brings new mechanistic insights of pharmaceutical relevance and develops a methodology to increase the "in vivo" relevance of the results, providing a platform to investigate the potential modulation of peptide aggregation as a therapeutic target in other disorders.
Oligomerization alters binding affinity between amyloid beta and a modulator of peptide aggregation
John C Voss
Added on: 08-10-2021
Computational model of amyloid-beta aggregation
October 2017
Rice University, Houston, USA(1)
University of Miami, Coral Gables, USA(2)
University of Miami, Coral Gables, USA(2)
Currently, the onset of Alzheimer's disease is thought to be linked to the transition of amyloid-beta from soluble peptides to aggregated fibrils. Therefore, inhibiting amyloid-beta aggregation has been a long-pursued objective. But how amyloid-beta aggregates and how different molecules bind to it is still not known. Here, a rhenium complex that binds to amyloid-beta is used to identify its binding sites through light irradiation. Afterwards, the researchers used molecular dynamics to simulate the binding sites of amyloid-beta with the rhenium complex. The identified locations were further confirmed by the identification of oxidised sites via tandem mass spectrometry. This method elucidates binding sites and mechanisms that could be potentially used to design therapeutic strategies to interrupt amyloid-beta aggregation and/or accumulation.
Photochemical identification of molecular binding sites on the surface of amyloid-β fibrillar aggregates
Angel A Martí(1), Rajeev Prabhakar(2)
Added on: 08-09-2021
Effects of cortical activity on medium sized spiny neurons in dopamine-depleted contexts
October 2017
South China University of Technology, Guangzhou, China
Parkinson's disease is a major neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons that innervate the striatum. There is evidence that the overexposure of medium-sized spiny neurons to cortical glutamatergic input induces a loss of dendritic spines and dendritic length, proposed as a mechanism to protect these neurons from excess excitatory inputs. However, there is a lack of consistency in the degeneration of dendritic components in medium-sized spiny neurons in experimental conditions. To solve this, the researchers propose a computational model to investigate the amount of dendritic spines and dendritic arborization loss to restore the normal regulatory function of the basal ganglia. The results showed that dendritic spine loss and/or dendritic trees could restore normal activity in specific dopamine level conditions through different mechanisms. Furthermore, the model allowed to explore the effects of cortical activity on the morphology of medium-sized spiny neurons in dopamine-depleted conditions and it elucidated that the manipulation of cortical activity can stop the degeneration of dendrites. In this study, a new updated model is developed to propose a potential therapeutical strategy through the manipulation of cortical inputs into medium-sized spiny neurons in Parkinson's disease and dopamine depletion context to stop dendritic degeneration.
The effects of medium spiny neuron morphologcial changes on basal ganglia network under external electric field: a computational modeling study
Shenquan Liu
Added on: 09-30-2021
Mathematical modelling to improve immunotherapy design for colorectal cancer
2017
University Hospital Heidelberg, Heidelberg, Germany
To improve immunotherapy results in colorectal cancer patients, a better understanding of the complex immunological interplay within the microenvironment is crucial. In this study, the researchers generated a mathematical model from quantitative histological data from cancer patients. This model incorporates stochastic interactions between tumor cells, immune cells, and stroma and faithfully represents diverse spatial patterns observed in histological samples of human colorectal cancer tissue. This model was then used to systematically test the effect of different therapeutic interventions on this system and to create specific recommendations for effective
immunotherapies.
In silico modeling of immunotherapy and stroma- targeting therapies in human colorectal cancer
Niels Halama
Added on: 07-26-2021
Software for tumor clonal classification
2017
Washington University School of Medicine, Saint Louis, USA
Software is developed to overcome the error rates in clonal ordering in the study of tumour progression. Using a bootstrap resampling technique, that takes into account statistical variability, it is possible to identify the sample origin and subclones. This method outperformed three other widely used tools and was able to identify and classify subclones in different clinical samples of leukaemia and breast cancer; showing the potential to monitor clonal populations in tumour biopsies or to guide personalised medicine.
ClonEvol: clonal ordering and visualization in cancer sequencing
Christopher A Maher
Added on: 07-28-2021
Computational approach to study protein structural properties and transformations
2017
Southern Medical University, Guangzhou, China
Alpha-synuclein is a protein with a significant role in several diseases known as synucleinopathies. Its dimerization can trigger conformational transformations critical for its aggregation and the formation of fibrils. In this study, molecular dynamics simulations are used to investigate the mechanisms of dimerization of alpha-synuclein and its structural properties. The results show that, effectively, the monomers undergo a series of conformational transformations and they can resolve several structural features that are consistent with current experimental observations. These transformations lead to intermolecular interactions that contribute to the formation and stabilization of alpha-synuclein dimers. The researchers present a computational strategy that can help to design small molecules that can inhibit the pathological processes that lead to alpha-synuclein aggregation.
Molecular dynamics study to investigate the dimeric structure of the full-length α-synuclein in aqueous solution
Jiajie Zhang, Shuwen Liu
Added on: 08-10-2021
Computational simulations to decipher drug properties
2017
Sharif University of Technology, Tehran, Iran
Aggregation of amyloid-beta is one of the main hallmarks of Alzheimer's disease. Different strategies are being investigated to inhibit its polymerization and/or avoid amyloid formation. Here, molecular dynamics is used to elucidate the interactions of RS-0406 with different amyloid-beta polymers. RS-0406, a small organic molecule, has already shown promising results in inhibiting amyloid formation in vitro. Using experimental and computational log P values, the researchers are able to describe different mechanisms by which RS-0406 affects the conformational stability of the polymers. They found that it affects both the stabilization of monomers and the destabilization of fibril structures. In summary, with this method, it is possible to describe the unique structural features from this small molecule that affect the amyloid formation and the mechanisms behind its effect. This platform could be used to investigate drug-related properties and drug interactions with protein structures to better understand their mechanisms and design better therapeutic strategies.
Inhibition mechanisms of a pyridazine-based amyloid inhibitor: as a β-sheet destabilizer and a helix bridge maker
Hamid R Kalhor
Added on: 08-10-2021
Mathematical modelling of hair follicle cycles to test treatment for alopecia
2017
Florida State University, Tallahassee, USA
Alopecia areata (AA) is one of the most common autoimmune diseases and causes highly characteristic patterns of hair loss. A key feature of AA is that it disrupts the natural, constantly repeating cycle of hair follicles (HFs). Like so many other autoimmune diseases it shows a dynamic, chronic course of clinical disease relapses and remissions over the lifetime of patients. In the present study, the researchers aimed to overcome the limitations of in vitro models, which are very limited in time, and generate a computational and systems biology tool that could be if used for drug design purposes. The mathematical model incorporates HFs cycling and illustrates how the growth phase is interrupted by a dynamic interaction of autoreactive immune cells. The model illustrates different states and transitions from one state to another. The model allows the testing of how different processes, such as proliferation, apoptosis and input from stem cells, impact the growth of HFs in healthy versus AA individuals. In conclusion, the described model may help in evaluating the effectiveness of existing treatments and identifying new potential therapeutic targets.
Analysing the dynamics of a model for alopecia areata as an autoimmune disorder of hair follicle cycling
Atanaska Dobreva
Added on: 10-27-2021
Virtual screening method to discover tumor escape inhibitors
2017
Jilin University, Jilin, China(1)
Second Military Medical University, Shanghai, China(2)
Second Military Medical University, Shanghai, China(2)
Tumor escape is a hallmark of cancer, which brings many difficulties and troubles in cancer therapy. Indoleamine 2,3-dioxygenase 1 (IDO1) plays an important role in the immune escape of tumors, although it has emerged as a promising target for cancer therapy there are still very few drugs developed. In the present study, the researchers have designed a novel high throughput virtual screening to
search for potential IDO1 inhibitors. The screening method was used to screen commercially available compounds and identified some candidates with inhibition activity. Some of the compounds may serve as interesting starting points for future chemistry elaboration. The screening method was validated as a tool to be employed in the discovery of IDO1 inhibitors.
A novel high throughput virtual screening protocol to discover new indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors
Qing Yang(1), Yunlong Song(2)
Added on: 07-27-2021
In silico model of metabolism in cardiomyocytes
2017
Massachusetts Institute of Technology, Cambridge, USA
Ischemic heart disease is one of the leading causes of death and occurs when the circulation of the blood is restricted, thereby limiting the delivery of nutrients and removal of metabolic by-products. The heart can be saved by restoring blood flow using reperfusion techniques but it carries the risk of damaging additional heart tissue (ischemia/reperfusion injury). In the present study, the researchers aimed at building a metabolism model allowing identification of precursor conditions to ischemia/reperfusion injury. The researchers developed an in silico model of glucose metabolism within the cardiomyocyte over time. Reduced oxygen levels and ATP consumption rates were simulated to characterize metabolite responses to ischemia. By tracking biochemical species within the cell, the model enables prediction of the cell's condition up to the moment of reperfusion. The model described in the study provides a time-dependent framework for studying various intervention strategies to change the outcome of reperfusion.
Modeling oxygen requirements in ischemic cardiomyocytes
C. Forbes Dewey Jr
Added on: 12-02-2021
Mathematical model of immunomodulated tumor growth predicts responsiveness to immunotherapy
2017
Center of Cancer Systems Biology, Boston, USA
Immune response can both stimulate and inhibit tumor growth. The interplay between these competing influences of the immune system has complex implications for tumor development, cancer dormancy, and immunotherapies. The study builds a mathematical model able to predict non-intuitive yet clinically observed patterns of immunomodulated tumor growth. It may provide a means to help classify patient response dynamics to aid the identification of appropriate treatments exploiting immune response to improve tumor suppression, including the potential attainment of an immune-induced dormant state.
Modeling the dichotomy of the immune response to cancer: cytotoxic effects and tumor-promoting inflammation
Philip Hahnfeldt
Added on: 07-06-2021
Pathways monitored in prognostic signature of breast cancer subtypes
2017
Toronto General Research Institute—University Health Network, Toronto, Canada(1)
University of Toronto, Toronto, Canada(2)
University of Toronto, Toronto, Canada(2)
Validation in human breast tumor is performed in samples of a 17-gene prognostic signature for HER2 enriched tumor-initiating cells. The main objective of this study is to identify the biological pathways that monitor the prognostic genes. With a series of computational methods and human tumor samples, it is shown that the main pathways involved correspond to cell proliferation, immune response and cell migration. Additionally, it identifies substitutes and 6 core genes that would facilitate the clinical implementation of this method.
Identification of cell proliferation, immune response and cell migration as critical pathways in a prognostic signature for HER2+:ERα- breast cancer
Jeffrey C Liu(1), Eldad Zacksenhaus(2)
Added on: 08-01-2021
New microRNA biomarkers for Parkinson's disease
2017
Affiliated Institute of the University of Lübeck, Bolzano, Italy
Parkinson's disease is a highly prevalent neurodegenerative disorder characterized by the massive loss of dopaminergic neurons, which leads to motor and cognitive dysfunction and, ultimately, death. Currently, there is a lack of biomarkers and early diagnostic tools for this disease. MicroRNAs have been shown to be dysregulated in several pathologies, including Parkinson's disease. Here, the microRNA profiles of plasma and white blood cells of L-dopa treated and non-treated patients are investigated to assess if they are interchangeable biomarker sources for early detection of Parkinson's disease. The results showed that the microRNA profiles of the two groups of patients have differences. Moreover, the expression profiles of plasma and white blood cells were also different. The analysis showed that miR-30a-5p could be a potential biomarker in plasma samples of Parkinson's patients, and an in silico analysis suggested that it is related to mitochondrial function and autophagy. Overall, this study proposes a new microRNA marker that could potentially develop into a new biomarker for the diagnosis of Parkinson's disease and reveals that plasma and white blood cells are not interchangeable for the analysis of biomarkers. Additional studies are needed to understand the modulation of miR-30a-5p in Parkinson's disease and how L-dopa treatment can influence microRNA expression profiles.
Plasma and white blood cells show different miRNA expression profiles in Parkinson’s disease
Luisa Foco, Christine Schwienbacher
Added on: 09-11-2021
In silico simulation to distinguish components of pulmonary arterial hypertension
2017
Maastricht University, Maastricht, Netherlands
Pulmonary arterial hypertension (PAH) is a disease characterized by a high mean pulmonary arterial pressure which impairs the correct function of the right ventricle (RV). Increased RV wall tension has been suggested to cause rapid leftward septal motion (RLSM) in the left ventricle. Progression of RV failure is associated with high mortality in patients with PAH. In the present study, the researchers aimed at using an in silico approach to more accurately assess RV function which could improve the diagnosis of RV failure. The researchers adapted the existing CircAdapt computational model to simulate myocardial tissue and pump function. Simulations of healthy circulation and mild, moderate, and severe PAH were performed. The researcher also assessed the co-existing effects of RV and RLSM. The model allows identifying how RV and RLSM evolve and influence each other through time and allow for a better understanding of disease progression.
Why septal motion is a marker of right ventricular failure in pulmonary arterial hypertension: mechanistic analysis using a computer model
Georgina Palau-Caballero
Georgina Palau-Caballero et al. American Journal of Physiology - Heart and Circulatory Physiology 20 [430]
EURL ECVAM [431]
Added on: 12-03-2021
Mathematical prediction of neurodegenerative disease' progression
2017
University of Western Australia, Crawley, Australia
One of the limitations of epidemiological studies is the lack of long-term data of longitudinal studies. To overcome this problem, this study presents a mathematical model to infer the underlying long-term trajectories of short-term sparse follow-up data from Alzheimer's disease studies. Through a step-wise method, the researchers are able to build a model that can reliably predict the disease progression curve, allowing them to build the sigmoidal trajectories of the disease. As a demonstration, throughout the study, they were able to quantify the long-term progression of the pathogenesis of amyloid-beta burden in the neocortex with the data from the Alzheimer's Disease Neuroimaging Initiative. In summary, this predictor model will be helpful to overcome the limitations of epidemiological studies in which participants' data collection has been abruptly interrupted and make it possible to quantify and understand full disease progression predicting long-term epidemiological data of neurodegenerative diseases.
Constructing longitudinal disease progression curves using sparse, short-term individual data with an application to Alzheimer's disease
C A Budgeon
Added on: 08-04-2021
Static electrical field affects amyloid beta aggregation
2017
Xidian University, Xi’an, China
Alzheimer's disease is the most prevalent neurodegenerative disorder. It is characterized by a progressive accumulation of amyloid beta peptides through aggregation, which has been thought to be a causal mechanism of the disease. Amyloid beta aggregation has been observed to depend on several factors, thus it is a very complex process to study. Here, a theoretical model is used to study the impact of a static electric field present in the human brain on the conformation of the amyloid beta 29-42 dimer. The simulations performed suggested that the electric field promoted the formation of beta-hairpins, an intermediate form thought to be important for the aggregation. Furthermore, the results showed that the application of different electrical field forces can help to reduce the conformational heterogeneity of amyloid beta 40/42 dimers to more easily elucidate insights into their structures that could have an influence on disease-related mechanisms. Overall, this study provides theoretical support to further explore the structural features of amyloid beta aggregates and further experiments combining different factors that can affect amyloid beta structure.
Small static electric field strength promotes aggregation-prone structures in amyloid-β(29-42)
Yan Lu
Added on: 09-30-2021
Computational model of the basal ganglia
2017
University of Sheffield, Sheffield, United Kingdom
Neural oscillations in the basal ganglia are well-studied and have been described to correlate with behaviour. However, the mechanisms underlying this correlation and their functional significance are not well understood. Here, a computational model of the basal ganglia is developed and fitted to experimental recordings of nuclei of the basal ganglia after cortical stimulation and used to predict the causal mechanisms of different frequency bands. This new model allowed the researchers to observe that inputs related to motor tasks induced beta and gamma frequency oscillations as seen in vivo and identified which network pathways are required to observe these frequencies. The evidence in this study suggests that this new model can provide a coherent framework to analyse several features of the healthy basal ganglia and sets the basis for a better comprehension of basal ganglia-related pathologies like Parkinson's disease.
Frequency and function in the basal ganglia: the origins of beta and gamma band activity
Alexander Blenkinsop
Added on: 09-30-2021
Photon-counting spectral mammography for classification of breast cancer
2017
U.S. Food and Drug Administration, Silver Spring, USA
Breast cancer classification using photon-counting spectral mammography is validated through a simulation of breast calcifications. When applied to the simulated events, this method was able to discriminate between different types of microcalcifications. Therefore, the results support the potential of this method as a non-invasive technique to improve early breast cancer diagnosis.
Investigating the feasibility of classifying breast microcalcifications using photon-counting spectral mammography: a simulation study
Bahaa Ghammraoui
Added on: 07-28-2021
Stimulation of T cells with tumor-specific peptide to develop immunotherapy
2017
University of Tübingen, Tübingen, Germany
The discovery of antigens specific to tumor cells is of crucial importance to develop efficient immunotherapy against cancer. Recent genome sequencing has uncovered a recurring somatic and oncogenic driver mutation of the Toll-like receptor adaptor protein MYD88 with the potential to be a specific antigen used for immunotherapy. Using in silico predictions, the researchers identified different MYD88L265P peptides and tested them for their stimulation capacity on T cells obtained from diseased patients. Cytotoxic capacity after stimulation was tested in vitro. The study shows the potential of stimulation to generate tumor-specific immunotherapy.
HLA class I-restricted MYD88 L265P-derived peptides as specific targets for lymphoma immunotherapy
Alexander N. R. Weber
Added on: 07-29-2021
In silico models of heart electrophysiology in ischemic conditions
2017
University of Oxford, Oxford, United Kingdom
One of the major causes of sudden cardiac death is acute myocardial ischemia, resulting from an imbalance in the supply and demand of oxygen and nutrients to the heart. During the first 10–15 min of ischemia, metabolic and electrophysiological changes occur rapidly and vary spatially. In the present study, the researchers aimed at investigating the response of four recent computational human-specific ventricular action potential (AP) models to varied ischemic conditions by comparing electrophysiological properties in single-cell and tissue simulations to assess their utility for studying mechanisms of arrhythmogenesis during the initial phase of acute myocardial ischemia. The study concludes that quantitative differences are observed between the models and overall, the ten Tusscher and modified O'Hara models show the closest agreement to experimental data.
Electrophysiological properties of computational human ventricular cell action potential models under acute ischemic conditions
Sara Dutta
Added on: 12-01-2021
Mathematical model to assess a combination of cancer therapies
2017
Winthrop University, Rock Hill, USA
A recent theory for tumor growth suggests that a small population of cancerous cells known as cancer stem cells have stem cell-like qualities. In the present study, the researchers developed a mathematical model of the effectiveness of immunotherapy and chemotherapy for the treatment of tumor cells and cancer stem cells. The researchers present conditions on treatment parameters to guarantee a globally attracting tumor clearance state. Further work on this model could incorporate healthy cells and monitor the detrimental effect chemotherapy has on the overall health of a patient. Also in the future, an updated model could consider different submodels for possible cancer persistent states or other specific chemotherapy and immunotherapy agents.
Global dynamics of a colorectal cancer treatment model with cancer stem cells
Kristen Abernathy
Added on: 07-27-2021
Computational method to diagnose breast cancer
2017
Northwest University, Xi’an, China
The study describes the development of a computational method to detect clustered microcalcifications from mammograms for early diagnosis of breast cancer. Based on a series of algorithms, this method allows classifying the samples in lesioned or normal breast tissues. When tested with synthetic and real samples, it reduces false-positive rates while maintaining the true positive rate. This computational tool can potentially be useful for the early diagnosis of breast cancer.
Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection
Jun Feng
Added on: 08-01-2021
A model system for stroke research
2017
Charité – Universitätsmedizin Berlin, Berlin, Germany
A model platform is established that simulates human brain tissue in two and three-dimensional systems. Human neurons and brain organoids are designed to study complex disease processes and develop new treatments for stroke. Various methods of stem cell biology, chemical biology, biophysics and structural biology were combined to investigate the complex disease processes of acute neurodegeneration in stroke and to improve drug development for treatment.
Schlaganfallforschung: Modellsystem kann Tierversuche ersetzen
Harald Stachelscheid
Added on: 05-25-2020
Biochemical diagnosis of Parkinson's disease
December 2016
The University of Texas School of Medicine at Houston, Houston, USA
Parkinson's disease is a devastating neurodegenerative disorder characterized by a massive dopaminergic loss that leads to motor and cognitive decline and, ultimately, death. Despite its high prevalence, there is a lack of diagnostic tools that allow a non-invasive biochemical evaluation to help in the early diagnosis and monitoring of the disease. One of the main pathological processes in Parkinson's disease is the accumulation of alpha-synuclein aggregates, which can be detected in small quantities in patients' cerebrospinal fluid. Here, a protein misfolding cyclic amplification is used to detect minimum amounts of alpha-synuclein aggregates, through a strategy of signal amplification. After the first validation with synthetic oligomers in vitro, this technique allowed for the identification of 88.5% of Parkinson's disease patients, with a specificity of 96.9% when using samples of patients with different neurodegenerative disorders. Additionally, the results of this test highly correlated with the severity of the disease. Here, the researchers propose a new non-invasive diagnostic approach to efficiently identify and discriminate Parkinson's patients, with a potential use in monitoring of the disease.
