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AI-powered algorithm accelerates TCR identification to develop personalized immunotherapies

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

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,
Added on: 12-19-2023

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

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

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

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

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

Framework for human stem cell organisation and variation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

National Eye Institute, Bethesda, USA
#blindness, #eyes
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

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

Physicians Committee for Responsible Medicine, Washington, USA(1)
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)
Added on: 04-19-2022

In-silico model for nanoparticle toxicity for water fleas

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

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

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

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

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
Added on: 05-13-2022

AI tool predicts who will develop pancreatic cancer based on CT images

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

Princeton University, Princeton, USA(1)
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

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

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 ( to interactively visualize these results and made the pipeline available as an open-source CRESSP package (, 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

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

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

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

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

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

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

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
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)
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)
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)
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)
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

Queen’s University Belfast, Belfast, United Kingdom(1)
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

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

MD Anderson Cancer Center, Houston, USA(1)
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

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

Maastricht University, Maastricht, Netherlands(1)
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

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

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

Columbia University, New York, USA(1)
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

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

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

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
OECD Guidelines for the Testing of Chemicals, Paris, France
#skin, #toxicity
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 [121]   URL
Added on: 06-23-2021

Genetic variability of the SARS-CoV-2 pocketome

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 (, 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

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

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

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

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

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

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

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

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

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
Added on: 09-12-2022

Brain-computer interface turns mental handwriting into text

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

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

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

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

Boston University, Boston, USA(1)
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

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

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

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

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

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

Lund University, Lund, Sweden(1)
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

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

Christian-Albrechts-University of Kiel, Kiel, Germany(1)
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

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

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

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

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

Charité Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany(1)
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

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 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

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

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

Karolinska Institutet, Stockholm, Sweden(1)
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

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
Added on: 07-01-2021

Biological activity-based modeling identifies SARS-CoV-2 drug candidates

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

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

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

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

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

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

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

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

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; 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

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

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

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)
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

December 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)
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)
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

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

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
Added on: 10-13-2020

Patient-derived cells and brain organoids uncover treatment targets in Parkinson’s disease

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

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
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

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

Hamamutsu University School of Medicine, Hamamatsu City, Japan(1)
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

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

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
Added on: 07-22-2020

Computational platform to make deep learning analysis of human genomics data

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

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
Added on: 11-20-2020

Software detects disease-causing gene mutations

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

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

Molecular dynamics simulations for drug development

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

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

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

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

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

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

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

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

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

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

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

Fraunhofer-Institut für Angewandte Optik und Feinmechanik IOF, Jena, Germany(1)
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

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

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

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

Universität Osnabrück, Osnabruck, Germany(1)
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

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
Added on: 05-26-2020

Computational models for prediction of ligand-transporter interaction

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
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

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

Fraunhofer IBMT, Sulzbach, Germany(1)
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
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)
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)
Added on: 12-17-2020

Living Heart Project: A virtual heart for improved drug testing

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

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

Max Planck Institute for Human Development, Berlin, Germany(1)
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

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

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
Added on: 07-27-2021

RETERO project: Electronic fish surrogates for hydropower facility risk assessment

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

Computational assessment of tumor heterogeneity

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

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)
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

October 2018
Insilico Medicine, Rockville, USA(1)
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

Friends Select School, Philadelphia, USA(1)
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

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)
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
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

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

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

Broad Institute of MIT and Harvard, Cambridge, USA
#RNA-Seq, #tumor
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

National Cheng Kung University Hospital, Tainan, Taiwan(1)
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

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

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

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

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

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

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)
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

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

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

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

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

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

Jilin University, Jilin, China(1)
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

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

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

Toronto General Research Institute—University Health Network, Toronto, Canada(1)
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

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

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
Added on: 12-03-2021

Mathematical prediction of neurodegenerative disease' progression

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

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

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

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

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

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

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

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

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)
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