Development of a biochemical diagnosis of Parkinson disease by detection of α-synuclein misfolded aggregates in cerebrospinal fluid
Claudio Soto
Added on: 09-04-2021
Genomic analysis of estrogen receptor-associated breast cancer
December 2016
University of Pittsburgh, Pittsburgh, USA(1)
University of Pittsburgh Cancer Institute, Pittsburgh, USA(2)
University of Pittsburgh Cancer Institute, Pittsburgh, USA(2)
Estrogen receptors are known to be critical for breast cancer through DNA binding mechanisms, leading to transcriptomic and phenotypic changes. Thus, single nucleotide variants in estrogen receptor binding sites might be involved in disease progression. Here, a computational analysis to identify single nucleotide variants in estrogen receptor binding sites is performed using chromatin immunoprecipitation sequencing data from different breast cancer models and further validated with human breast cancer cells to identify allele-specific binding. The analysis identified an intronic single nucleotide variant predicted to increase estrogen receptor binding and was experimentally validated. Furthermore, 17 regulatory single nucleotide variants correlated with expression of adjacent genes in estrogen receptor-associated breast cancer, from which GSTM1 promoter was the top candidate and showed to be correlated with higher expression of GSTM1 in estrogen receptor-associated tumors and better outcome in patients. Overall, the researchers establish a computational pipeline that can be used to investigate and elucidate key single nucleotide variants that can potentially regulate target genes contributing to the outcome of breast cancer in patients.
Non-coding single nucleotide variants affecting estrogen receptor binding and activity
Adrian V Lee(1), Steffi Oesterreich(2)
Added on: 10-12-2021
Mathematical model of psoriasis
December 2016
Jadavpur University, Kolkata, India
The immune system executes coordinated responsibility for the progression of the disease psoriasis, especially T-Cells mediated hyper-proliferation of epidermal keratinocytes. In the present study, the researchers aimed at developing a mathematical model of psoriasis involving T-Cells, keratinocyte cell populations and the effect of cytokine release. The model allows a prediction of the dynamics of the disease and should enable optimal drug dosing to reduce keratinocyte proliferation.
Fractional-order model of the disease psoriasis: a control based mathematical approach
Roy Priti Kumar
Added on: 10-28-2021
Amyloid-beta anti-aggregation drug assessment
November 2016
Jagiellonian University Medical College, Cracow, Poland
Amyloid-beta aggregation is one of the hallmarks of Alzheimer's disease and, in the amyloid hypothesis, the main driver of its pathogenesis. In the last years, it has been the targeted pathological process to design therapeutical strategies. In this study, a computational approach is described to study inhibitory molecules of amyloid-beta aggregation. The researchers used molecular docking and all-atom molecular dynamics simulations and found out that the number of backbone hydrogen bonds is related to the anti-aggregation properties of these compounds and that these were able to destroy the beta-sheet amyloid structures. Here, valuable data is provided that can be potentially used for the design of novel inhibitors of amyloid-beta aggregation in Alzheimer's disease.
Computational approach for the assessment of inhibitory potency against beta-amyloid aggregation
Marek Bajda
Added on: 08-16-2021
Computational model to predict the efficiency of inferior vena cava filters to trap embolus
November 2016
The Pennsylvania State University, University Park, USA(1)
United States Food and Drug Administration, Silver Spring, USA(2)
United States Food and Drug Administration, Silver Spring, USA(2)
Pulmonary embolism (PE) occurs when an embolus occludes the blood flow in the lungs. When patients are irresponsive to anticoagulants, an inferior vena cava (IVC) filter may be placed to serve as a mechanical barrier to embolus passage but unfortunately, complications with IVC filters remain common. In the present study, the researchers aimed at building a computational model of embolus transport that could be used to help engineers and clinicians improve the performance of IVC filters. The developed model combines simulations of fluid dynamics and embolus transport while also resolving interactions between embolus and surroundings. The model was validated using literature data. Further, the researchers used the model for simulations of IVC efficiency with variations of different parameters such as filter geometry, placement, and embolus diameter. The computational tool should be refined in the future and used to investigate IVC filter design improvements and the effects of patient anatomy to improve performance.
A resolved two-way coupled CFD/6-DOF approach for predicting embolus transport and the embolus-trapping efficiency of IVC filters
Keefe B. Manning(1), Brent A. Craven(2)
Added on: 11-28-2021
Development of a web-based platform to help designing immunotherapy against cancer
November 2016
CSIR-Institute of Microbial Technology, Chandigarh, India
Identification of cancer-specific epitopes or neoepitopes from cancer genomes is one of the major challenges in the field of immunotherapy or vaccine development. Due to advancements in sequencing technology, the genomes of thousands of cancer tissues or cell lines have been sequenced. In the present study, the researchers developed an in silico platform (called Cancertope) for designing genome-based immunotherapy or vaccine against cancer cells. To do this, the researchers analyzed the mutational profile of 905-cancer cell lines and identified neoepitopes that can activate different arms of the immune system. The platform can be used to look for cancer antigens but can also be used in a personalized fashion to identify neoepitopes from the genomes of cancer patients. Cancertope is a web-based platform open for use by the scientific community.
A platform for designing genome-based personalized immunotherapy or vaccine against cancer
Gajendra P. S. Raghava
Added on: 09-17-2021
Individualised cancer metabolic models
November 2016
University of Costa Rica, San José, Costa Rica
The study describes the development of a computational approach to predict cancer prognosis. The cancer-specific models are developed by combining existing general metabolic models with transcriptomic data. As a test, different expression datasets of breast cancer cell lines are used to generate 3 cancer-specific models and allow the description of specific metabolic signatures related to aspects of human breast cancer. This application can help in personalised cancer targets to improve cancer treatments.
A biocomputational application for the automated construction of large-scale metabolic models from transcriptomic data
R.A. Mora-Rodriguez, Edwin Baez-Villalobos
Edwin Baez-Villalobos et al. IEEE 36th Central American and Panama Convention (CONCAPAN) 2016 [461]
EURL ECVAM [462]
Added on: 07-29-2021
In silico analysis of estrogen receptor alpha signaling pathway
October 2016
National University of Science and Technology, Islamabad, Pakistan
The construction of a discrete model is described to analyse the behaviour of estrogen receptor alpha-associated signalling pathways to understand some of the mechanisms that lead to breast cancer metastasis. The model was able to determine gene-gene interactions of different receptors that lead to inhibition of tumour suppressor genes, giving new insights to direct further research in the treatment of human breast cancer therapies.
Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer
Rumeza Hanif, Samra Khalid
Added on: 07-29-2021
Alzheimer's disease research by screening inhibitory molecules targeting acetylcholinesterase
2016
Amrita University, Kochi, India
Cognitive decline in Alzheimer's disease might be led by dysregulation of acetylcholine, known as the cholinergic hypothesis. For this reason, several treatment strategies for this neurodegenerative disorder are based on targeting the inhibition of acetylcholinesterase, for example with donepezil, but which can cause severe side effects. In this study, a 3D-pharmacophore model using specific inhibitors was used for sequential virtual screening from small-molecule databases. Five molecules, selected based on their docking scores and pharmacokinetic properties, were then tested against the crystal structure of human acetylcholinesterase to reveal their binding mechanisms. After confirming their ADMET profiles, these molecules were subjected to Ellman's assay to assess their inhibitory activity, which for three of the five selected molecules was shown to be weaker than Donepezil. Here, the researchers propose a methodology that can lead to the discovery of existing molecules that can have better inhibitory activity of acetylcholinesterase than those used currently in clinical applications.
Integration of common feature pharmacophore modeling and in vitro study to identify potent AChE inhibitors
C Gopi Mohan, Krishnakumar N Menon
Added on: 08-15-2021
Computational model to study side effects of deep brain stimulation
2016
Indian Institute of Technology Madras, Chennai, India
The subthalamic nucleus has been described to have a central role in conflictive decision making. Furthermore, in Parkinson's disease patients with deep brain stimulation surgery in the subthalamic nucleus, it was observed that conflictive decision making was impaired leading to impulsive behaviour. Here, a 2D computational model of different components of the basal ganglia is used to decipher the mechanisms behind these adverse effects. This model was complemented with experimental data and used to compare the outcome of probabilistic learning tasks in different groups of untreated and treated Parkinson's patients and a group that underwent deep brain stimulation surgery in the subthalamic nucleus. The results showed that treated groups made impulsive (small reaction time) decisions which led to poor performance. Moreover, depending on the position of the electrode for the deep brain stimulation in the subthalamic nucleus, there was a decrease in neural activity. Finally, antidromic activation of the globus pallidus externa decreased reaction time in deep brain stimulation patients without altering learning abilities. Overall, this model allowed the researchers to elucidate the potential causes of conflictive decision making alterations in Parkinson patients with deep brain stimulation in the subthalamic nucleus that can be further studied in experimental setups.
Probing the Role of medication, DBS electrode position, and antidromic activation on impulsivity using a computational model of basal ganglia
V. Srinivasa Chakravarthy
Added on: 09-24-2021
Database for the creation of an interaction network in humans
2016
Massachusetts General Hospital, Boston, USA(1)
Max Planck Institute for Molecular Genetics, Berlin, Germany(2)
Max Planck Institute for Molecular Genetics, Berlin, Germany(2)
The database ConsensusPathDB-human integrates interaction networks in humans including binary and complex protein-protein, genetic, metabolic, signalling, gene regulatory and drug-target interactions, as well as biochemical pathways. Currently, the data originate from 32 public resources for interactions and from interactions that the researchers have curated from the literature. The interaction data are integrated in a complementary manner (avoiding redundancies), resulting in a seamless interaction network containing different types of interactions.
Analyzing and interpreting genome data at the network level with ConsensusPathDB
Atanas Kamburov(1), Ralf Herwig(2)
Added on: 02-14-2022
In silico model of the arterial tree for the diagnosis of cardiovascular disease
2016
University of Maryland, College Park, USA
Among a wide range of cardiovascular diseases, peripheral artery disease (PAD) and arterial stiffening stand out due to their high prevalence. However, techniques currently available to detect and diagnose PAD and arterial stiffening have ongoing limitations. In the present study, the researchers aimed at developing a new approach to cardiovascular disease diagnosis based on an analysis of arterial mechanical properties manifested in blood pressure waveforms. The approach was tested in a full-scale in-silico arterial tree simulation. The results showed that the approach exhibited superior sensitivity and convenience to current methods. The study shows that this approach coupled with the development of measurements of blood pressure waveform will evolve into an alternative for cardiovascular disease diagnosis.
Model-based cardiovascular disease diagnosis: a preliminary in-silico study
Jin-Oh Hahn
Added on: 12-03-2021
Mathematical model of psoriasis incorporating cytokines dynamics
2016
University of Kent, Canterbury, United Kingdom
Psoriasis is a chronic inflammatory skin disease that affects millions of people worldwide. As far as now, the computational model of psoriasis have lacked integration of the dynamics of cytokines, the signalling molecules of the immune system. In the present study, the researchers develop a mathematical model of psoriasis is developed incorporating interactions between different cell types, mediated by the cytokines they produce. The possibility of some of the cytokines acting as fast actuators to the cell population dynamics was investigated. The analysis shows that the system can display two steady states reflecting normal and psoriatic skin conditions. The study explores the inherent finite time nature of the underlying biology of psoriasis and demonstrates an analysis approach that could be used for other biological systems.
Modelling and finite-time stability analysis of psoriasis pathogenesis
Sarah K. Spurgeon
Added on: 10-28-2021
In silico model of calcium channel mutation to predict effect on ventricular tachycardia
2016
Harbin Institute Technology, Harbin, China
Ventricular tachycardia (VT) characterized by high rates of ventricular excitation may cause sudden cardiac death. Electrical remodelling of ion channels was documented to be a major contributory factor of initiating and sustaining VT. Mutations in the CACNA1C gene, coding for a calcium channel subunit, have been linked to VT.
In the present study, the researchers aimed at uncovering the mechanisms by which mutations in CACNA1C could lead to VT. The researchers developed 1D, 2D and 3D computational models of calcium dynamics changes induced by CACNA1C mutation in cardiomyocytes. The computational tools showed that increased calcium influx in the mutation provoked increased vulnerability to unidirectional conduction block in response to a premature stimulus, facilitating the initiation and maintenance of VT. The study concludes that the increased repolarization dispersion caused by the CACNA1C mutation is a primary factor contributing to cardiac arrhythmias.
Pro-arrhythmogenic effects of CACNA1C G1911R mutation in human ventricular tachycardia: insights from cardiac multi-scale models
Kuanquan Wang
Added on: 12-02-2021
Let-7 miRNAs disrupt triple-negative breast cancer stem cells activity
2016
The First Affiliated Hospital of Zhengzhou University, Henan, China
Let-7 miRNAs family has been shown to be able to disrupt the normal functioning of cancer stem cells. However, their therapeutic potential in cancers with a bad prognosis like triple-negative breast cancer remains unknown. Here, different in vitro models using human breast cancer cell lines were used to investigate the inhibitory effect of let-7 miRNAs on self-renewal abilities of triple-negative breast cancer stem cells. The results showed that let-7 could reduce the number of mammospheres and had synergistic effects with radiation on hindering stem cell renewal. Moreover, the signalling pathway by which let-7 acts was identified. Consequently, the re-activation of the let-7 inhibited pathway through recombinant protein signalling abolished the effects of let-7. Overall, the researchers deciphered the signalling pathway involved in the anti-tumor activity of let-7 miRNAs family and suggest new therapeutic targets to reduce cancer stem cell activity in triple-negative breast cancer treatment.
Let‑7 miRNAs sensitize breast cancer stem cells to radiation‑induced repression through inhibition of the cyclin D1/Akt1/Wnt1 signaling pathway
Jianbo Gao
Added on: 10-31-2021
Cancer therapies combination assisted by mathematical modeling
2016
Aix Marseille University, Marseille, France(1)
Assistance Publique-Hopitaux Marseille, Marseille, France(2)
Assistance Publique-Hopitaux Marseille, Marseille, France(2)
Combining radiotherapy with immunotherapy may offer a considerable therapeutic impact. In this study, the researchers propose a set of mathematical equations that describe the pharmacodynamics of radiotherapy in combination with two paradigmatic immunotherapies used. The modelling offers an explanation for the reported biphasic relationship between the size of a tumor and its immunogenicity and how synchronizing immunotherapy and radiotherapy can produce synergies. The ability of the model was validated retrospectively by checking data from experimental studies. Such a model could further facilitate decision making about optimal scheduling of immunotherapy with radiotherapy.
Mathematical modeling of cancer immunotherapy and its synergy with radiotherapy
Dominique Barbolosi(1), Xavier Muracciole(2)
Added on: 07-26-2021
Mathematical model for reactive oxygen species-induced cell death mechanisms
2016
University of Waterloo, Waterloo, Canada
A mathematical model is used to understand and decipher the mechanisms of ascorbic acid-induced cytotoxicity on cancer cells. This model determines that reactive oxygen species-induced cell death relies on membrane properties. This is confirmed with experimental data from in vitro assays with human breast cancer cells. These findings provide key insights in the mechanisms of ascorbic acid-induced cancer cell death and confirm this model as a potential tool to understand the mechanisms of selective drugs.
Drug-induced reactive oxygen species (ROS) rely on cell membrane properties to exert anticancer effects
Mohammad Kohandel, Hamid R Molavian
Added on: 07-21-2021
Screening platform for assessment of NK cell cytotoxicity
2016
Karolinska Institutet, Tumor and Cell Biology, Stockholm, Sweden(1)
KTH – Royal Institute of Technology, Solna, Sweden(2)
KTH – Royal Institute of Technology, Solna, Sweden(2)
Cytotoxic effector lymphocytes, such as natural killer (NK) cells and T cells, are important for immune defence against cancer and viral infections, the traits that have made these cells valuable in adoptive cell therapy. This screening platform can be used for assessment of the cytotoxic potential of individual natural killer (NK) cells within larger populations. Human primary NK cells were distributed across a silicon–glass microchip containing a high number of individual microwells loaded with target cells. Through fluorescence screening and automated image analysis, the numbers of NK and live or dead target cells in each well could be assessed at different time points after initial mixing. Cytotoxicity was also studied by time-lapse live-cell imaging in microwells quantifying the killing potential of individual NK cells. Moreover, the screening approach was adapted to increase the chance to find and evaluate serial killing NK cells. This approach could find use in clinical applications, e.g., in the selection of donors for stem cell transplantation or generation of highly specific and cytotoxic cells for adoptive immunotherapy.
Microchip screening platform for single cell assessment of NK cell cytotoxicity
Björn Önfelt(1, 2)
Added on: 05-27-2020
Subtypes of degenerative mitral valve disease distinguished by microRNAs profiling
2016
National University of Singapore, Singapore, Singapore
In developed countries, degenerative mitral valve disease (DMVD) is the leading indication for mitral valve surgery. Two variants can be distinguished: myxomatous mitral valve prolapse (MMVP) and fibroelastic deficiency (FED). In the present study, the researchers aimed at understanding the molecular mechanisms differentiating these two types. As a model, the researchers used valvular specimens from MMVP and FED patients obtained from a tissue bank. Total RNA was extracted from the tissues and micro RNA were sequenced. A cluster of differentially expressed microRNAs was identified. In silico analysis of potential target sites for regulation by these microRNAs revealed genes involved in extracellular matrix homeostasis. The study showed microRNAs which could be used as diagnostic biomarkers and targets for therapeutics development.
Differential microRNA expression profile in myxomatous mitral valve prolapse and fibroelastic deficiency valves
Yei-Tsung Chen
Added on: 11-30-2021
Virtual screening of HER2-targeting drugs
2016
Alagappa University, Karaikudi, India
Using delphinidin as a query parent, this study is based on searching for homologous compounds that have safer pharmacological profiles for HER2 targeting in human breast cancer. After a screening of PubChem database and further validation procedures, a compound was identified with a higher affinity than delphinidin and a better ADMET profile. This study can serve as a starting point for the "in vitro" assessment of this identified HER2-targeting drug.
Virtual screening approaches in identification of bioactive compounds akin to delphinidin as potential HER2 inhibitors for the treatment of breast cancer
Sanjeev Kumar Singh
Added on: 07-30-2021
miRNAs as biomarkers of breast cancer
2016
Georgetown University Medical Center, Washington, USA
In recent years, microRNAs have been described as potential solid biomarkers for breast cancer detection and evaluation. However, there is still high variability among different studies, which leads to difficulties in the interpretation of the data. Other sources than blood have the potential to be used for biomarker detection and solve some of its limitations, like ductal lavage and nipple aspirate fluids. Here, fluid from the ductal lavage of breast cancer patients was used to perform a miRNA transcriptomic analysis. The results identified 17 differentially expressed miRNAs in breast tumors related to several processes of breast cancer development and metastasis. These miRNAs were not limited to this fluid, but could also be detected in samples of other origins. Several signalling pathways involved in breast cancer were elucidated through the analysis of the candidate miRNAs. Additionally, it was described that the miRNA expression profile was specific to different histological types of the tumors. Overall, the researchers demonstrate that the analysis of miRNAs in the ductal fluid can be a potential tool to be used as biomarkers of breast cancer with non-invasive procedures.
MicroRNA analysis of breast ductal fluid in breast cancer patients
Bassem R Haddad
Added on: 10-25-2021
Mathematical model to improve local radiotherapy site decision
2016
H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA
For many years it has been speculated that localized radiotherapy for cancer metastases can occasionally generate a host immunotherapeutic response known as the abscopal effect. In the present study, the researchers describe a mathematical model that incorporates physiological information about immune cell trafficking through the patient circulatory system. The study shows that immune cell distribution varies significantly between different metastatic sites and depends on the immune system activation site. The presented framework could be used to inform about patient-specific treatment targets in metastatic patients.
The ability to purposefully and reliably induce abscopal effects in metastatic tumors could meet many unmet clinical needs.
Abscopal benefits of localized radiotherapy depend on activated T cell trafficking and distribution between metastatic lesions
Jan Poleszczuk
Added on: 07-26-2021
ToxCast ER Pathway Model for endocrine disruptors
Validated Method 2016
European Union Reference Laboratory for Alternatives to Animal Testing, Ispra, Italy
The ToxCast estrogen receptor (ER) pathway model is a mathematical model that combines the results from 18 high-throughput screening (HTS) assays from the ToxCast and Tox21 research programs. The HTS assays measure ER binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“assay interference”). The output of the model provides an area under the curve (AUC) value for the potential of a chemical to cause ER agonism, normalized with respect to the positive control chemical, estradiol. Validated and regulatory accepted under TM2016-08 (US) by EURL ECVAM (TSAR list).
The ToxCast Estrogen Receptor Agonist Pathway Model
EURL ECVAM
Added on: 07-03-2020
Development of dopaminergic synapse computational model
December 2015
University of Ulster, Londonderry, United Kingdom
Homeostatic dopamine release is essential for the proper function of the brain, and the disruption of the dopaminergic system leads to neurological disorders. Currently, there is a lack of efficient and integrated models that allow linking in one model the molecular and neuronal circuit levels of the dopaminergic system. Here, the researchers try to develop a realistic computational model that efficiently represents a dopaminergic pre-synaptic terminal. Starting from an already established computational model, it was possible to simplify it and reduce it to two time-scale models. Moreover, both the original and the reduced model have similar dynamics, while the reduced version is more computationally efficient and can be used to investigate underlying key mechanisms. Finally, this reduced model was combined with a spiking neuronal model, with a later inclusion of an autoreceptor-mediated inhibitory current, to realistically simulate dopaminergic neuronal behaviour. In conclusion, a new integrated computational model is developed and represents the first steps towards an efficient computational platform to simulate the dopaminergic system, which could have great potential in drug discovery and development.
Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors
KongFatt Wong-Lin
Added on: 09-11-2021
Expression profiling and in silico modelling in patients to perdict tissue compatibility
December 2015
Virginia Commonwealth University, Richmond, USA
Stem cell transplantation from HLA-matched donors (HLA is one of the main genetic markers for tissue compatibility) delivers curative therapy to patients with hematologic malignancies, but at the cost of significant morbidity derived from graft-versus-host disease and the immunosuppression administered to control it. In the present study, the researchers used whole-exome sequencing combined with in silico peptide-binding evaluation to estimate potential alloreactivity, resulting in GVHD. The study was conducted on 34 donor-recipient pairs for stem cell transplantation who were HLA-matched. The methodology was shown to be able to provide a quantitative basis for refining donor selection and titration of immunosuppression after stem cell transplantation.
Dynamical system modeling to simulate donor T Cell response to whole exome sequencing-derived recipient peptides demonstrates different alloreactivity potential in HLA-matched and -mismatched donor–recipient pairs
Amir A. Toor
Added on: 09-19-2021
Computational model of Parkinson's disease symtoms
November 2015
RWTH Aachen University, Aachen, Germany
Parkinson's disease is characterized by a progressive loss of dopaminergic neurons that leads to a reduction of dopamine in the basal ganglia. One of the consequences of this pathology is the freezing of articulatory movements during speech production. To further investigate this phenomenon, this study uses a computational approach to simulate syllable sequencing tasks by modelling the cortico-basal ganglia-thalamus-cortical action selection loop altering dopamine levels. Two parameters were used to represent the effects of D1 and D2 receptors and allow to differentiate and modify the different dopamine levels in the striatum. The results show that by decreasing dopamine by 50% it was possible to replicate the freezing effect after less than 5 syllable productions. Moreover, the model allowed to discriminate that dopamine level reduction in D1 receptors was more preeminent in freezing of action selection in speech. The model used here allowed to reproduce the symptomatology of Parkinson's disease and to elucidate potential mechanisms that can induce this behaviour.
Reduction of dopamine in basal ganglia and its effects on syllable sequencing in speech: a computer simulation study
Bernd J Kröger
Added on: 09-23-2021
In silico simulation of electrodes placement to improve diagnosis of myocardial ischemia
October 2015
Institute of Biomedical Engineering, Karlsruhe, Germany
The ST segment in an electrocardiogram is a flat segment between two waves called S and T. Identification of ST-segment deviation from the normal baseline is an essential method for the diagnosis of myocardial ischemia. Unfortunately, some forms of ischemia cannot be identified this way. In the present study, the researchers aimed at making a computational study of different electrode setups in detecting early ischemia at 10 minutes after onset. To do this, a simulation study was performed for 765 different locations and sizes of ischemia in the left ventricle of three virtual patients. The results add to the knowledge concerning ischemia-induced electrocardiogram changes and the findings may aid in eventually reducing the share of ischemia without ST elevation in acute diagnosis, thus increasing the number of patients benefiting from immediate treatment.
ECG-based detection of early myocardial ischemia in a computational model: impact of additional electrodes, optimal placement, and a new feature for ST deviation
Axel Loewe
Added on: 12-02-2021
MicroRNA profiles for the diagnosis of Parkinson's and Alzheimer's disease
October 2015
Zhejiang University, Hangzhou, China
Clinical diagnosis of Parkinson's and Alzheimer's disease is difficult at early stages with a high risk of mixed diagnosis. Therefore, there is a need to develop tools that can reliably differentiate these diseases, as it is extremely important to start the disease-specific treatment as early as possible. Here, a microRNA profiling method is developed to analyse the exosomal microRNAs isolated from the cerebrospinal fluid of patients with Parkinson's and Alzheimer's disease. The researchers found several microRNAs differentially expressed in Parkinson's exosomes in the cerebrospinal fluid from both healthy controls and Alzheimer's patients. Afterwards, a computational method was used to analyse the enriched pathways in the Parkinson's microRNA profiles. Moreover, they found that there were other types of RNA that were also differentially expressed in exosomes of cerebrospinal fluid in Parkinson's disease and Alzheimer's disease patients. Altogether, these data support the idea of using exosomal RNA from the cerebrospinal fluid as a reliable biomarker sensitive enough to make a differential diagnosis of Parkinson's disease.
Altered microRNA profiles in cerebrospinal fluid exosome in Parkinson disease and Alzheimer disease
YaXing Gui
Added on: 08-23-2021
In silico analysis of angiography from patients to predict risk of coronary plaque rupture
2015
Seoul National University Hospital, Seoul, South Korea
Coronary plaque rupture is a critical event that triggers the initiation of an acute coronary syndrome (ACS). Although the sequence of plaque rupture is well understood with previously reported histopathological data, the prediction of plaque rupture in an individual patient is still problematic. In the present study, the researchers aimed at characterizing the hemodynamic force acting on plaques and investigating its relationship with lesion geometry. The researchers performed computational fluid dynamics analysis on coronary plaque images from 81 patients' lesions. The study establishes a link between hemodynamic stress on coronary plaque and lesion geometry which should be helpful in assessing the risk of plaque rupture and treatment strategies.
Coronary artery axial plaque stress and its relationship with lesion geometry: application of computational fluid dynamics to coronary CT angiography
Bon-Kwon Koo
Added on: 11-24-2021
Neural constructs for predicting neural toxicity
2015
Morgridge Institute for Research, Madison, USA
Human pluripotent stem cell-based in vitro models that mirror human physiology have the potential to cost-effectively assess the developmental neurotoxicity of chemicals. Here, human embryonic stem (ES)-derived neural progenitor cells, endothelial cells, mesenchymal stem cells and microglia/macrophage progenitors were combined on synthetic hydrogels and cultured in a serum-free medium to model cellular interactions in the developing brain. The progenitor cells self-assembled into 3D neuronal constructs with distinct populations of neurons and glia, interconnected vascular networks and branching microglia. The replicate constructs were reproducible by RNA sequencing (RNA-Seq) and expressed genes for neurogenesis, vascular development and microglia. Using machine learning, a predictive model was built from these RNA-Seq for the neuronal constructs exposed to a training set of 60 toxic and non-toxic chemicals and then predicted in a blind trial with a set of 10 additional compounds. The model correctly classified 9 of the 10 additional chemicals. This combined strategy demonstrates the value of cell-based assays for predictive toxicology and should be useful for assessing the safety of both drugs and chemicals.
Human pluripotent stem cell-derived neural constructs for predicting neural toxicity
James A. Thomson
Added on: 11-09-2021
Computational discovery of transcriptional regulators in cancer
2015
National Institute of Genomic Medicine, Mexico City, Mexico
Implementation of a series of algorithms to analyze gene regulatory networks and transcriptional regulators in the context of breast cancer is presented. By analyzing 880 microarrays of biopsy samples from primary breast cancer, it was possible to describe several master regulators involved in well-known hallmarks of cancer. Overall, this study shows that these techniques could be a potential tool to understand the first stages of cancer development.
Transcriptional master regulator analysis in breast cancer genetic networks
Enrique Hernández-Lemus
Added on: 07-31-2021
In-vitro to in-vivo extrapolation to predict fish growth
2015
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
This study uses a fish cell line combined with mechanism-based computational models, to quantitatively predict chemical impact on fish growth, which is a common endpoint for chronic toxicity testing.
The rainbow trout gill cell line RTgill-W1 and the two fungicides cyproconazole and propiconazole were used for in vitro tests. A physiologically based toxicokinetic (PBTK) model, which simulates a chemical’s distribution into various fish tissues and organs, was used to calculate the chemicals' concentration in the exposure medium. Cell survival and proliferation were measured for up to 120 hours. Inhibition of cell population growth was compared with the inhibition of fish growth, for which data was derived from literature.
In summary, the model, which predicts reduced fish growth based on inhibition of fish cell growth, shows good agreement with literature-derived in vivo data. The method is simple, inexpensive, and rapid, requiring only in-vitro data to calibrate the model.
Toxicology across scales: Cell population growth in vitro predicts reduced fish growth
Julita Stadnicka-Michalak
Added on: 04-15-2024
Investigation of endocrine disrupting effects in avian cells
2015
Örebro University, Örebro, Sweden
Increased exposure of birds to endocrine disrupting compounds (EDC) can result in developmental and reproductive dysfunctions. The present study aimed to determine whether two flame retardants and a metabolite of a third flame retardant can interact with the chicken androgen receptor (AR).
In silico modelling studies showed that the investigated compounds were able to dock into the chicken AR ligand-binding pocket. In vitro assays using a chicken hepatocellular carcinoma cell line revealed that all three compounds acted as chicken AR antagonists and inhibit testosterone induced AR activation. qRT-PCR analysis demonstrated that the compounds also alter expression patterns of various genes.
The results of this study suggest that the investigated compounds are potential EDCs in chicken. Moreover, the study demonstrates that the combined use of in silico and in vitro techniques is a fast and reliable procedure to identify new toxic compounds in the environment.
The brominated flame retardants TBP-AE and TBP-DBPE antagonize the chicken androgen receptor and act as potential endocrine disrupters in chicken LMH cells
Per-Erik Olsson
Added on: 02-14-2024
Mathematical model of T-cell dynamics in type 1 diabetes
2015
McGill University, Montréal, Canada
T cells generate the proper immune response(s) against pathogens using a set of surface molecules called T-cell receptors (TCRs) which allow recognition of foreign antigens. The diversity of TCR-reactivity to various antigens make some of these T cells, however, susceptible to autoreactivity to self-antigens which may cause an autoimmune disorder like Type 1 diabetes (T1D). In the present study, the researchers aimed at modelling the regulation dynamics of T cells during T1D progression. The model allows for predicting T1D progression based on adjustable parameters. The model can thus help unravel the complex interplay between T-cell pools and to optimize the therapeutic efficacy in the treatment of the disease.
Continuum model of T-cell avidity: understanding autoreactive and regulatory T-cell responses in type 1 diabetes
Anmar Khadra
Added on: 10-29-2021
Predictive model of breast cancer lymph node invasion
2015
Seoul National University Hospital, Seoul, South Korea
Luminal A breast cancer can have early complications leading to the development of lymph node metastasis. However, the methods of detection of this process are unreliable and some false negative cases have been reported. Here, a biomarker-based model is developed to predict lymph node metastasis in luminal A breast cancer using tissue samples of patients of luminal A invasive ductal carcinoma to investigate the expression of silent mating type information regulation 2 homolog 1 and apoptosis-related factors. The results showed that a combination of different specific factors of clinical and transcriptomic data had the strongest prediction performance for lymph node invasion, which also could predict shortened disease-free survival. Furthermore, it was elucidated that silent mating type information regulation 2 homolog 1 and specific apoptosis-related proteins have tumor suppressor activity in luminal A breast cancer. Overall, the researchers present a new tool that can help improve the prediction of lymph node metastasis, which can have a great impact in optimizing surgical strategies in breast cancer patients.
Expression of SIRT1 and apoptosis-related proteins is predictive for lymph node metastasis and disease-free survival in luminal A breast cancer
Han Suk Ryu, Seock-Ah Im
Added on: 10-25-2021
Biomarker identification for Alzheimer's disease
2015
Korea Basic Science Institute, Chungbuk, South Korea(1)
Université de Lille 1, Villeneuve-d’Ascq, France(2)
Université de Lille 1, Villeneuve-d’Ascq, France(2)
Alzheimer's disease is the most prevalent form of dementia and is characterized by progressive brain degeneration that leads to cognitive deficits and death. Despite the increasing knowledge available, the onset mechanisms and biomarkers of the pathology remain unclear. Here, hippocampal CA4 and dentate gyrus subfields from Alzheimer's patients were investigated with mass spectrometry-based proteomic analysis combined with label-free quantification to identify potential biomarkers. The results elucidated 113 potential markers with a 2-fold difference in protein levels in Alzheimer's patients compared to controls. Five of these proteins were identified and validated as putative Alzheimer's biomarkers. Moreover, five upstream signalling factors were identified from the 113 differentially regulated proteins. Altogether, the researchers provide new information about altered proteins in Alzheimer's disease that could potentially be used as biomarkers for the diagnosis of the disease and, also, open the door to new therapeutical targets.
Proteome-wide characterization of signalling interactions in the hippocampal CA4/DG subfield of patients with Alzheimer’s disease
Young Mok Park(1), Isabelle Fournier(2)
Added on: 10-01-2021
In silico model of cerebrospinal venous circulation
2015
IRCCS Santa Maria Nascente, Milano, Italy
In recent years, the relationship between extracranial venous system abnormalities and central nervous system (CNS) pathologies has been scrutinized but no clear link has ever been demonstrated. In the present study, the researchers aimed at shedding light on the matter by creating an in silico model of cerebrospinal venous drainage from anatomical data taken from the literature. Effects of the obstruction of the main venous outflows were simulated and the model was used to reanalyze the 112 Multiple Sclerosis patients. The model developed in the study can predict physiological and pathological behaviours with good fidelity. Further development of the model should take into account different body positions.
An anatomy-based lumped parameter model of cerebrospinal venous circulation: can an extracranial anatomical change impact intracranial hemodynamics?
Maria Marcella Laganà
Added on: 11-28-2021
Network model to study dopamine and serotonin in the basal ganglia
2015
Indian Institute of Technology Madras, Chennai, India
The basal ganglia have been proposed to contribute to risk-based decision making. However, the computational principles and neural correlates of risk computation are not well-known in this area. In previous studies, a model of reinforcement learning of the basal ganglia was proposed based on the interaction between dopamine, responsible for reward prediction error, and serotonin, related to risk prediction error. Here, the previous model is developed into a detailed network model of the basal ganglia that incorporates anatomical and cellular-level data to evaluate the contributions of dopamine-serotonin interactions in risk and reward-punishment sensitivity. An important feature of this expanded model is that it includes dopamine D1 and D2 co-expressing medium-sized spiny neurons and how dopamine and serotonin mediate their activity. The results show that serotonin has significant modulatory effects on D2 and D1-D2 co-expressing neurons, predicting the diverse functions of serotonin in the basal ganglia in risk sensitivity and reward-punishment learning. Moreover, this model could also predict the impairment of these functions in Parkinson's disease. The researchers show in this study that serotonin might have an important role in reward-punishment learning and could be a potential target to complement dopamine-based therapies in patients with deficits.
A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making
V. Srinivasa Chakravarthy
Added on: 09-22-2021
ToxCast ER model to screen chemicals for endocrine activity
Validated Method 2015
U.S. EPA, Washington, D.C., USA
The ToxCast ER model is a computational model to rapidly screen chemicals for endocrine bioactivity. Results from 18 estrogen receptor (ER) ToxCast high-throughput screening assays, measuring different points along the signalling pathway with different assay technologies, were integrated into a computational model. With an accuracy of 95%, it predicted the bioactivity of reference chemicals across a range of structures and potencies and for a relatively large set of 193 chemicals. In addition, the ToxCast ER model predicted the outcomes of EDSP Tier 1 guideline and other uterotrophic studies with > 90% accuracy. The results show that the model is a sensitive, specific, quantitative, and efficient test system. The EPA is now accepting ToxCast ER model data for 1.812 chemicals as alternatives for EDSP Tier 1 ER binding, ER transactivation, and uterotrophic assays.
Screening chemicals for estrogen receptor bioactivity using a computational model
Patience Browne
Patience Browne et al. Environmental Science & Technology 2015 [520]
United States Environmental Protection Agency (EPA) [521]
Added on: 08-18-2021
Multi-approach study of tripeptides to depolymerize amyloid beta fibrils
2015
Polish Academy of Sciences, Warsaw, Poland(1)
Slovak Academy of Sciences, Košice, Slovakia(2)
Slovak Academy of Sciences, Košice, Slovakia(2)
The aggregation of amyloid-beta and the formation of fibrils has been proposed as the main cause driving Alzheimer's disease pathology. Despite the lack of effective treatments, there is experimental data that suggests that the reversion of amyloid aggregation can reduce the symptoms of the disease. In this study, all the tripeptides were screened for their amyloid beta depolymerization capabilities. Different computational approaches revealed four tripeptides with high binding affinity to amyloid aggregates and showed that the interaction is preferably done at the hydrophobic regions of the fibrils. Also, the researchers performed "in vitro" assays to experimentally validate the candidates. They found that the four tripeptides had significant depolymerizing activity and their DC50 values were in the micromolar range, confirming the results obtained in the "in silico" analysis. This method describes a set of tripeptides with high binding affinity to amyloid beta fibrils and the mechanisms of these interactions that lead to amyloid beta depolymerization and that could be a potential therapeutic approach for Alzheimer's disease that can be further tested in future studies.
In silico and in vitro study of binding affinity of tripeptides to amyloid β fibrils: implications for Alzheimer’s disease
Mai Suan Li(1), Zuzana Gazova(2)
Added on: 08-15-2021
Inter-laboratory homogenization of Ki67 scoring
2015
University of British Columbia, Vancouver, Canada
To increase the homogeneity of Ki67 scoring results among different laboratories, researchers from18 laboratories were trained and tested through a web-based exercise to score Ki67 stained tissue microarray cases. This standardization procedure allowed for an increase in Ki67 reproducibility between these labs. This shows that using Ki67 as a biomarker could be a potential solution for breast cancer diagnostics, although future research is still needed to make it a reality.
An international study to increase concordance in Ki67 scoring
Torsten O Nielsen
Added on: 07-31-2021
Transcriptomic analysis of human striatum development
2015
University of Barcelona, Barcelona, Spain
A better understanding of neurodevelopment is necessary to properly comprehend brain physiology in different conditions. Stem cell technologies advances in recent years allow the generation of human models through the recapitulation of human development in vitro. However, there are still certain limitations and a proper evaluation of the protocols should be done by comparing the in vitro process to their in vivo counterparts. Here, human samples of the whole ganglionic eminence and adult striatum are processed and analyzed by quantitative high-throughput gene expression analysis to elucidate the gene expression patterns that drive striatum development. The results showed that the relative expression of specific genes between brain areas is a key factor in their proper development. Afterwards, these expression profiles were used to characterize the differentiation of human pluripotent stem cells through whole ganglionic eminence identity towards adult striatum-like cells. Overall, the researchers establish a transcriptomic profile to evaluate stem cell-derived tools for in vitro modelling or cell therapy strategies.
Quantitative high-throughput gene expression profiling of human striatal development to screen stem cell–derived medium spiny neurons
Josep M Canals
Added on: 11-28-2021
Computational tools to explore oxidative and immunological stress in dementia
December 2014
San Raffaele Scientific Institute, Milan, Italy
Mild cognitive impairment can increase the risk of developing Alzheimer's disease. Therefore, prediction tools are needed to know the prognosis of the disease. Pro-oxidative state and neuroinflammation are increasingly linked to dementia. So, a new computational model based on artificial neural networks is used in this study to decipher the relationship between oxidative stress and inflammation in Alzheimer's disease and mild cognitive impairment. The results show that machine learning was able to build an algorithm that, using a small amount of immunological and oxidative stress parameters, accurately classified Alzheimer's disease and mild cognitive impairment. Also, it was possible to establish a correlation between global immune deficit and cognitive impairment with a new non-linear mathematical model. Overall, this study proposes a new method to discriminate between different types of dementia, to accurately predict the possible prognosis of these cases and also to decipher new mechanisms of the pathophysiology of these disorders, making it a potentially valuable tool for clinical applications.
A global immune deficit in Alzheimer’s disease and mild cognitive impairment disclosed by a novel data mining process
Maira Gironi
Added on: 08-05-2021
In silico prediction of chemical toxicity on avian species
December 2014
East China University of Science and Technology, Shanghai, China
The aim of this study was to develop an in-silico prediction tool for chemical toxicity on avian species. Therefore, the toxicity of more than 663 diverse chemicals, including pesticides and industrial chemicals, on 17 avian species was accessed. Data sets were selected from the EPA Ecotox database and data for mallard duck and northern bobwhite quail were used to build models for avian toxicity prediction, while Japanese quail data was used to validate the models. All the chemicals were classified into three categories, i.e. highly toxic, slightly toxic and non-toxic, based on the toxicity classification criteria of the United States Environmental Protection Agency (EPA).
Chemical category approaches were used for model development and both, molecular descriptors and fingerprints, were calculated to represent the compounds. Afterwards, binary classification models were developed using machine learning methods. The best model had an overall accuracy of 0.851 for the prediction of toxicity on avian species. Furthermore, several representative substructures for characterizing avian toxicity were identified.
In silico prediction of chemical toxicity on avian species using chemical category approaches
Yun Tang, Philip W. Lee
Added on: 02-06-2024
Computational model to study misfolded protein dynamics in the brain
November 2014
Montreal Neurological Institute, Montreal, Canada
The aggregation of misfolded proteins is associated with several neuropathologies. Amyloid-beta is one of them, and its mechanisms of propagation and deposition are not well understood. In this study, a computational approach is used to generate an epidemic spreading model for misfolded protein dynamics that reconstructs individual lifetime intra-brain propagation and the factors that promote it. Using PET amyloid-beta datasets, the model was able to reproduce amyloid-beta deposition patterns in human brains and proposes several mechanisms that explain the amyloid beta-driven onset and progression in Alzheimer's disease, but also its deposition's dynamics in the normal ageing brain. It was also capable of relating the accumulation of amyloid-beta to several other factors and the interactions between them. In summary, this model allows to relate misfolded protein dynamics in the brain with individual risk factors and clinical and demographic data, opening the door to explore the mechanisms of misfolded proteins associated with ageing and neurological pathologies.
Epidemic spreading model to characterize misfolded proteins propagation in aging and associated neurodegenerative disorders
Alan C. Evans, Yasser Iturria-Medina
Added on: 08-08-2021
In-silico assessment of the dynamic effects of drugs on human atrial patho-electrophysiology
November 2014
Institute of Technology (KIT), Karlsruhe, Germany
Atrial fibrillation (AF) is a relevant arrhythmia due to its high prevalence and association with severe complications. Atrial fibrillation is treated with various antiarrhythmic drugs, such as amiodarone and dronedarone. Besides certain differences in the inhibitory effects on ion channels, both drugs differ markedly in their pharmacokinetic properties. In the present study, the researchers aimed at investigating the complex effects of these drugs in an in-silico model of human atrial electrophysiology. The pharmacodynamics of amiodarone and dronedarone were investigated with respect to their dose and heart rate dependence by evaluating 10 descriptors of action potential morphology and conduction properties. An arrhythmia score was computed. The data gives possible explanations for the superior efficacy of amiodarone and may aid in the design of substrate-specific pharmacotherapy for AF.
In-silico assessment of the dynamic effects of amiodarone and dronedarone on human atrial patho-electrophysiology
Axel Loewe
Added on: 12-02-2021
Blood flow dynamics evaluated in patients with Fontan procedure
October 2014
Georgia Institute of Technology & Emory University, Atlanta, USA
The Fontan procedure is the palliative strategy used to treat single ventricle lesions. Operative mortality is low but gradual power loss contributes to some long-term complications. In the present study, the researchers aimed at studying the variability of power loss in treated patients. The researchers made a computational analysis of Fontan connections from cardiac magnetic resonance (CMR) scans of 100 patients. The study showed that power loss varies widely among Fontan patients and may vary with age and development. Also, elevated levels of power loss are correlated with a lower systemic flow and cardiac index but do not depend on the type of Fontan connection. This type of studies allows for an improved evaluation of surgery standards.
Fontan hemodynamics from 100 patient-specific cardiac magnetic resonance studies: a computational fluid dynamics analysis
Ajit P. Yoganathan
Christopher M. Haggerty et al. Journal of Thoracic and Cardiovascular Surgery 2014 [534]
EURL ECVAM [535]
Added on: 11-27-2021
Mathematical analysis of the repertoire of immune cells in healthy donors and autoimmune patients
October 2014
Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
T lymphocytes are the key drivers of the adaptive immune system and detect, combat, and memorize pathogens in all their possible variations. Specific recognition of potentially harmful foreign peptides is achieved by the highly selective binding of T cell receptors (TCRs) to peptide-MHC complexes (p-MHC), mounted on the surface of specialized antigen-presenting cells. Required diversity of recognition arises due to an astronomic number of distinct molecular variants of TCRs. Still, a T cell typically expresses a single TCR variant, and all its daughter cells have identical antigen recognition properties, constituting a clonotype. The clonal structure of the human peripheral T-cell repertoire is shaped by a number of processes and its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. In the present study, the researchers developed a statistical approach to assess T cell clonal size distributions from recent next-generation sequencing data from human donors, 41 healthy individuals and one patient with autoimmune disease. The study shows that the clonal structure is globally the same in the healthy individual but different for the autoimmune patient before therapy and converging towards a typical value after therapy. The data shows the importance of theoretical understanding and mathematical modelling of adaptive immunity.
Assessing T cell clonal size distribution: a non-parametric approach
Mikhail V. Ivanchenko
Added on: 10-27-2021
Mathematical computation of drug targeting model
October 2014
Federal University of Juiz de Fora, Juiz de Fora, Brazil
A mathematical model is used to simulate the effects of the use of the drug Lapatinib in cancer stem cells. The model is a combination of a breast tumor growth model together with a pharmacokinetic model. The obtained results are satisfactory when compared with experimental data, which makes this a potentially useful method to understand drug mechanisms against tumor growth.
Pharmacokinetics simulation of breast cancer
Daniela S Carvalho
Added on: 07-15-2021
Mathematical model of cancer antigen binding
October 2014
National Institutes of Health, Bethesda, USA(1)
Pusan National University, Busan, South Korea(2)
Pusan National University, Busan, South Korea(2)
Immunotoxins and antibody-drug conjugates are designed to bind to specific target antigens on tumor cells and kill these cells. Most cancer-specific antigens are shed from the cell surface without clear knowledge on how this process can affect the delivery efficiency of antibody-drug conjugates and immunotoxins.
In this study, the researchers used a mathematical model to reproduce the kinetic events happening in the blood flow, extracellular space and tumors. The model shows that shedding can reduce, as well as enhance, antitumor activity depending on the number of antigen molecules on the cell surface. It raises the possibility of a new mechanism by which receptor shedding can regulate signaling in normal tissues.
Effect of antigen shedding on targeted delivery of immunotoxins in solid tumors from a mathematical model
Byungkook Lee(1), Youngshang Pak(2)
Added on: 07-27-2021
Virtual training platform for cardiovascular surgery
October 2014
Shanghai Jiao Tong University, Shanghai, China
Training in cardiovascular interventional surgery (CIS) has been mainly performed under fluoroscopic guidance in animals. However, this has drawbacks due to the anatomical differences between animal models and the human body.
In this study, the researchers present a virtual training platform for cardiovascular interventional surgery (CIS) that creates a realistic simulated environment for training basic techniques.
The platform consists of a mechanical manipulation unit, a simulation platform, and a user interface. A decoupled haptic device provides dynamic behaviour and high-quality force feedback. In addition, an efficient physics-based hybrid model is used, which can realistically and interactively simulate the complex behaviour of guidewires and catheters. Three simulation studies show that the platform has reasonable accuracy and robustness. This platform enables realistic simulation of vascular surgery technique testing such as guidewire and catheter simulation and stent placement, as well as rendering techniques.
Cardiovascular-interventional-surgery virtual training platform and its preliminary evaluation
Le Xie
Added on: 10-06-2021
Mathematical model of renal interstitial fibrosis
2014
The Ohio State University, Columbus, USA
Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease that can also affect the kidneys. The most common kidney manifestation of SLE is lupus nephritis (LN). LN occurs when autoantibodies combine with self-antigens to form immune complexes that accumulate in the glomeruli, the filtering units of the kidney, causing inflammation and fibrosis in the tubulointerstitial compartment of the kidney. Unfortunately, inflammation and fibrosis can only be assessed by invasive kidney biopsy. In the present study, the researchers attempted to create a mathematical model for the progression of inflammation to fibrosis. The model was validated by showing that the expression levels of two biomarkers in LN patients at three different stages of chronic kidney injury matched the levels predicted by the model. To demonstrate how the model can be used clinically to diagnose and monitor tubulointerstitial fibrosis, the effect of treatment with anti-inflammatory or anti-fibrosis drugs on the progression of damage was investigated. The data shows that this model can be used to monitor treatments in LN.
Mathematical model of renal interstitial fibrosis
Avner Friedman
Added on: 10-28-2021
Improving breast cancer subtypes diagnosis through bioinformatic analysis
2014
Northwestern University, Evanston, USA
In the present study, the researchers used computational techniques to analyze gene expression patterns from breast cancer patients data which are publically available from The Cancer Genome Atlas (TCGA). Novel signatures associated with transcription factor STAT3 (Signal transducer and activator of transcription 3) were identified and were shown to be specific for basal-like breast cancer and not seen in other subtypes such as luminal A or luminal B cancers. Because STAT3 is known to be important for basal-like breast cancer malignancy, elucidating its most highly affected downstream targets is of great importance to cancer diagnosis and therapy.
Bioinformatic analysis reveals a pattern of STAT3-associated gene expression specific to basal-like breast cancers in human tumors
Curt M. Horvath
Added on: 07-26-2021
Mathematical model for disturbed tau dynamics
2014
University of California Davis, Davis, USA
Tau proteins are critically important in the stabilisation of axonal microtubules and their dysregulation is present in several neurodegenerative diseases such as Alzheimer's disease, tauopathies, and chronic traumatic encephalopathy. However, how the disturbed tau protein dynamics leads to the collapse of axonal microtubules is still not well understood. Here, the researchers provide a simplified mathematical model to study the mechanics of microtubule-tau bundles in neuronal axons where taus are removed. The results show the different conditions of tau binding conditions and the potential role of dynamic instability in microtubule's collapse. The main conclusion of which is that well before dynamic instability is relevant, the mechanical damage done to microtubule bundles at low tau binding rates is already irreversible. Therefore, this could be a very useful model to properly design treatment strategies for tau related pathologies.
Simulated cytoskeletal collapse via Tau degradation
Daniel L Cox
Added on: 08-09-2021
In silico analysis of sequences of prostate cancer marker
2014
University of Beira Interior, Covilha, Portugal
The six transmembrane epithelial antigen of the prostate 1 (STEAP1) is present almost exclusively in prostatic cells; although it was shown to be overexpressed in prostate cancer, its function is not clear. In the present study, the researchers conducted an extensive in silico analysis of STEAP1 and related gene STEAP1B, and evaluate the in vitro STEAP1 and STEAP1B expression in human prostate cell lines. In addition, the putative post-transcriptional and post-translational modifications are evaluated through STEAP1 mRNA and protein stability, supplemented by a post-translational in silico analysis. The data indicate that STEAP1B2 is overexpressed specifically in neoplastic cells, and post-translational modifications may be involved in the regulation of STEAP1 expression in prostate cells.
Expression of STEAP1 and STEAP1B in prostate cell lines, and the putative regulation of STEAP1 by post-transcriptional and post-translational mechanisms
Cláudio J Maia
Added on: 09-18-2021
Mathematical modeling of signaling network response
2014
University of Michigan, Ann Arbor, USA
Modelling signalling networks dynamics is still a challenging task. In this study, a mathematical model is used to predict the dynamics of signalling networks response under unseen perturbations. This algorithm is based on three steps and is validated against simulation and experimental data. Additionally, when compared with other available methods, the computational time is reduced by magnitudes, what potentially will allow the genome-wide modelling of signalling pathways in practical times.
Predicting dynamic signaling network response under unseen perturbations
Yuanfang Guan
Added on: 07-18-2021
Model to predict and stratify Alzheimer's disease patients
2014
CHU de Montpellier and Université Montpellier I, Montpellier, France
Alzheimer's disease is the most prevalent form of dementia. Cerebrospinal fluid biomarkers have been widely used to diagnose and follow the evolution of the disease. However, the way these data are interpreted and how are used to predict the onset or the prognosis of the disease is not clearly established. Therefore, in this study, a biologic scale of probabilities is developed to properly predict and stratify potential patients of Alzheimer's disease. The researchers use cerebrospinal fluid samples from several memory clinics and they use different models combining levels of amyloid-beta 42, tau and phosphorylated tau. A simple model based on numbered classification of the biomarkers is developed and shown to be very efficient to predict and stratify the patients from the memory clinics. This was validated computationally and with an independent dataset from different centres. Overall, here a mathematical predictive model is presented that can help diagnose patients of Alzheimer's disease in the very early stages of the disease, or even before the onset, and correctly stratify them for treatment or clinical research purposes.
A diagnostic scale for Alzheimer’s disease based on cerebrospinal fluid biomarker profiles
Sylvain Lehmann
Added on: 08-29-2021
Mathematical model for alpha-synuclein molecular dynamics
2014
National and Kapodistrian University of Athens, Athens, Greece
Alpha-synuclein is involved in several pathologies and has been described to have a central role in Parkinson's disease. The profiles of expression of alpha-synuclein have been suggested to be correlated with familial and sporadic forms of the disease and result in the aggregation of fibrils in the form of Lewy bodies in neurons. However, there is a lack of understanding of the molecular mechanisms of alpha-synuclein that can lead to the prevention or to a potential cure of Parkinson's disease. Here, a mathematical biomolecular reactions model is developed to describe the molecular dynamics of intracellular alpha-synuclein. Furthermore, experimental data of alpha-synuclein overexpression is obtained using a human neuroblastoma cell line to validate the simulated data generated with this model. The results show that in three hypothetical intervention scenarios the model is capable of simulating the cell viability outcome that fits with experimental data. This new model allows predicting alpha-synuclein dynamics in newly generated scenarios to estimate the underlying mechanisms that lead to proteolysis deregulation. This opens new possibilities in the study of alpha-synuclein and provides researchers with a powerful tool to test hypotheses prior to experimental tests.
In silico modeling of the effects of alpha-synuclein oligomerization on dopaminergic neuronal homeostasis
Elias S Manolakos
Added on: 09-23-2021
Computational deconvolution of tumor sequencing data
2014
University of California, Irvine, USA
Two types of sequencing information are combined to improve the identifiability of reads associated with certain tumour cells or subclonal types. This method, which is available in a Python package named PyLOH, is able to outperform existing methods when used in both simulated data and in real breast tumour datasets. Therefore, it can be a useful tool to determine the subclassification of sequencing reads in mixed tumour cell populations.
Deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygosity
Xiaohui Xie
Added on: 07-28-2021
Mathemathical models for metastasis prediction
2014
Ondokuz Mayis University School of Medicine, Samsun, Turkey
The study presents the building of a new nomogram for predicting non-sentinel lymph node metastasis in sentinel lymph node patients with invasive human breast cancer to try to overcome the need of performing axillary lymph node dissections. The nomogram was built taking into account several independent predictive factors and, together with other selected models, they showed excellent discrimination capacities. After testing several models in 237 patients, the researchers found several properties of breast tumours that can be predictive factors. Here, a new nomogram is presented that could be used to predict the likelihood of non-sentinel lymph node metastasis.
A breast cancer nomogram for prediction of non-sentinel node metastasis - validation of fourteen existing models
Bekir Kuru
Added on: 07-30-2021
Computational model of dopaminergic neurons
November 2013
University of Tuebingen, Tübingen, Germany
Parkinson's disease is a devastating neurodegenerative disorder that is characterized by a progressive death of dopaminergic neurons that leads to motor and cognitive deficits and, ultimately, death. Despite having identified several pathological features of the disease, the mechanisms that cause the onset remains unknown. Here, the researchers propose a computational model of dopaminergic neurons based on several sub-models that include cellular processes involved in the homeostatic and pathological mechanisms present in Parkinson's disease. The model was investigated to find steady model states through the modulation of several experiments. The results show that this model can predict neuronal outcomes that fit previous observations and can be used to study how the manipulation of different cellular processes affect the pathological outcome in the cell. This study provides an in silico platform to simulate the behaviour of dopaminergic neurons and predict the outcome of potential dysregulations that are present in pathologies such as Parkinson's disease.
Parkinson’s disease: dopaminergic nerve cell model is consistent with experimental finding of increased extracellular transport of α-synuclein
Finja Büchel
Added on: 09-30-2021
A DEP-array allows direct measurement of immune lysis
2013
Regina Elena National Cancer Institute, Immunology Laboratory, Rome, Italy
DEP arrays - chip platforms based on dielectrophoresis (DEP) - have the advantage that they allow direct measurement of cell lysis by identifying individual cells and capturing them in DEP "cages". "Cages" and their contents can be moved within the chip to any location on its surface. In this way, controlled and forced interactions between cells can be induced and infected cells and tumors can be detected. Here, DEP-based manipulations of human single CTL (Cytotoxic Lymphocyte) and NK (Natural Killer) cells as well as controlled and forced interactions were performed to understand and control lytic interactions. Specific lysis could be measured in real time and target cells with different susceptibility to immunolysis were identified. Applications are foreseeable in human immunology, including antiviral surveillance and tumor immunotherapy.
Lysis-on-Chip of single target cells following forced interaction with CTLs or NK cells on a dielectrophoresis-based array
Patrizio Giacomini
Added on: 05-27-2020
In silico correction of an arrhythmia model based on human induced pluripotent stem cells
2013
University at Buffalo, Buffalo, USA
Despite the potential of cardiac myocytes derived from induced pluripotent stem cells (iPSCs), problems with this system have been noted, leading to serious concerns about their use in studying arrhythmogenic mechanisms and drug safety screening. Action potentials (APs) from hiPSC-derived cardiocytes are often referred to as an “immature phenotype". In the present study, the researchers aimed at complementing the use of iPSCs with an in silico approach to rectify the immature phenotype. Electrophysiology was performed on iPSCs derived cardiomyocytes treated with different drugs in vitro, the data was then processed using an in silico platform to turn the immature electric activity into a mature one and obtain expected phenotypes. The study concludes that this in silico platform is the appropriate complement of hiPSC-derived cardiac myocytes to be used for arrhythmia modelling.
Electronic "expression" of the inward rectifier in cardiocytes derived from human-induced pluripotent stem cells
Randall L Rasmusson, Glenna C L Bett
Added on: 11-29-2021
Targeting breast cancer stem cells with ellipticine
2013
National Institute of Pathology, New Delhi, India
Breast cancer stem cells are surging as a potential target to disrupt cancer progression and avoid further relapses. Here, the effects of ellipticine on ALDH1A1-expressing human breast cancer stem cells are studied both in in vitro and in silico setups. The results show that at concentrations of 3mM it was able to significantly decrease the ALDH1A1+ cancer stem cells in two different human breast cancer cell lines. Contrary to paclitaxel, ellipticine also reduced mammosphere formation but, when both agents were combined, there was an enormous drop of ALDH1A1+ cancer stem cells. The in silico model revealed that several residues of ALDH1A1 were potentially interacting with ellipticine, confirming the potential interactions of the drug with this protein. In this study, the researchers demonstrate that ellipticine can disrupt the proliferation abilities of ALDH1A1+ breast cancer stem cells and can be combined with cytotoxic therapeutical agents to efficiently target them.
Effects of ellipticine on ALDH1A1-expressing breast cancer stem cells—an in vitro and in silico study
Sunita Saxena
Added on: 10-14-2021
Computational tools to study human Superoxide Dismutase 2
2013
Federal University of Rio de Janeiro State, Rio de Janeiro, Brazil
Polymorphisms of the superoxide dismutase 2 gene have been related to the development of neurological disorders. In this study, all the known human variants of this gene were analysed with different algorithms. With this analysis and with well-fitted structural theoretical models, it was possible to see that all mutations lead to the pathogenicity of the protein. In the end, all this data, together with a phylogenic analysis, were included in a freely accessible database that can be used by biologists and clinicians. This will allow to further explore the outcomes of the mutations of the superoxide dismutase 2 gene in pathological processes in humans.
Structural modeling and in silico analysis of human Superoxide Dismutase 2
Joelma Freire De Mesquita
Added on: 08-06-2021
Advanced tests for skin and respiratory sensitization assessment
2013
Center for Alternatives to Animal Testing (CAAT)-Europe, University of Konstanz, Konstanz, Germany
Sens-it-iv is an EU-funded project that finished in March 2011 after 66 months of activity. The ultimate goal of the project was the development of a set of in vitro methods for the assessment of the skin and respiratory sensitization potential of chemicals and proteins. At the end of the project it can be concluded that the goal has been largely accomplished. This work presents a list of methods that are ready for skin sensitization hazard assessment. Potency evaluation and the possibility of distinguishing skin from respiratory sensitizers are also well advanced.
Advanced tests for skin and respiratory sensitization assessment
Costanza Rovida
Added on: 05-25-2020
In silico assessment of sodium blockers' drug safety in human heart
2013
Universitat Politècnica de València, Valencia, Spain
Anti-arrhythmic drugs mainly act on ionic currents but there is still a gap in the understanding of these currents to be able to clearly assess the potential benefits and risks of drugs in development. In the present study, the researchers aimed at gaining detailed knowledge by developing an in-silico tool for preclinical anti-arrhythmic drug safety assessment. The researchers based their work on a well-referenced model, the O’Hara et al., for human ventricular myocytes. Biomarkers for arrhythmic risk were calculated using single myocyte and one-dimensional strand simulations. Predetermined amounts of blockage of the two main ionic currents INaL and IKr were evaluated. “Safety plots” were developed to illustrate the value of the specific biomarker for selected combinations of IC50s for IKr and INaL of potential drugs. Two anti-arrhythmic drugs, Ranolazine and GS967 (a novel potent inhibitor of INaL) generated a biomarker data set that is considered safe by regulatory criteria. The study describes a novel in-silico approach to evaluate the potential anti and pro-arrhythmic properties of drugs.
In silico assessment of drug safety in human heart applied to late sodium current blockers
Beatriz Trenor
Added on: 12-03-2021
Digital model of epicardial pacing for training of physicians
2013
University of Kentucky Medical Center, Lexington, USA
Improvement of postoperative management of patients having cardiac surgery could be obtained with increased understanding of epicardial pacing, In the present study, the researchers aimed at developing a software-based epicardial pacing program to be used with the existing patient simulator. These digital training tools should assist junior physicians to become competent in the management of epicardial pacemakers. The tools developed were based on a human patient simulator and Flash animation to present arrhythmias and epicardial pacing interventions. The trainee is able to make adjustments (type of pacing, rate of pacing, and chamber output) as if using an actual pulse generator. The study reports the training of 15 anesthesiology residents who had minimal epicardial pacing experience. An anonymous post-scenario questionnaire was used to receive feedback which showed that the simulation session improved their understanding and management of epicardial pacemakers.
Using software-based simulation for resident physician training in the management of temporary pacemakers
Zaki-Udin
Added on: 12-02-2021
Computational approach to model estrogen regulation of amyloid beta production
2013
University of Illinois at Urbana-Champaign, Urbana, USA
Amyloid-beta accumulation in Alzheimer's disease is a complex process that is not well understood. Despite this, estrogen is known to influence the regulation of its production. Here, a computational approach is used to model the complex contribution of estrogen to amyloid-beta regulation. The results show that by using estrogen it is possible to induce the reduction of amyloid-beta levels. The model also shows the mechanisms behind this potential reduction and that the use of non-steroidal anti-inflammatory drugs could be used as an additional treatment. It also describes a series of other compounds that could be also used synergistically with estrogen to further decrease the amyloid-beta levels. The results show that this model could be used as a starting point for drug development and to understand the mechanisms behind them.
Exploring the contribution of estrogen to amyloid-beta regulation: a novel multifactorial computational modeling approach
Thomas J Anastasio
Added on: 08-08-2021
Mathematical modeling of reciprocal modulated reactions of the tau protein
Company 2013
EnVivo Pharmaceuticals, Watertown, USA
Hyperphosphorylation of tau protein is implicated in several neurodegenerative diseases. One of the approaches to tackle this process is to increase the levels of tau O-GlcNAcylation, as they are reciprocally regulated. Therefore, some therapeutical strategies are based on the inhibition of O-GlcNAcase. In this study, mathematical models are developed to analyse the dynamics of phosphorylation and O-GlcNAcylation of tau protein during O-GlcNAcase inhibition. The predictions show an increase of O-GlcNAcylated tau proportional to the inhibition levels and a non-dependent variable decrease of phosphorylated forms. This reduction in phosphorylated tau happens in short-term inhibitory scenarios and it is expected to return to its initial values under sustained inhibition, while O-GlcNAcylated proteins achieve higher steady levels. Furthermore, inhibition arrest is predicted to cause a temporal increase in phosphorylated tau levels. This model deciphers complex mechanisms in phosphorylation and O-GlcNAcylation co-regulation in different inhibitory strategies and can be a useful tool to design pharmacological interventions in scenarios where tau phosphorylation is a major pathological process.
A dynamic view to the modulation of phosphorylation and O-GlcNAcylation by inhibition of O-GlcNAcase
Cuyue Tang
Added on: 08-16-2021
Design of optimised beta secretase inhibitors
2013
University of Leeds, Leeds, United Kingdom
Amyloid-beta accumulation is one of the major Alzheimer's disease pathological processes. The first step in the production of this peptide is the cleavage of amyloid precursor protein by beta-secretase. Therefore, this secretase is an attractive target for inhibition strategies to tackle the progression of the disease, avoiding the accumulation of amyloid-beta. Here, a computational approach is used to design several nonpeptide beta-secretase inhibitors based on a biphenylacetamide scaffold. A new library of optimised ligands was generated and the newly designed molecules had more than a 10-fold higher binding affinity than their scaffold, as suggested by their IC50. Afterwards, a final selection was done based on a cytotoxicity "in vitro" assay with an immortalised human cell line, which revealed that one of the newly designed compounds had minimal cellular toxicity. This study presents a methodology that can be used for the design and initial screening of optimised compounds to modulate the activity of therapeutic targets.
Discovery of biphenylacetamide-derived inhibitors of BACE1 using de novo structure-based molecular design
Colin W G Fishwick, A Peter Johnson, Nigel M Hooper
Added on: 08-16-2021
Tomographic technique applied to breast cancer diagnosis
2013
Universidade de São Paulo, Ribeirão Preto, Brazil
A computational study to test whether topographic Rayleigh to Compton scattering ratio can be used in human breast cancer diagnosis. Several parameters are studied to evaluate the technique, showing that the contrast of the images depends on how they are adjusted. Also, the statistical noise was important, but without concerning influence in the final quality of the images. Overall, the results show that this technique could, effectively, be used s a complementary tool in breast cancer diagnostics.
Rayleigh to Compton ratio scatter tomography applied to breast cancer diagnosis: a preliminary computational study
Marcelo Antoniassi
Added on: 07-30-2021
Simulation of the fibril formation typical for Alzheimer's disease
December 2012
University of California Irvine, Irvine, USA
Amyloid-beta aggregation and fibril formation are some of the hallmarks of Alzheimer's disease, but the mechanism that connects these phenomena with the onset of the disease is not yet clear. Before, it has been proposed that the formation of these fibrils follows a dock/lock mechanism. Therefore, in this study, the researchers use a simulation of two-dimensional ultraviolet spectroscopy to confirm this hypothesis. The signals generated can be used to monitor local dynamics and conformational changes in the secondary structure of amyloid-beta peptides, showing that these are in agreement with a dock/lock pathway. The results confirm that this method can be used to further explore the dynamics of protein aggregation.
Tracking the mechanism of fibril assembly by simulated two-dimensional ultraviolet spectroscopy
Alfonso R Lam
Added on: 08-06-2021
Alzheimer's disease prediction model
November 2012
Radboud University Nijmegen, Nijmegen, Netherlands
Alzheimer's disease is one of the most prevalent neurodegenerative disorders. However, the available diagnostic tools are not efficient and there are severe limitations to predict the occurrence and the onset of the disease. Here, a prediction model is presented to estimate the probability of developing Alzheimer's disease based on amyloid-beta 42 and phosphorylated tau levels in cerebrospinal fluid together with patients' sex. The logistic regression analysis gives an estimation to calculate the probability of developing Alzheimer's disease and when this is applied to the validation data set, has a powerful discriminative ability. The researchers present, and validate, a prediction model that has the potential to be applied in memory clinics to assess the probability of patients developing Alzheimer's disease based on commonly used biomarkers.
A prediction model to calculate probability of Alzheimer’s disease using cerebrospinal fluid biomarkers
Petra E Spies
Added on: 08-28-2021
Mathematical model of amyloid beta dynamics in the presence of gamma-secretase inhibitors
CompanyNovember 2012
AstraZeneca, Macclesfield, United Kingdom
The mechanisms of amyloid-beta accumulation in Alzheimer's disease are still not well-known. One unanswered question is the rise of amyloid-beta levels after treatment with gamma secretase inhibitors in some cell lines. In this study, a mathematical model is proposed to quantitatively describe the dynamics of amyloid-beta in cell lines that undergo this phenomenon compared to those that do not. The results show that the changes in the dynamics of the amyloidogenic and non-amyloidogenic pathways are driven by the accumulation of C-terminal fragment 99 of the amyloid precursor protein. Also, the model is able to reproduce the amyloid-beta profiles of humans treated with gamma secretase inhibitors. Overall, this study proposes an effective mathematical model that can be used to develop new therapeutics that target amyloid-beta production in Alzheimer's disease.
Interplay between α-, β-, and γ-secretases determines biphasic amyloid-β protein level in the presence of a γ-secretase inhibitor
Claus Bendtsen
Added on: 08-08-2021
3D visualization of the brain using multiscan technology
CompanyEaglescience Software B.V., Amsterdam, Netherlands
Eaglescience B.V. works in collaboration with various companies and medical research centres in the development of AI-supported software (Neurostars) that enables personalized 3D visualization of the brain. By combining different neuroimaging and scanning technologies and using deep learning algorithms, the relevant tissue types (e.g. brain, veins, tumor) and patterns can be displayed in a differentiated manner. Using an easy-to-use software tool, the diverse information is synchronized and converted into a three-dimensional brain model of the patient in a virtual environment. The program uses additional lighting techniques to create shadows and reflections to enable realistic depth vision. This allows neurosurgeons to plan their procedures precisely and also train in a virtual reality environment. Furthermore, Neurostars improves doctor-patient communication by facilitating the shared decision-making process and patient approval for a recommended operation. By integrating the software platform into teaching, the program can help improve the training of brain surgeons and students. In summary, the Neurostars project can help advance neuroscience research, as well as create new opportunities to improve diagnosis and personalized treatment of patients with neurological diseases.
Neurostars. Virtuele 3D visualisatie van de hersenen
info@eaglescience.nl
Added on: 04-09-2024
Acquisition of basic surgical techniques with the box trainer
CompanySurgical Science, Gothenburg, Sweden
Box trainers offer prospective surgeons to train with their preferred surgical instruments and get a sense of the movements, limited space awareness and reduced tactile feedback that constitute some of the characteristics of laparoscopy. A high definition camera and advanced instrument-tracking provides objective feedback and is crucial to learn and implement safe and efficient techniques. Basic skills and basic surturing modules are included as well as video tutorials and metrics to follow one's progress in performance.
SimBallBox
support@surgicalscience.com
Added on: 12-18-2020
AI and OMICs-supported drug pipeline to develop personalized therapies
CompanyRelation Therapeutics, London, United Kingdom
To accelerate the development of (personalized) therapeutic approaches against bone diseases, Relation Therapeutics' lab-in-the-loop approach combines functional single-cell analysis, patient-specific OMICs datasets (generated directly from patient tissue samples) and machine learning in an AI-powered drug pipeline. The AI assesses the multimodal patient data using the company's Osteomics bone atlas, which brings together huge, global datasets of osteoporosis patients. The platform enables effective screening for disease-associated biomarkers, drug targets and potential drug candidates. In this way, the method can help in various studies to generate new knowledge about certain diseases, develop new treatment strategies or optimize existing therapeutic approaches in order to reduce drug-induced side effects. The approach can be expanded to other clinical pictures. The company is currently working on developing new programs to help advance drug discovery in the areas of immunology and metabolic diseases.
Our pipeline. Driving significant value for patients.
enquiry@relationrx.com
Added on: 04-11-2024
AI for aquatic toxicity testing
CompanySmarter Sorting, Austin, USA
Since some of our everyday products like cosmetics or pharmaceuticals are toxic to aquatic animals, when they get washed into the water system, they can pose a serious threat to wildlife. Thus, companies need to test whether their products are toxic—and in some cases, doing so requires testing them on animals, often by putting a certain amount of product into a tank of fish and waiting to see how many of them die. With data from prior toxicity tests, intelligence company Smarter Sorting has been able to conduct its own accurate toxicity tests without killing any animals. Instead of dumping chemicals into tanks, it uses AI and machine learning to compute all the necessary calculations on the toxicity of products companies are developing. Its AI aiming to eliminate unnecessary animal testing is the winner of the AI and data category of Fast Company’s 2022 World Changing Ideas Awards.
support@smartersorting.com
Added on: 06-09-2022
AI predicts the probability of success of clinical trials
CompanyInsilico Medicine, Pak Shek Kok, Hong Kong SAR of China
To optimize drug studies in the clinical phase, Insilico Medicine has developed the AI-based platform inClinico. To predict the feasibility and probability of success (PoS) of individual clinical studies, the AI relies on a huge data set that, in addition to relevant OMICs data on genomics, epigenomics, transcriptomics and proteomics, includes all published information and findings from past and current (pre-)clinical studies taken into account. The company uses specially developed algorithms to integrate the study-specific data. This enables the platform to carry out tailor-made studies, from planning and creating the study design to recruiting and selecting participating patients and evaluating the results. Potential drug candidates can be identified quickly and reliably and prioritized for further study. In summary, inClinico shows promise as a method for accelerating clinical drug trials that can help improve risk management and manage resources more efficiently.
inClinico. Clinical risk assessment and portfolio triage
info@insilicomedicine.com
Added on: 12-01-2023
AI-assisted (epi-)genetic screening platforms for toxicology and efficacy studies
CompanyToxGenSolutions B.V., Maastricht, Netherlands
The company ToxGenSolutions specializes in identifying drug targets and developing new drugs. The company's goal is to detect severe diseases (such as neurodegenerative diseases, cancer, (auto-)immune deficiencies) at an early stage and stop them in their tracks. Based on (epi)genetic data sets, computer tools identify potential drug candidates. For toxicity and efficacy evaluation, human spheroids are exposed to the active ingredient to be tested and subjected to high-throughput screening. The AI-based methods accelerate previous complex and time-consuming testing procedures. They also open up the possibility of developing personalized medicines. In addition to processes for developing and testing the safety and efficacy of new active ingredients, the company also develops various methodological tools to optimize early diagnostics. Currently, ToxGenSolutions is working to validate a diagnostic tool that will enable preclinical diagnosis of Alzheimer's disease with a focus on differences between men and women.
ToxGenSolutions
erwin.roggen@toxgensolutions.eu
Added on: 11-22-2023
AI-assisted assay to evaluate the dose-response mechanism of skin sensitizers
CompanySenzaGen, Lund, Sweden
The company SenzaGen specializes in the development of AI-based test methods for identifying and evaluating skin allergens. The GARD®skin Dose-Response Platform is a modified variant of the GARD®skin assay, which distinguishes sensitizing and non-sensitizing substances with over 90% accuracy and subcategorizes them into strong and weak sensitizers (1A and 1B) according to the GHS/ CLP system enables. To predict the biochemical properties of a substance, a machine learning platform generates a biomarker signature of an exposed test sample, which is compared to existing data sets. The GARD®skin Dose-Response Assay not only enables the hazard classification of an active substance, but also evaluates the potency of the test substance; i.e. it determines the minimum quantitative active ingredient concentration that is sufficient to induce an allergic reaction in the skin and thereby enables an early ranking of potential active ingredient candidates. The method delivers meaningful results within a period of 4–8 weeks that correlate with the measured values of previous established methods. In summary, the modified assay proves to be a valuable tool for the predictive evaluation of chemicals and other substances to be tested, which can help to optimize risk management in early drug and medical device development and to accelerate previously time-consuming testing procedures.
GARD®skin Dose-Response. In vitro quantitative assessment of skin sensitizing potency
info@senzagen.com
Added on: 11-21-2023
AI-assisted skin sensitization assay to predict allergenic agents and chemicals
Validated Method CompanySenzaGen, Lund, Sweden
GARD®skin is an in vitro test method for identifying and evaluating active ingredients and chemicals that trigger allergic reactions in the skin. For this purpose, the test substances are brought into contact with a human dendritic cell line (SenzaCell™), which simulates a critical part of the immune system. A machine learning platform then analyses the gene expression pattern of the exposed sample and compares it with existing data sets that include 196 biomarkers associated with known skin allergens. The GARD®skin Assay distinguishes between sensitizing and non-sensitizing substances with over 90% accuracy. The method can be used in a variety of different test procedures. The GARD®skin technology also enables the analysis of “difficult to test” samples, such as complex mixtures, indirectly acting haptens, lipophilic compounds, metals, metal salts, solid materials and surfactants. In summary, SenzaGen's skin sensitization assay proves to be a valuable and innovative method for evaluating skin sensitizers and for identifying new relevant biomarkers. The integrated AI-based learning platform generates meaningful results for researchers within a period of 4–8 weeks, significantly reducing the duration of previous test procedures and studies for safety testing of active ingredients and chemicals to be tested. Validated and regulatory approved under OECD test no. 442E.
GARD®skin. OECD TG 442E: in vitro skin sensitization
info@senzagen.com
Added on: 11-15-2023
AI-based platform for the development of cancer therapeutics
CompanyBioCopy AG, Basel, Switzerland
The company BioCopy specializes in the development of cancer therapeutics using AI-supported methods. The drug discovery platform enables effective screening for drug candidates that are able to connect the tumor cells with the body’s own immune cells by recognizing specific surface markers. The binding leads to the immune cell specifically killing the cancer cell without damaging the surrounding healthy cells. To engineer the highly complex antibody therapeutics, various technologies are combined in an automated process. The method shows high time and cost savings compared to conventional processes and is able to provide high-quality active ingredients on a large scale for industry. To date, the company has developed three drug development programs in solid tumors (bladder cancer, ovarian cancer, lung cancer) and two in blood cancers (acute myeloid lymphoma). In the future, BioCopy would like to expand the platform for screening drugs against neurological and immunological diseases. In summary, the method can help to accelerate drug development and optimize existing drug therapies.
We revolutionize the development of next-generation cancer drug candidates
info@biocopy.com
Added on: 04-23-2024
AI-based protein design tool in an app format
CompanyExazyme, Berlin, Germany
To predict protein evolution, the start-up company Exazyme has developed an AI-based protein design tool in an easy-to-use app format. First, a starting data set is uploaded to the app. An algorithm checks whether the data is suitable for further processing. The search or design request can then be individually configured to suit the research interest. The app enables, among other things, random mutations, digital deep mutation scans or a selection based on a fixed list of candidates. Within a few minutes, the AI provides sequence predictions for the desired properties of the protein and information on protein quality, which can then be tested immediately in the wet laboratory. To optimize the artificial enzymes, subsequent data sets can be uploaded after the tests. The program is suitable for a variety of different protein modifications such as: increasing protein catalysis and enzyme activity (for faster biochemical reactions), improving protein stability in different solvents and at certain temperatures (to improve storage, transport and distribution) or increasing the Protein affinity (for improved protein-protein interaction).
Artificial intelligence. Human ingenuity.
www.exazyme.com
Added on: 10-04-2023
AI-based test method for hazard classification of skin sensitizers
CompanySenzaGen, Lund, Sweden
The GARDpotency is an AI-based assay for the differential assessment of skin sensitizers, which was developed as an additional test method to the GARDskin Assay. The GARD®skin technology is based on a gene expression analysis of a human dendritic cell line (SenzaCell™), which can distinguish allergenic and non-allergenic active ingredients and chemicals with over 90% accuracy. Skin allergens validated by the GARDskin (OECD TG 442E) can now be additionally divided into strong (1A) and weak (1B) sensitizers using the GARDpotency. For this purpose, a machine learning platform compares the gene expression pattern of the test sample with 51 relevant biomarkers. Meaningful results are generated within a period of 4–8 weeks. The test procedure is part of the OECD test guidelines program (TGP 4.106) and is accepted by the European Chemicals Agency (ECHA) for the hazard classification of skin allergens according to the GHS/CLP system. In summary, GARDpotency proves to be an innovative method that can help to optimize and accelerate current testing and safety procedures for active ingredients and other chemicals.
GARD®potency. Skin sensitizing potency classification according to GHS/CLP
info@senzagen.com
Added on: 11-15-2023
AI-based test method for identifying respiratory sensitizing agents
CompanySenzaGen, Lund, Sweden
To identify respiratory sensitizers during the efficacy and safety testing of new medications, the company SenzaGen has developed the AI-based test procedure GARD®air. At the beginning, human dendritic cells, which simulate part of the critical immune system, are brought into contact with the active ingredient to be examined. A machine learning platform then analyses the gene expression pattern of the test sample and compares it to 28 biomarkers associated with allergic respiratory reactions. The method can reliably distinguish inhalation allergens from non-sensitizers with a specificity of 95% and therefore proves to be a valuable test method for identifying allergenic active ingredients early in drug development and rejecting them as potential candidates. At the same time, the assay prevents incorrect evaluation of non-sensitizing active ingredients. GARD®air delivers meaningful results within 4–8 weeks and can thus help to accelerate the development and approval of new drugs.
GARD®air. A predictive test for chemical respiratory sensitizers
info@senzagen.com
Added on: 11-21-2023
AI-powered image analysis platform for early risk assessment of breast cancer patients
CompanyOwkin, Boston, USA
The company Owkin has developed the AI-supported image analysis platform RlapsRisk® BC for an early assessment of the risk of recurrence in breast cancer patients. The method is suitable for adults who have been diagnosed with primary invasive breast cancer (ER+/HER2-). To assess the risk, surgically removed patient tumor tissue samples are examined on digitized slides. Artificial intelligence analyses patterns and characteristics of the tumor and compares them with a clinical data set that includes data from 1,800 breast cancer patients (including 1,480 HER2-/HR+). A machine learning system automatically integrates new insights, meaning the platform is in continuous training to improve the accuracy of its predictions. So far it has been shown that the platform has a cumulative sensitivity of 76% and therefore correctly diagnoses more high-risk patients as positive than the clinical score. After five years, the diagnostic procedure achieved a dynamic specificity of 76%. In summary, the RlapsRisk® BC platform can help doctors better assess the risk of their patients in order to decide on an appropriate form of therapy as early as possible. Furthermore, the researchers hope that the method will provide new insights and an improved understanding of the mechanisms of highly aggressive tumors in the future.
RlapsRisk® BC. Assess the risk of breast cancer relapse.
www.owkin.com
Added on: 01-18-2024
AI-powered platform to develop personalized medicines for chronic skin diseases
CompanyIntegraSkin GmbH, Ihlow, Germany
The company IntegraSkin specializes in the development of personalized medications for chronic skin diseases (CISCs). To screen for effective drug candidates, the company has developed an AI-supported platform that compares multimodal patient data with a large-scale OMICs database for its analyses. To develop a personalized treatment strategy, the AI examines, among other things, tissue samples from diseased areas of the patient's skin and compares them with skin samples from unaffected areas. The patient's individual lifestyle and diet are also taken into account. Specially developed algorithms enable accelerated and safe screening for disease-associated patterns and biomarkers, potential drug candidates and their possible targets. In summary, the platform offers doctors and researchers a comprehensive diagnostic and analysis tool for more in-depth, general or individual research into CISCs. The method offers high potential for generating new insights and can thereby help accelerate drug development and optimize existing non-specific therapeutic approaches. The company makes its databases available to researchers and also offers services for those affected.
One step ahead in AI for personalized medicine.
www.integraskin.de
Added on: 04-30-2024
AI-supported development of universal personalized TCR immunotherapies against cancer
CompanyTcelltech GmbH, Mannheim, Germany
Tcelltech GmbH specializes in the development and production of TCR immunotherapy technologies. In collaboration with the German Cancer Research Center (DKFZ), the company has developed the personalized adoptive cell therapy UNIPACT, which is intended to enable universal control of all types of cancer. UNIPACT is based on two different platforms. The AI-based bioinformatics platform selectTCR enables the identification of reactive T cell receptors (TCRs) that recognize and specifically target tumor cells within a few days. Because the TCRs come directly from patients, they do not cause off-target toxicity and can be immediately used to produce modified T cells. SelectTCR's proof-of-concept has been carried out in previous studies in melanoma, colon, pancreatic, lung and brain cancer. The company has developed the high-performance DNA vector platform nanoSMAR to load the patient's own T cells with the selected TCRs. nanoSMAR enables safe and efficient gene expression without damaging cells or activating the immune system. The technology allows long-term gene expression with large genetic capacity. In summary, UNIPACT proves to be a groundbreaking approach that accelerates the development of personalized cancer therapies and can help increase patients' chances of survival and reduce undesirable therapeutic side effects.
Pioneering antigen-agnostic TCR T cell therapies
info@tcelltech.de
Added on: 04-02-2024
AI-supported diagnostics and prognosis platform for assessing the risk of (intestinal) cancer patients
CompanyOwkin, Boston, USA
To classify and assess the risk of colorectal cancer, the company Owkin has developed the AI-supported diagnostic tool MSIntuit® CRC. The method enables digital pre-screening of surgically removed tumor tissue samples for the presence (or absence) of genetic microsatellite instability (MSI). Approximately 15% of the entire colon cancer population has MSI, of which approximately 20% are affected by hereditary Lynch syndrome (HNPCC), which is why MSI is considered a valuable biomarker for the diagnosis of colorectal cancer (CRC) and for predicting the course of the disease. So far it has been shown that the AI predictions have a sensitivity of 95%. MSI pre-screening also plays an important role in determining the treatment decision. Colorectal cancer patients with MSI show better prognoses and do not benefit from chemotherapy in stage II. In addition, the screening platform may also be relevant for other tumor diseases, as therapy with immune checkpoint inhibitors (ICI) is particularly suitable for MSI patients (regardless of the type of cancer). Approved or recommended MSI screening technologies currently used by Owkin include MMR-IHC staining, MSI-PCR testing and next-generation sequencing (NGS). In summary, the MSIntuit® CRC test procedure can help to predict the risk and progression of (intestinal) cancer in order to make an appropriate therapy selection for the patient at an early stage and help to identify familial risks.
MSIntuit® CRC. Optimize MSI testing for colorectal cancer.
www.owkin.com
Added on: 01-25-2024
AI-supported machine learning system to optimize cancer imaging diagnostics
CompanyKeyZell, Sevilla, Spain
To optimize cancer imaging diagnostics, the biotechnology company KeyZell has developed the AI-based Oncology Precision System (O.P.S.) in collaboration with One Technology. O.P.S is a machine learning system trained on 108,948 chest X-ray images from over 30,000 patients and is made available to physicians in the form of a Software as a Service (SaaS) tool. To validate the method, patterns reported to the AI were compared with patterns rated as abnormal by the radiologist. Currently, the diagnostic platform includes up to 112 biomarkers and enables assessment of oncological status in less than a minute with an efficiency of 89%. KeyZell has completed training for the lung and breast cancer prototype and is currently training a module for AI-assisted diagnosis of colorectal cancer. In summary, O.P.S. as an effective diagnostic tool that supports physicians in their clinical decision-making and the development of a personalized treatment strategy.
A.I. Diagnosis
www.keyzell.com
Added on: 02-15-2024
Anesthesia training on realistic models
CompanySimulab, Seattle, USA
The trainer consists of a body with replaceable tissue. Different regions are available like upper body with a movable head or a femoral trainer. It can be connected with any PC to provide needle-to-nerve visual and audio verification of proper technique. Physicians can use this training device to gain procedural accuracy and enhance the training experience during interscalene and supraclavicular nerve block skill development. A signal pops up when the needle enters into the neural sheath. A simulated arterial pulse can cause bleeding when needles are inserted inadequately.
Regional Anesthesia Trainer with SmarTissue
www.simulab.com
Added on: 12-18-2020
App with a 3D representation of human anatomy
CompanyVisible Body, Boston, USA
The app contains the complete male and female anatomy. Adjustable are body regions, systems, cross-sections (optionally from MRI scans or cadavers), animated representations of muscle actions and fine representations of individual organs. The anatomical structures can be successively removed layer by layer. Pathological conditions such as dislocated joints can also be represented anatomically. Augmented reality allows the user to virtually move around the object and enables different viewing directions.
Interaktive 3D-Modelle der menschlichen Anatomie
www.visiblebody.com
Added on: 06-23-2020
Artificial intelligence to tackle food allergies
CompanyUkko Inc., Tel Aviv, Israel
The aim of the project is to apply machine learning technologies in immunology, computational biology, and protein engineering to develop new approaches to treating food sensitivities. Currently, a map of the molecular structure of food allergies and disorders is constructed. It will enable the engineering of food proteins with eliminated allergenicity while keeping “good” biochemical and nutritional characteristics intact. For this purpose, millions of antibodies from the blood of people with allergies like peanut allergy or gluten sensitivity are analysed. This data is then fed to the algorithms to predict what exactly is triggering the allergic attack by the immune system. When the triggers are identified, algorithms are used to search for ways to minimally alter these elements in a way that will prevent the binding by immune cells but will maintain the structure, function, and overall traits. This offers new possibilities for allergy treatment, drug development and therapy.
www.ukko.us
Added on: 02-04-2021
Automated development of personalized T cell therapies
CompanyActiTrexx GmbH, Mainz, Germany
To prevent and treat rejection reactions in stem cell transplants, the company ActiTrexx has developed an automated process for the development of personalized immune therapeutics (actileucel therapy). The development of graft-versus-host disease (GvHD) is primarily induced by activation of the donor's CD4+ T lymphocytes. The cellular therapeutic agent actileucel contains modified regulatory T cells that prevent this activation and can thereby prevent the development of GVHD. At the same time, they help to strengthen the patient's own immune system. By automating the various work steps (cleaning and preparation of donor leukapheresis, selection and activation of suitable T cells), ActiTrexx can make the vital medication available to the patient in just 24 hours. Actileucel has already been classified as an advanced therapy medicinal product (ATMP). The development is supported by, among others, the Federal Ministry of Education and Research and the European Regional Development Fund (ERDF). In summary, the method can help to develop an optimized treatment path for various serious diseases that require stem cell transplantation (such as cancer, autoimmune diseases and infections) and to significantly reduce the risk of life-threatening complications and the development of long-term side effects.
ActiTrexx. Activated treg for tolerance.
info@actitrexx.de
Added on: 04-09-2024
Automated screening platform for assessing genetic risk in newborns
CompanyRevvity, Inc., Waltham, USA
To optimize newborn screening, PerkinElmer has developed the high-throughput platform GSP® instrument. The platform enables screening for seven relevant genetic diseases: congenital hypothyroidism, phenylketonuria, galactosemia, cystic fibrosis, congenital adrenal hyperplasia, biotinidase deficiency and glucose-6-phosphate dehydrogenase (G6PD) deficiency. To assess genetic disease risk, the system examines dried blood spot (DBS) samples that are uploaded to the GSP system along with barcoded reagents. After uploading the plates and reagents, the automated system takes over all critical work steps, which are monitored using integrated screening software (Specimen Gate®). Using a touchscreen, researchers can, for example, have their work lists automatically created by a punching device, determine the order of plate analysis or induce new test runs if the results are unclear. In addition, the software includes a quality control program that enables effective and long-term management of patient data. In summary, the GSP instrument platform proves to be an innovative solution that simplifies newborn screening for researchers through automated work steps, at the same time reducing sources of human error and thereby generating precise results more quickly.
GSP® Instrument. Fully automated newborn screening.
www.revvity.com
Added on: 01-11-2024
Brain surgery simulator
CompanyCAE Healthcare, Sarasota, USA
The NeuroVR is a virtual reality training device for open cranial and endoscopic brain surgery. With modules that replicate realistic instruments, imaging, and open neurosurgical procedures, it allows a risk-free, self-directed practice. It features realistic sounds, realistic scope lens blurring and lifelike renderings of brain tissue, vessels and tumors. Modules for instrument handling are suction, ultrasonic aspirator, bipolar forceps and microscissors. Fundamental skills modules like burr hole selection, endoscopic ventricular landmarks, tumor debulking and aneurysm exposure are available. Endoscopic surgery modules include sphenoid ostium drilling, ethmoidectomy and endoscopic third ventriculostomy (ETV). Microsurgery for meningioma and glioma can be trained as well.
NeuroVR
www.caehealthcare.com
Added on: 03-30-2021
BrainSim: 3D in vitro model for drug and toxicology screening of the central nervous system
CompanyAxoSim, Inc., New Orleans, USA
AxoSim's BrainSim platform is an in vitro model that, when combined with an AI, enables improved toxicology and drug screening of the central nervous system. For this purpose, three-dimensional spheroids are cultivated based on inductive, pluripotent stem cells. These are then used in the BrainSim platform, where further differentiation generates three relevant, critical cell types (neurons, astrocytes and oligodendrocytes) of the central nervous system in a biomimetic environment. The cultivation method induces a high level of myelination and reflects important properties of the characteristic cells of the CNS and their interaction, and allows for an imaging of the brain structure. Reactions between the different cell types are characterized using electrophysiology, immunohistochemistry (ICC), histology, flow cytometry and gene expression. These can be accompanied by phenotypic changes in the mechanisms of action of the drugs to be tested. As a result, relevant predictive data is determined more quickly than is possible with the previous, established methods. The BrainSim platform thus enables the identification of neurotoxic substances and improves drug development and research into neurodegenerative diseases.
BrainSim®
info@axosim.com
Added on: 06-06-2023
Cardiac ultrasound TEE and TTE via virtual simulation
CompanyMedaPhor Ltd., Cardiff, United Kingdom
HeartWorks is an anatomic cardiac module for transthoracic (TTE) and transesophageal (TEE) echocardiography. It provides a realistic simulation of 135 accurately designed 3D cardiac structures which can be removed or highlighted for full comprehension. In combination with a patient mannikin, trainees can learn how to perform TTE and TEE safely and how to orientate with the help of the key landmarks to identify the image windows and compensate for obstructions to acquire images of high quality. 30 patient cases are included and students can assess their skills with a testing module.
HeartWorks
www.intelligentultrasound.com/
Added on: 06-30-2022
Chat technology for personalized drug development and discovery via conversation
CompanyInsilico Medicine, Pak Shek Kok, Hong Kong SAR of China
Insilico Medicine specializes in optimizing the development of new drugs and personalized therapeutic approaches. The AI-based platform PandaOmics combines machine learning with genetic data analysis. To identify and evaluate potential drug candidates, the AI uses a huge data set. The OMICS database includes all relevant biochemical information on genomics, epigenomics, transcriptomics and proteomics. In addition, the AI takes connections from publications, grants, patents and clinical studies into account. The company develops specific deep learning algorithms to simulate diseases and analyse patient-specific data. Disease modelling enables targeted drug target analysis, the development of (personalized) drugs and the identification of disease-specific biomarkers. The platform automatically saves the newly gained insights in the database to be taken into account in future analyses. Insilico Medicine's latest innovation is the integration of a new language program: ChatPandaGPT. This allows researchers to converse with the AI in natural language and navigate their studies verbally in a simple and straightforward manner. In summary, the PandaOmics platform proves to be a valuable method that can help accelerate personalized drug discovery and improve risk management.
Panda Omics
info@insilicomedicine.com
Added on: 11-29-2023
Chemoproteomics platform for developing of targeted cancer drugs
CompanyBridGene Biosciences, San Jose, USA
The company BridGene Bioscienes specializes in the development of small molecule drugs against difficult-to-cure, protein-associated cancers. To identify potential active ingredients and their binding targets, the company has developed the chemoproteomics platform IMTAC™ (Isobaric Mass Tagged Affinity Characterization), which combines different technological approaches (covalent chemistry, chemical proteomics and quantitative mass spectrometry). The platform includes a comprehensive covalent library of small molecules loaded with various “warheads” targeting specific amino acids (cysteine, lysine, tyrosine, etc.). The molecules are brought into contact with living cells and penetrate into all cellular areas. IMTAC™ thereby enables effective screening of the entire proteome and direct isolation and identification of bound protein targets. Furthermore, the platform is able to specifically screen for disease-promoting mutants that are associated with certain oncological diseases (such as K-RAS G12C). In addition, the living cells are examined in different stimulation states. This also makes it possible to discover target binding targets in dynamic protein pockets that only develop in certain stimulus states. Using quantitative mass spectrometry, the potential protein targets are identified, their binding affinity to the molecule is assessed and a drug candidate ranking is created. In summary, the IMTAC™ platform proves to be an innovative solution for screening for previously unknown drug targets that can help improve the development of targeted cancer drugs.
Unlocking the proteome. Bridging new medicines with undruggable targets.
info@bridgenebiosciences.com
Added on: 01-18-2024
Combined laser and software technology to study functional and structural cardiotoxicity
CompanyForesee Biosystems, Genova, Italy
Foresee Biosystems has developed the so-called IntraCell platform to research short-term (functional) and long-term (structural) damage to heart muscle cells. The platform combines laser-based technologies with Microelectrode Array (MEA) technology, enabling the monitoring of the electrophysiological activity of cultured myocardial cells and simultaneous optical microscopic assessment of the cell morphology. The cells are automatically examined using control software that documents the experiments in real time (photos, videos). At the beginning of the experiment, the user can use the software to specify individually desired research parameters (electrode selection, laser scan parameters, examination period, MEA layout). With IntraCell, the action potentials of a single cell can be monitored over a period of a few hours to several weeks. This enables the identification of acute as well as chronic cardiotoxic agents. Several heart-damaging substances from the CiPA list have already been validated using the method in various test series. The computer-aided combination of electrophysiological and microscopic technologies enables non-invasive research into functional and, for the first time, chronic cardiotoxicities in vitro and can help to improve drug development.
IntraCell
info@foreseebiosystems.com
Added on: 07-25-2023
Computer models to optimize drug discovery and drug development
CompanyProtoQSAR, Valencia, Spain
ProtoQSAR is a company that specializes in the development of computer-based methods to optimize drug discovery. To analyse and evaluate the compounds to be tested, two different mathematical calculation approaches (“molecular modelling” and “chemical informatics”) are used, depending on the input information. To search for suitable drug candidates, ProtoQSAR uses a virtual screening technology that enables the evaluation of large quantities of molecules. The ProtoPRED platforms can be used for different areas and phases of drug discovery and drug development. In addition to the identification of new active ingredients, they enable the therapeutic repositioning of drugs, the prediction of pharmacokinetic and toxicological properties (such as ADME behaviour, assessment of cytotoxicity and genotoxicity), as well as the design of non-structural (pharmacophoric) drug analogues that are not protected by previous patents. The newly acquired information can be stored in so-called focused libraries and thus taken into account in future calculations. In addition to the various services, ProtoQSAR offers courses where researchers can learn to program their own QRSA mathematical models.
ProtoPRED
info@protoqsar.com
Added on: 11-22-2023
Computer platform for the development of selective and targeted intracellular therapeutics
CompanySRI International, Menlo Park, USA
To optimize the development and delivery of intracellular biotherapeutics, SRI Biosciences has developed the computer-based platform FOX Three Molecular Guidance System. The platform enables cell-selective and targeted transport of large-molecule active ingredients into the interior of the cell by identifying unique peptide suppliers, the so-called MGS. The MGS are able to transport various therapeutic agents, such as protein-based toxins, antibodies, nucleic acids, liposomes and nanoparticles, to the desired cell types. After systemic administration and binding to the target cell, the MGS induce rapid cellular uptake of the bound cargo and deliver it to a specific target within the cell (also called a subcellular organelle). The computational platform's integrated data library currently includes information on 40 known MGS, selectively targeting nearly 20 different cell types and enabling targeted delivery to up to a dozen different subcellular drug targets. The FOX Three MGS platform was originally developed primarily to identify efficient targets for tumor suppression. The company has now expanded the database to other research areas and is working on the identification and development of MGS, which is intended to advance drug therapy options for liquid tumors and cardiac and metabolic diseases. Research is also underway to discover potential vaccine targets. In summary, the platform is a promising method that can help develop drugs that specifically target diseased cells without affecting the functional activities and structures of healthy cells.
FOX Three Molecular Guidance System (MGS)™
customer.service@sri.com
Added on: 11-30-2023
Developmental toxicity assay for the specific detection of teratogenic drug compounds
CompanyToxys Europe, Oegstgeest, Netherlands
The ReproTracker from Toxys is a specially developed computer-based high-throughput screening assay for the identification of active ingredients and other bioactive substances that can lead to disorders of stem cell differentiation and malformations in early embryonic development. The method is based on the controlled cultivation and differentiation of human induced pluripotent stem cells into specific tissue types, which allows relevant steps in cellular embryogenesis to be observed and investigated. Using high-resolution imaging screening methods, functional and morphological expression patterns of relevant biomarkers are analysed to evaluate the toxic properties. ReproTracker shows high sensitivity and reliably detects specific teratogenic drug compounds and enables prediction of possible drug-induced pathological consequences for the growing embryo. In summary, the assay platform could help accelerate the safety testing of new active ingredients and improve drug treatment options for pregnant women.
ReproTracker
info@toxys.com
Added on: 11-08-2023
Digital heart twins for personalized therapeutic approaches
CompanyinHEART, Pessac, France
The company inHEART specializes in 3D visualization of the human heart using computer-aided methods. Using special segmentation algorithms, the software creates a personalized digital copy of the patient's heart based on the patient's data. Within a cloud-based platform, physicians and scientists can interactively explore cardiac anatomy, highlight individual major and collateral structures for closer examination, and assess the properties of myocardial tissue. For broad applicability, the platform can be integrated into all common clinical EAM systems. The method enables optimized planning of cardiac surgical procedures. The three-dimensional visual representation also supports doctors in educating patients and can thereby help to improve compliance. So far, it has been shown that “digital heart twins” help to significantly reduce surgical procedure times and reduce the occurrence of recurrences. In summary, inHEART offers an innovative solution approach that helps advance personalized (surgical) therapy in the field of cardiology on several levels. In addition, the platform is suitable as a digital learning tool for students.
Digital twin of the heart
contact@inheartmedical.com
Added on: 04-24-2024
Endoscopy simulator for diagnostics and surgery
CompanySurgical Science, Gothenburg, Sweden
The endoscopic training simulator offers repetitive practice at increasing levels of difficulty with customizable challenges, scenarios and complications in a realistic environment. Lifelike anatomic detail and realistic touch feedback help physicians to gain experience in instrumental handling, navigation, mucosal examination, retroflexion and loop reduction. Key clinical skills related to pathology biopsy, polypectomy biopsy, cholangiopancreatography and injection sclerotherapy can be acquired. Bronchoscopy and colonoscopy are other modules for training of interventional and diagnostic procedures. It Includes a split-screen for simultaneous display of endoscopic and fluoroscopic views.
EndoSim
support@surgicalscience.com
Added on: 12-18-2020
Exploring populations with special genetic characteristics for drug research
CompanyVariant Bio, Seattle, USA
To optimize active ingredient research and drug development, Variant Bio conducts human genetic studies in collaboration with numerous research partners worldwide. The company takes a unique approach by combining anthropological, epidemiological and genetic findings. In its population genetic analyses, the company focuses in particular on underrepresented population groups that have special health-related characteristics and/or rare genetic variants at high frequencies. The current pipeline includes multiple programs at various stages of development to identify therapeutic targets and potential drug candidates. The focus of the pipeline is on the development of drugs to treat severe fibrotic, immunological, liver and kidney diseases for which there are not yet sufficient drug therapy options. Research into previously unconsidered genetic populations and cohorts shows high potential for generating new knowledge. By developing patient-specific algorithms and using state-of-the-art analysis techniques from various biomedical research areas, the company's large-scale biodatabase and screening platform helps to accelerate drug discovery and optimize patient medication care in a personalized manner.
Human genetics has the power to transform drug development
info@variantbio.com
Added on: 04-24-2024
Eye surgery simulator
CompanyHelpMeSee, Inc., New York, USA
This ocular incisions and dissections course teaches and assesses external ocular incision and dissection skills with the help of a simulator device. Skills acquired in this simulated procedure are essential in manual small incision cataract surgery (MSICS), different phacoemulsification cases, secondary intraocular lens (IOL) implantation and trabeculectomy. Virtual reality simulation with haptic feedback, physical models of the surgical instrument and eye tissue interactions mediate the mechanics of ophthalmic surgery. It simulates how ocular tissues respond to interactions with instruments during cutting, stabbing, and injecting. Performance reports are retrievable to improve outcomes.
HelpMeSee
pr@helpmesee.org
Added on: 12-18-2020
Fully automatic extraction system for sample preparation for environmental analysis
CompanyBiotage, Uppsala, Sweden
The company Biotage offers various solutions and products for the extraction, purification and preparation of environmental samples. The Biotage® Horizon 5000 is a fully automated disk extraction system designed for sample preparation for drinking water, groundwater and wastewater analysis. The compatible table system has various modules that enable the chemical preparation of up to 12 different samples with just one PC. After uploading the sample to be examined and an SPE disk, researchers can select the required extraction method from a number of different options within an easy-to-use software program. The Biotage® Horizon 5000 then handles all critical work steps, from conditioning the disk, loading the sample, drying the disk and generating a final extract. In order to reduce possible sources of error, integrated safety software monitors the entire sample preparation process. So far it has been shown that the extraction system is able to effectively recover even very low concentrations of pollutants (such as 1,4-dioxane or certain herbicides) from heavily contaminated samples and supports established test methods. In summary, the Biotage Horizon 5000 helps improve the accuracy and sensitivity of environmental analysis by reducing sources of human error and effectively removing interfering compounds from test samples. The system can also be complemented with the Biotage® VacMaster™ Disk, a manual disk extraction system to extract semi-volatile organic compounds, as well as oil and grease from aqueous samples.
Biotage® Horizon 5000. Automation for SPE disks.
info@biotage.com
Added on: 02-01-2024
Gastrointestinal and bronchial endoscopy simulator
CompanyCAE Healthcare, Sarasota, USA
The virtual simulator for both gastrointestinal and bronchial training features haptic technology that accurately replicates the use of an endoscope during clinical procedures of cases developed from real patient data. It allows learners to get a feel for upper and lower GI (gastrointestinal) and bronchoscopy flexible endoscopy procedures. The following bronchoscopy modules are available: introduction to bronchoscopy, bronchoalveolar lavage (BAL), transbronchial needle aspiration (TBNA), pediatric difficult airways, and endobronchial sampling. For the upper and lower GI, procedures like esophagogastroduodenoscopy, cholangiopancreatography, introduction to sigmoidoscopy, introduction to polypectomy or biopsy can be trained. The extensive library of pathologies, as well as customizable curriculum and success monitoring, offer a wide learning range.
CAE EndoVR™ Interventional Simulator
www.caehealthcare.com
Added on: 03-30-2021
Gastrointestinal simulation model for diagnosis and treatment
Company3D Systems, Littleton, USA
The combined hard- and software simulation offers over 100 tasks and patient scenarios for gastrointestinal diagnostics and therapeutic procedures. It creates a life-like sensation of real endoscopic procedures with authentic scopes. Modules available are fundamental skills (navigation, mucosal evaluation, targeting, retroflexion, and loop reduction), cyberscopy, upper and lower GI endoscopy, EMR (endoscopic mucosal resection), ESD (endoscopic submucosal resection), ERCP (endoscopic retrograde cholangiopancreatography), emergency bleeding, flexible sigmoidoscopy and endoscopic ultrasonography.
This GI training is evidence-based and validated in over 40 studies.
GI Mentor
healthcare@3DSystems.com
Added on: 12-14-2020
Holotomography for the investigating of cells and tissues
CompanyTomocube, Daejeon, South Korea
Tomocube’s Holotomography (HT) technology provides label-free 4D quantitative imaging solutions for imaging and analysing cells, tissues and organoids. Without using any preparation including fixation, transfection, or staining, details of dynamics and mechanisms of live cells, subcellular organelles, and tissue structures can be seen. HT not only enables observation of nanoscale, real-time results based on quantitative phase imaging (QPI) but also provides quantitative information of cells and organelles.
The Tomocube system uses a Digital Micro-mirror Device (DMD) to enable the illumination beam rotation.
Using the TomoStudio™ software, it is possible to visualize 3D, colour-coded structures that were previously undetectable without staining.
Because the refractive index has a linear correlation to protein concentration, quantitative data such as volume, surface area, and dry mass can be extracted from the cell and its subcellular components without invasive labelling.
Furthermore, with full automation, Tomocube’s HT enables long-term study of live cells on a large scale, and real time live cell analysis.
Holotomography - Label-free quantitative imaging: A completely new way of investigating cells and tissues
info@tomocube.com
Added on: 08-31-2023
Hybrid bio-AI platform for evaluating the effectiveness and safety of new drugs
CompanyQuris Technologies LTD, Tel Aviv, Israel
Quris Technologies specializes in optimizing drug development through AI-based solutions. To optimize the development of new drugs, the company has developed a predictive bio-AI platform that combines microfluidic chip technology with machine learning. To assess drug safety, newly developed active ingredients derived from human stem cells are tested on patients-on-a-chip. The hybrid platform has hundreds of these “microfluidic mini-patients” and enables high-throughput screening of new drugs. The newly acquired information is then transformed into classification algorithms that use AI to predict the effectiveness and possible toxic interactions of the tested active ingredients. The basis for the prediction is a huge genetic data set developed by Quris, which includes newly discovered microRNA genes and is continuously expanded through automated high-throughput screenings. The integrated machine learning system also uses the newly acquired data to train the existing classification algorithms in order to continually improve and make the predictions more precise. In summary, the bio-AI platform is proving to be an advanced method that can help accelerate drug trials and improve clinical predictions about the efficacy and safety of new agents.
Machine-Learning. Trained by Patients-on-a-Chip.
contact@quris.ai
Added on: 01-09-2024
Identification of new biomarkers for cancer and neurodegenerative diseases
CompanyVITO NV, Mol, Belgium
Biomarkers are used in prevention, screening for certain diseases and evaluating treatments. The VITO organization focuses on the identification of biomarkers for minimally invasive diagnostic applications, preferably using liquid biopsies (urine, blood and cerebrospinal fluid). In addition, however, fresh and fixed tissue material is also used for biomarker development using MALDI-based imaging. Within the scope of biomarker research, VITO focuses on the following areas:
1. Mainly for cancer research (bladder and lung cancer) and neurodegenerative diseases (dementia), VITO uses state-of-the-art mass spectrometry for the identification and detection of (panels of) protein biomarkers.
2. In the context of cancer research (especially lung and colorectal cancer), immunopeptides are analysed using immunopeptidomics with regard to applications in T-cell therapy, immunotherapy and personalised vaccination.
www.vito.be
Added on: 11-19-2021
In-silico heart model for cardiotoxicity and drug studies
Companymyofarm, Göttingen, Germany
The company myofarm has developed a platform for high-throughput analysis of cardiac, three-dimensional microtissue. The platform enables multiparametric measurements without damaging heart tissue cultured from human inductive pluripotent stem cells (hiPS). In this way, short-term as well as long-term interactions between the active substances to be tested and the excitation-contraction coupling of the mini-heart can be assessed. The measurement data obtained is fed into an AI-based database, which researchers can use to plan and carry out laboratory-independent cardiotoxicity tests and preclinical drug studies.
myofarm for a better and animal-free drug development
hello@myofarm.de
Added on: 08-08-2023
IndivuServ: assistance for personalised oncology
CompanyIndivumed GmbH, Hamburg, Germany
IndivuServ is a service by Invidumed that offers high-quality cancer patient biospecimens and analysis for biomarker discovery, drug profiling, immune-oncology studies and clinical trials.
info@indivumed.com
Added on: 06-21-2021
IndivuTest: innovative diagnostics for personalised cancer therapy
CompanyIndivuTest GmbH, Hamburg, Germany
IndivuTest is a subsidiary of Indivumed andaims to decipher the biological basis of cancer using the latest scientific analysis methods and thus promote the individualization of cancer therapies in advanced stages of the disease. For this purpose, tumor samples from patients are analysed using elaborate and complex Multi-OMICs techniques combined with the latest scientific knowledge of drug development. The resulting individual patient profile enables the attending physician to select the presumably most effective therapeutic approach based on scientific criteria.
IndivuTest
info@indivutest.com
Added on: 06-21-2021
IndivuType: global multi-OMICs cancer database
CompanyIndivumed GmbH, Hamburg, Germany
IndivuType is a knowledge and discovery platform that combines genomics, transcriptomics, and proteomics datasets to enable cutting edge precision medicine approaches. IndivuType is a cancer database comprising patient-derived biospecimen and clinical data. It offers comprehensive visualization, statistical, bioinformatics and artificial intelligence tools for clinical evaluation, biomarker and target identification and validation. InviduType can be used to assist patient stratification and cohort design for clinical trials, as well as for multiple molecular aspects in basic and clinical research.
info-eu@indivumed.com
Added on: 06-21-2021
Infrared fingerprints optimize the analysis of biomolecules
CompanyIsospec Analytics, Lausanne, Switzerland
Isospec Analytics specializes in the analysis of biomolecules to identify unknown compounds in pharmaceutical, food and environmental samples. To precisely characterize each molecule, the company combines LC/IMS separation technologies and mass spectrometry with highly sensitive infrared (IR) technology, which provides a detailed insight into the metabolite and glycan structure of the molecules. The information obtained in this way is transformed into “molecular fingerprints” using AI-supported methods. Machine learning systems can now make predictions about the mode of action and toxicity of individual molecules based on these unique structural profiles. The method also shows high potential for identifying still unknown biomarkers, potential active ingredients and new therapeutic targets. In summary, IR fingerprint technology expands the possibilities of previous mass analysis and can thereby help to accelerate quality and safety tests in various application areas, as well as generate valuable new insights for medical, food and environmental research. To optimize work processes and reduce sources of errors, Isospec Analytics is working on a completely automated solution approach.
Redefining what's possible in molecular analysis
www.isospecanalytics.com
Added on: 04-23-2024
The virtual simulator is designed to practice, learn and improve minimally invasive surgical skills, from basic to advanced laparoscopic procedures. Trainees can develop proficiency in techniques such as suturing, knot tying and loop ligation. Some frequently performed laparoscopic surgeries like gallbladder removal, appendectomy or gynaecological procedures like tubal occlusion or tubal ectopic pregnancy are included. Essential skills like camera navigation, cutting and clipping can be trained. The dual foot pedal is for electro-surgery and advanced energy devices. The software tracks time, skill level and complications of the training to estimate the proficiency of trainees.
CAE LapVR™ Surgical Simulator
www.caehealthcare.com
Added on: 03-30-2021
Learning with a hologram patient
CompanyGigXR, Los Angeles, USA
HoloHuman is a full-sized 3D human anatomy atlas for medical students to learn anatomy systems and structures in virtual reality. A part of the body can be separately examined, it can be fully rotated and virtually dissected, providing a much deeper insight into human anatomy than it is possible with a cadaver. One step further is a mixed-reality system called HoloScenarios in which a hologram patient displays disease symptoms with asthma, anaphylaxis, pulmonary embolism and pneumonia. Modules for cardiology and neurology are under development, a dental module is already in use. Students with virtual reality headsets can interact with the holo-patient but still see each other for interaction. Due to the scenarios being virtual, they are accessible from anywhere in the world.
HoloHuman
www.gigxr.com
Added on: 06-30-2022
Machine learning and computer-aided drug design to improve drug discovery
CompanyMerck KGaA, Darmstadt, Germany
AIDDISON™ is an AI-based software that combines machine learning (ML) and computer-aided drug design (CADD) in one platform to optimize drug discovery. By integrating all the necessary online tools in just one program, time-consuming work steps such as transferring and reformatting huge data sets are avoided. The complex calculations of the integrated cloud-native systems enable screening for complex 2D/3D structures and an assessment of the ligands they bind. By developing drug-like molecules, the ML system is continuously trained, which in turn increases the diversity of the screening results. The files fed in and the results obtained are managed according to the highest security standards and transmitted to the researchers in accordance with data protection regulations. Overall, AIDDISON™ can be evaluated as a comprehensive digital solution that can help accelerate the search for suitable active ingredients and improve the accuracy of pharmaceutical predictions.
AIDDISON™ AI-powered drug discovery
www.sigmaaldrich.com
Added on: 02-20-2024
Mini smart PC for improved surgery performance
CompanyMedtronic Covidien, Watford, United Kingdom
This surgical video platform consists of a smart computer that digitally saves surgeries in a secure online library. Surgeries can be automatically analyzed in order to review the performance and learnings of the whole operating team. A build-in AI anonymization provides necessary patient data privacy. Via app or web, the online library can be used as well for surgeon trainees to get familiar with various procedures.
Touch Surgery
www.medtronic.com/covidien/en-gb/index.html
Added on: 06-20-2022
Model for catheter procedure training
CompanyCAE Healthcare, Sarasota, USA
The portable, modular system allows practising endovascular diagnostic and interventional catheter lab procedures. Navigation of catheters, wires, balloons and stents can be learned with this simulator.
Various 3D anatomies with cardiac and vascular abnormalities from real patient cases are included as well as a 3D fluoroscopic view of coronary anatomy. Procedures like Trans-catheter Aortic Valve (TAV) placement, carotid artery angioplasty and stenting can be trained and these case parameters can be customized according to the trainee's training levels.
CathLabVR™
www.caehealthcare.com
Added on: 03-31-2021
NerveSim: 3D in vitro model for drug and toxicology screening of the peripheral nervous system
CompanyAxoSim, Inc., New Orleans, USA
AxoSim's NerveSim platform is an in vitro model that, when combined with an AI, enables improved toxicology and drug screening of the peripheral nervous system. For this purpose, three-dimensional spheroids are cultivated from induced pluripotent stem cells (iPSCs). These are then inserted into the NerveSim platform and further grown into a biomimetic model that mimics the form and function of peripheral nerves. The model features unique Schwann cell myelination and reliably produces key clinical measures (nerve excitability, conduction, and histomorphometry) of peripheral neuropathies and neuropathic pain previously only achievable in in vivo clinical methods. The SimTox platform, for the detection of neurotoxic agents, has a higher sensitivity to neurotoxic agents compared to the currently established clinical models, thereby enabling faster treatment to save the patient. With the SimDiscovery platform, neurological clinical pictures can be simulated and drugs in the development phase can be identified safely and faster than is possible with previous time-consuming and cost-intensive methods. The NerveSim platform thus enables more targeted treatment of patients and is proving to be suitable for improving long-awaited drug development.
NerveSim®
info@axosim.com
Added on: 05-30-2023
Patient-derived 3D mini tumors make it easier to choose treatment for cancer
CompanyASC Oncology GmbH, Berlin, Germany
To improve and facilitate the selection of a suitable drug therapy for cancer, ASC Oncology has developed the Reverse Clinical Engineering® test procedure. The personalized test procedure is suitable for patients with malignant, solid tumors, such as carcinomas or sarcomas. In the ASC Oncology laboratory, numerous three-dimensional “mini tumors” (PD3D® tumor organoids) are cultured from patients’ tumor tissue samples (obtained through biopsy or surgical resection), on which drugs can be safely tested. An automated screening platform with an integrated genetic data set analyses the exposed active ingredients and assesses cell viability. In addition, a sensitivity profile of the individual tumor model is created for each active ingredient tested. To make the predictions more precise, the test procedure can be expanded with additional options (e.g. sequencing, proteome analysis). Within an average of 28 days, the process generates meaningful results that are transmitted to doctors in accordance with data protection regulations. So far, it has been shown that Reverse Clinical Engineering® could correctly predict the effectiveness of drugs for tumor patients in up to 88% of cases and ineffectiveness in up to 100% of cases. In summary, it can be seen that the method enables an accelerated and personalized prediction of the effectiveness of various active ingredients, which can help doctors and their patients decide at an early stage on the safest and most effective drug treatment for cancer.
Krebs. Vor Behandlungsbeginn die Optionen testen. Im Labor. Ohne Nebenwirkungen.
www.asc-oncology.com
Added on: 01-18-2024
Pelvic training and diagnostic simulator
Company3D Systems, Littleton, USA
This simulation is a didactic tool for pelvic examination training which provides immediate feedback on the rate of anatomical learning success. Different pathologies like abnormal uteri can be visualized and identified in this physical model that is combined with a 3D virtual system. It allows a real-time indication of finger palpation, downward press on the abdomen and cervix manipulation during the anatomical recognition phase or while performing a pelvic exam.
Modules include a normal uterus, uterine fibroids, an ectopic pregnancy, a multiparous uterus, an ovarian cyst, and a retroflexed uterus. Anatomies are interchangeable and the speculum examination is linked to a pain meter.
PELVIC MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
Personalised brain maps to optimize the diagnosis and treatment of brain tumor
CompanyBraincarta, Utrecht, Netherlands
In order to be able to use the advantages of functional MRI scan technology for the diagnosis and treatment planning of brain tumor patients in smaller clinics and oncological therapy facilities, the company Braincarta has developed the Elonav concept. Elonav is an AI-powered, fully automated data processing and evaluation program that enables functional MRI scans to be performed without the presence of a specially trained technical expert. The patient's fMRI data is recorded according to standardized specifications and then uploaded to the Braincarta server. To evaluate brain function and structure, the recorded patient data is compared with an integrated patient database. Based on this comparison and using well-known neurologically relevant algorithms, Elonav creates a personalized brain map of the patient, which provides doctors with precise information about the location of the tumor as well as the structure and functionality of neighbouring brain areas. Within a few hours, the data protection-compliant results are provided in the form of a PDF report and a 3D DICOM file. The three-dimensional, image representation of the brain enables neurosurgeons and radiation therapists to develop a targeted and personalized treatment strategy. In summary, the CE-certified method proves to be an innovative digital solution that can help make fMRI technology accessible to all patients for optimal medical care and reduce possible treatment risks (such as functional damage caused by neurosurgical procedures or radiation). Additionally, the Elonav concept is suitable for multicentre pharmaceutical and biotechnology research projects that require standardized protocols for fMRI, resting state fMRI and DTI (diffusion tensor imaging).
Personalised brainmaps
info@braincarta.com
Added on: 02-01-2024
Personalized tumor therapy with 3D cell culture and high-throughput flow cytometry
CompanyCELLphenomics, Berlin, Germany
CELLphenomics has developed a functional precision medicine platform that combines high-throughput flow cytometry with advanced automation and an optimized analysis pipeline.
CELLphenomics' ex vivo assays combine machine learning, automation and high-throughput flow cytometry to predict response to potentially approved or investigational therapies and ultimately determine which drugs or drug combinations are most effective for specific cancer types. To do this, PD3D® cell cultures or tumor organoids are grown from tumor biopsies within a short period of time. These are then treated in parallel with all possible cancer drugs and combinations of drugs. Classical chemotherapeutic agents as well as small molecules and therapeutic antibodies are tested individually or in combination. In addition, proteomic analysis can be performed.
CELLphenomics combines data generated by its flow cytometry platform with valuable patient clinical characteristics for patient stratification, enabling more precise treatment of patients with solid cancers. CELLphenomics uses cutting-edge cancer research technology to advance new therapies from target identification in drug discovery to clinical trial validation and successful clinical development.
info@cellphenomics.com
Added on: 07-30-2021
Platform for analysing the formation and mechanisms of action of disease-associated condensates
CompanyDewpoint Therapeutics, Boston, USA
Condensates are membrane-unbound cell organelles that influence the course of important biochemical processes within the cell through compartmentalization and concentration of certain molecules. Malformed condensates can be associated with a toxic gain and/or loss of function of the affected cell (or the affected cell compartment). Their dynamic behaviour is regulated by interaction mechanisms between different proteins and nucleic acids. To analyse condensates' formation and mechanism of action, and their significance for the development of complex diseases (so-called condensatopathies), the company Dewpoint Therapeutics combines findings and methods from condensate biology with AI-based in silico methods in an integrated platform. High-resolution imaging screening methods are used to analyse the genetic and phenotypic characteristics of certain condensates, and the information obtained is stored in the form of “digital fingerprints”. The integrated AI can now predict statements about the behaviour of specific condensates based on the resulting data sets (multi-omics). In addition, the platform is able to identify disease-associated cell organelles and thereby enables targeted screening for relevant biomarkers and condensate-modifying agents (so-called c-mods). In summary, the integrated platform proves to be a valuable method that can help to understand the complex interactions between different cell organelles and compartments in greater depth and to prevent the development of condensation diseases through improved and personalized drug development.
A fully integrated discovery platform
www.dewpointx.com
Added on: 11-14-2023
Portable laparoscopic training device
CompanySimulab, Seattle, USA
The portable device with a video screen allows instructors and students to perform life-like simulated laparoscopic surgery. Different synthetic realistic tissues and organs can be ordered to train basic skills like suturing, needle transferring, knot tying, dissection as well as surgeries like cholecystectomy or Nissen fundoplication.
LapTrainer with SimuVision®
www.simulab.com
Added on: 12-18-2020
Portable minimally invasive surgery simulator
CompanyeoSurgical, Edinburgh, United Kingdom
The portable training device is a minimally invasive surgery simulator that combines hard- and software. It allows handling of various instruments and offers 18 skill modules. The camera of a mobile phone or tablet can be used instead of the built-in webcam. Using basic modules, suturing, cutting and tube ligation can be trained. Within the gynaecological modules, fallopian tube ligation, ovarian cyst dissection and suturing techniques can be trained.
Urology and orthopaedic modules offer a wide training range. For paediatric surgery training, hernia repairs and pyeloplasty are available. It is possible to combine the device with 3D printed silicone models for patient-specific simulation and pre-operative planning.
A pocket size microsurgical simulator is available for use with a mobile phone.
eoSIM & SurgTrac
www.eosurgical.com
Added on: 03-31-2021
Practice emergency operations in a real environment
CompanyNordic Simulators, Lahti, Finland
2D and 3D environments can be customized to train various healthcare situations such as mass car crashes or emergency medical care after combat operations. Comprehensive camera systems are available that track a simulated emergency case, which is discussed after the training to optimize patient care. The advantage is that the complete process (patient admission, initial care, emergency procedures) can be practised in areal hospital environment until the procedures are confidently mastered.
Nordic Simulators Design Services
office@nordicsimulators.com
Added on: 12-18-2020
Practicing epidural anesthesia with a body simulator
CompanySimulab, Seattle, USA
Spinal epidural insertion and catheterization are critical techniques which can be trained with these ultrasound-guided lumbar epidural and puncture trainers. The physical true-to-life simulator sits upright or in a lateral decubitus position and features the lumbar vertebrae, iliac crest, spinous process, ligamentum flavum, epidural space, and dura. Replaceable tissues to simulate obese, geriatric and normal patients are available.
Lumbar Puncture & Epidural Trainers
www.simulab.com
Added on: 12-18-2020
Prenatal screening platform for risk free early detection of trisomy 21, 18 and 13
CompanyRevvity, Inc., Waltham, USA
The Revvity company offers the Vanadis®-NIPT (non-invasive prenatal test) system for improved and risk-free early detection of genetic diseases in the unborn child. Vanadis enables screening for risk trisomies 21 (Down syndrome), 18 (Edwards syndrome) and 13 (Pätau syndrome), as well as determining the fetal sex by examining cell-free DNA fragments (cfDNA) in the mother's blood. The automated platform takes over all critical work steps. At the beginning, the cfDNA is separated from the plasma and prepared (Vanadis Extract®). The cfDNA fragments are then converted into circular DNA by the Vanadis Core® and marked with chromosome-specific, fluorescent dyes, which are transferred to a microfilter plate (Vanadis View®). Here, the fluorescent DNA fragments are counted using an image analysis algorithm and the results are then transferred to a prenatal screening software (LifeCycle™ 7.0), which assesses the trisomy risk of the respective samples. The method does not require complex PCRs, genome sequencing or microarrays and provides precise predictions within 2–3 days. So far it has been shown that the Vanadis platform has comparable performance in terms of detection and false positive rates with a lower no-call rate compared to NGS (Next Generation Sequencing)-based NIPT systems. In summary, the method enables precise and highly effective prenatal high-throughput screening for relevant genetic diseases, which can help to identify risks to the child's health during pregnancy at an early stage.
Vanadis® NIPT System. A whole new way to NIPT.
www.revvity.com
Added on: 01-11-2024
Self-learning AI accelerates drug development in cancer research
CompanyReverie Labs, Cambridge (Massachussetts), USA
The company Reverie Labs specializes in the development of AI-based software programs to accelerate and optimize drug development. To do this, the company combines different technological approaches that enable a predictive calculation of the efficacy and toxicity of compounds to be tested. To generate the predictions, the systems rely on information from huge, biochemically and physiologically relevant data sets. The programs also work on the principle of machine learning; i.e. they recognize new relevant compounds and mechanisms of action and automatically integrate them into their future calculations. This means that the programs continue to optimize themselves in an ongoing process. In addition, the platforms can help advance personalized medicine, as they are also able to process patient-specific data and take individual characteristics into account in their predictions. The method can be used in various approaches to drug development, such as evaluating the efficacy and safety of new drugs or generating information on the absorption, distribution, metabolism and excretion (ADME behaviour) of certain active ingredients. A particular focus of the company is the development of kinase inhibitors that are used to treat cancer. In summary, the AI-based programs prove to be a valuable and advanced method that can help accelerate (personalized) drug development and improve existing drug treatment options.
Pioneering new technology to develop next-generation cancer therapies
contact@reverielabs.com
Added on: 11-08-2023
SIMCor: In-Silico testing and validation of Cardiovascular Implantable devices
Charité – Universitätsmedizin Berlin, Berlin, Germany
The EU-funded research project SIMCor (In-Silico testing and validation of Cardiovascular Implantable devices) will create an in-silico platform and simulation tools for the development, validation and regulatory approval of cardiovascular devices, providing tangible value to patients and clinicians, device manufacturers, clinical researchers, medical authorities and regulatory bodies. The project aims to enhance the quality of medical devices released into the market, increasing their efficacy and safety, meanwhile reducing costs and time-to-market and minimising the need for live testing on animal and human subjects. High-priority safety, efficacy and usability endpoints will be investigated, focusing on device implantation and effect simulations in two representative areas: transcatheter aortic valve implantation (TAVI) and pulmonary artery pressure sensors (PAPS).
Titus Kühne
Added on: 03-17-2021
Simulation of virtual patient populations for disease- and patient-specific drug development
CompanyCertara, Princeton, USA
The company Certara has developed the biosimulation software Symcyp to evaluate the pharmacokinetics of active ingredients to be tested. The platform can be used in all phases of drug development. It is useful for determining first-in-human dosage, optimizing clinical trial designs, evaluating new drug formulations, setting dose in untested populations, performing virtual bioequivalence analyses, and predicting drug-drug interactions (DDIs). To do this, Symcyp draws on an extensive set of genetic, physiological and epidemiological databases, which facilitates the simulation of virtual patient populations with different demographic characteristics, ethnicities and disease states. The multi-layered data sets and specially developed modules enable the modelling of (organ-)specific diseases, the investigation of various new routes of drug administration, and an assessment of extrinsic factors (such as the influence of smoking or alcohol consumption). In addition, the platform is suitable for drug prediction and dose determination for particularly vulnerable groups, such as infants, small children, pregnant women, breastfeeding mothers and older patients. In summary, the simulation software proves to be a valuable tool for accelerating and improving quantitative drug development and optimizing drug treatment strategies.
Simcyp™ PBPK Simulator
www.certara.com
Added on: 10-04-2023
Simulator for diagnosis and therapies of the urinary tract
Company3D Systems, Littleton, USA
The simulator provides hands-on training for diagnosis and therapeutic interventions of the urinary tract. Simulated bladder and kidney inspection with rigid and flexible cystoscopes and ureteroscopes can be trained, as well as simulation of fluoroscopy and C-arm control. Skills like stone extraction, stone lithotripsy, cutting strictures or taking biopsies can be acquired on a variety of normal or pathological virtual patient cases. The PERC Mentor can be purchased as an add-on tool.
URO MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
Skin sensitization assay for safety assessment of medical devices and solid materials
CompanySenzaGen, Lund, Sweden
The GARD®skin Medical Device from SenzaGen is a quantitative, standardized test method for evaluating medical devices and solid compounds. The method is based on GARDskin technology, which uses AI-based gene expression analysis to differentiate between valid sensitizing and non-sensitizing substances and can thereby predict potential allergic skin reactions. The assay meets the requirements of the ISO Guideline for the Evaluation of Biological Medical Devices (ISO 10993) and supports polar (saline) and non-polar (oil) extraction vehicles as recommended. The machine learning platform generates meaningful results within 4–8 weeks that can help accelerate safety testing and approval of new biomedical products.
GARD®skin Medical Device. In vitro skin sensitization testing for medical devices and solid materials
info@senzagen.com
Added on: 11-21-2023
Software-controlled 3D cell cultivation
CompanyCelVivo, Odense, Denmark
The company CelVivo has specialized in the production of in-vitro research equipment for the maturation and cultivation of three-dimensional cell tissue (spheroids, organoids and other aggregates). The range includes a Clinostat CO2 incubator (Clinostar) and a tablet with pre-installed software allowing all 6 Clinostat units to be controlled independently. The computer-assisted control of the CO2 and temperature conditions within the incubator creates stable and homogeneous growth conditions for the cell cultures. For the cultivation of spheroids, the company also offers bioreactors (ClinoReactors), which are equipped with a semi-permeable membrane, among other things, which ensures that the nutrient medium is in equilibrium with the CO2 content in the incubator. An integrated humidification system keeps the volume in the incubation chambers constant and at the same time minimizes the risk of infection. In addition to optimized maturation and cultivation conditions, the intuitive control software enables the researchers to monitor and document the experiments with images.
Clinostar products
info@celvivo.com
Added on: 07-19-2023
SonoMan: emergency sonography model
CompanySimulab, Seattle, USA
This diagnostic ultrasound training platform is designed for physicians who need to become proficient in the use of trauma, emergency or bedside ultrasound in evaluating critically ill patients. It allows users to visualize the thoracic and abdominal regions in normal or various pathological states. Different modules are available like gallbladder, abdominal aortic aneurysm (AAA), FAST and eFAST ((extended) focused assessment with sonography for trauma) to discover a pneumothorax or hemothorax and a renal module that simulates hydronephrosis or bladder distention.
SonoMan Diagnostic Ultrasound Simulator
www.simulab.com
Added on: 12-18-2020
Spine training model for pain therapy
Company3D Systems, Littleton, USA
The physical spine model with its realistic materials and advanced virtual reality enables the simulation of a full procedure for minimally invasive spine surgery with high accuracy and realistic sensation. Spinal cord stimulation with the implementation of up to 4 needles in the epidural space can be practised under simulated real-time fluoroscopic imaging and C-arm control. Another module covers the lumbar puncture to practice safe insertion of a spine needle and its accurate tracking. Different scenarios are available. The simulator is suitable for anesthesiologists, and pain medicine surgeons.
SPINE MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
Team training of simulated operations
CompanySurgical Science, Gothenburg, Sweden
This dynamic platform is an extended system that uses LapSim, a laparoscopic training device, as a base. Most simulators concentrate on training a single surgeon but with this add-on a whole OR (operating room) team can practice and refine their communication and non-technical skills. It simulates realistic surgery scenarios while controlling bleeding and complications. Available modules include cholecystectomy, appendectomy, gynaecology, hysterectomy, camera anatomy training, VATS lobectomy, bariatrics and nephrectomy. In all modules, instructors can introduce complications and target specific skills development.
TeamSim®
support@surgicalscience.com
Added on: 12-18-2020
The Living Heart Project: A realistic heart simulation
CompanyDassault Systèmes, Paris, France
The Living Heart Project is a big international collaboration project with the mission to develop and validate highly accurate personalized digital human heart models. These models will establish a unified foundation for cardiovascular in silico medicine and serve as a common technology base for education and training, medical device design, testing, clinical diagnosis and regulatory science, ultimately leading to improved patient care.
The Living Heart Project: A translational research initiative to revolutionize cardiovascular science through realistic simulation
Arnaud Malherbe
Added on: 05-25-2020
ToxPHACTS – an expert system for toxicological read across
CompanyPhenaris Softwareentwicklungs und -consulting GmbH, Vienna, Austria
ToxPHACTS integrates professional expertise in computational toxicology and semantic data integration to offer an expert system that will help pharmaceutical companies to foresee the possible toxicity of new drug development candidates. In contrast to the current process for toxicological read-across, ToxPHACTS uses innovative ways of similarity searching, such as bioisosteric similarity, allows complex queries across multiple, semantically integrated data sources and provides advanced visualization tools for rapid and easy analysis of read-across search results. ToxPHACTS thus combines highly innovative similarity searching with the power of semantically integrated life science data. With its unique combination of highly innovative cheminformatics with big data analytics, ToxPHACTS is specifically designed for increasing the precision and sensitivity of toxicity assessments.
Gerhard Ecker
Added on: 06-23-2021
Training model for diagnostic renal access
Company3D Systems, Littleton, USA
This is a medical simulator for training percutaneous access procedures performed under real-time fluoroscopy and is an add-on to the Uro Mentor. The urinary tract can be examined via the urethra and the ureter (Uro Mentor) as well as from the back (Perc mentor).
Percutaneous renal access procedures via the back can be trained on normal or obese virtual patients with normal anatomies or various pathologies.
PERC MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
Training simulator for laparoscopic surgery skills
Company3D Systems, Littleton, USA
The LAP Mentor is a simulator for training essential laparoscopic skills and advanced complete clinical procedures. Lifelike tactile feedback, detailed anatomy, realistic imaging, a wide range of surgical instruments and various trocar configurations across multiple disciplines provide a wide array of hands-on laparoscopic techniques. Training on general surgeries (lap chole, cholangiography, incisional hernia, appendectomy, Nissen fundoplication) as well as bariatric, colorectal, urology, gynaecology and thoracic surgeries are possible. A VR headset is available which enables a completely immersive experience in a realistic 3D operating room setting, featuring the OR (operating room) team, the patient, OR equipment and real-life sound diversions.
LAP MENTOR
healthcare@3DSystems.com
Added on: 12-18-2020
Ultrasound simulation model for different disciplines
Company3D Systems, Littleton, USA
This combined physical model and virtual reality system is used for training of ultrasound-related examinations and interventions. It provides residents and practising physicians an opportunity to acquire and improve their sonography-related skills on a variety of virtual patients. Ultrasonic task of the following can be performed and trained: basics skills, RUSH (Rapid Ultrasound in Shock), eFAST (extended Focused Assessment with Sonography for Trauma), abdominal, neck, lung, transesophageal echocardiography (TEE), COVID-19, basic gynaecology, trimester screening, fetal echo, fetal neurosonsography; interventional ultrasounds include thoracentesis and central venous catheter. Pericardiocentesis is coming soon.
Simulation cases from one's own patients can be scanned and uploaded as well for augmented training scenarios.
U/S MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
This software can plan personalized dental operations and test the surgical incisions in advance, taking into account the individual tooth status and surrounding tissue. This also enables initial experience with as yet unknown techniques and is suitable for anatomy lessons.
3D Cephalometric Tracing and Analysis
info@anatomage.com
Added on: 12-18-2020
Virtual dissection table
CompanyAnatomage, Santa Clara, USA
The Anatomage table is a virtual dissection table that displays real anatomical structures in 3D with the same accuracy as in the real body. It contains complete data from 4 cadavers, more than 20 body regions in high resolution and more than 1000 pathological cases, including some from veterinary medicine. Thousands of structures have been segmented from the high-resolution photographs; even individual vascular structures are reproduced in detail. The FDA-approved radiology software provides additional diagnostic learning content. A dental module is also available.
Anatomage Table
info@anatomage.com
Added on: 12-18-2020
Virtual simulator for bronchoscopy training
Company3D Systems, Littleton, USA
Basic skill tasks and complete clinical procedures of bronchoscopy can be trained with this simulator. Motor, cognitive and coordinative as well as diagnostic and therapeutic skills can be acquired. Suitable for team and solo training. The virtual patient environment covers sedation, topical anaesthesia, reactive vital signs, cough and complications like bleeding or secretion which is crucial for training emergency interventions.
The Bronch mentor won the ERS award of the European Respiratory Society as "Product of outstanding interest" in 2014.
BRONCH MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
Virtual training simulator for arthroscopic interventions
Company3D Systems, Littleton, USA
This simulator offers more than 80 different surgical scenarios for shoulder, hip and knee joint operations as well as diagnoses for basic knowledge and more advanced training scenarios. The system features realistic anatomical models and a training software, with which many surgical scenarios can be represented virtually and by using real surgical instruments, including the artroscopic camera. The haptic feedback allows for a realistic sensation. Basic arthroscopy knowledge, knee, shoulder and hip diagnostics as well as knee and shoulder therapy are taught and trained with high learning efficiency. A meniscal repair module is coming soon.
ARTHRO MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
Virtual training simulator for endovascular interventions
Company3D Systems, Littleton, USA
The training simulator platform is for the practising of endovascular procedures and techniques. The technology enables realistic visualization of the anatomy and instrument activity. It is combined with a haptic system for visual and tactile feedback, which realistically mimics the look and feel of actual endovascular interventions.
Different modules provide basic and advanced cases. The system features more than 35 different endovascular procedures and over 220 patient scenarios. Additional software can create patient-specific 3D digital models based on a patient’s CT, so the actual intervention can be planned and rehearsed prior to surgery on the patient.
ANGIO MENTOR
healthcare@3DSystems.com
Added on: 12-14-2020
Virtual training simulator for surgical laparoscopic surgeries
CompanySurgical Science, Gothenburg, Sweden
The virtual training simulator offers fundamental laparoscopic skills, laparoscopic simulation exercises and an add-on of a VR headset to simulate a complete operating room. Different modules like basic skills (basic navigation, suturing) and advanced surgical procedures are available, like VATS lobectomy, gynaecology (hysterectomy, myoma suturing, tubal occlusion), nephrectomy, bariatric and appendectomy procedures. Various pathologies and complications for each procedure can be adjusted. It comes with a fully validated curriculum, the LapSim Certification Program and annual updates.
LapSim
support@surgicalscience.com
Added on: 12-18-2020
VR system for robotic clinical procedure training
Company3D Systems, Littleton, USA
This simulator enables surgeons of all expertise levels across diverse medical specialities to practice the skills required to perform robotic surgery. The VR Systems provides true-to-life graphics and realistic tissue behaviour. Basic skills can as well be trained as gynecologic, urologic, thoracic, colorectal and general surgeries. Additionally, educational features like real-life patient videos, team training between the robotic surgeon and surgical assistant and performance reports with learning curve graphs complete the system. The development was realized in collaboration with professional societies.
ROBOTIX MENTOR
healthcare@3DSystems.com
Added on: 12-18-2020
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