Articles tagged with Biological Models
A study published in Cancer Cell identified a six-gene signature in microscopic colorectal cancer liver metastases that may help predict recurrence after treatment. The findings suggest these tiny tumor deposits could serve as a tissue-based marker of residual cancer cells, recurrence risk and chemotherapy resistance.
Scientists developed a new population model, APODEMUS, to predict pesticide effects on animal populations. The model simulates complex agricultural landscapes and foraging scenarios, tracking energy conversion and exposure to pesticides.
A decade of canine research suggests that genetically engineered Beagles could be a complementary and welfare-conscious model for autism treatments. The study gathers findings on the behavior and traits of these dogs in relation to human autism, including signs of treatment with oxytocin and psychedelics.
A comprehensive review of breast cancer risk models in women with a family history found that none are highly accurate at identifying which women will develop the disease. The BOADICEA model showed balanced performance, while others had significant limitations, emphasizing the need for continued improvement.
Researchers identified two distinct subtypes of autism based on brain connectivity patterns, one characterized by reduced connectivity linked to synaptic pathways and the other by increased connectivity associated with immune-related systems. The study's findings offer a biological basis for personalized care and support.
Researchers developed a viable homozygous CHD8 mouse model, showing that stronger mutations can dramatically alter male–female autism patterns. The study revealed pronounced autism-related abnormalities in both sexes with severe mutations.
Researchers compared two prevalent MS models to identify specific scenarios where one model is better suited for studying myelin loss and regeneration. The study analyzed preclinical models alongside human MS tissue samples, revealing genetic changes and insights into disease progression.
Researchers discovered that strokes cause a chain reaction within the brain, leading to neuronal cell death. They found that blocking collagen production can prevent this damage and even restore motor function in paralyzed monkeys. The new drug KDS12025 reduces hydrogen peroxide levels and prevents the entire process from being triggered.
A new mathematical model called LFSPRO was developed to predict the risk of Li-Fraumeni Syndrome. The model provides a more quantitative risk estimate for individuals who would benefit from testing but do not meet established criteria.
Terrence Sejnowski receives Scientific Breakthrough Award for his foundational development of Boltzmann machines, providing the architectural bedrock for deep learning and generative AI. His work has had a profound impact on modern artificial intelligence and tools like ChatGPT.
Researchers propose a novel brain architecture for efficient processing, integrating parallel cortical and subcortical pathways. This approach may improve decision-making tasks, suggesting current AI models are missing key brain function principles.
A new study uses an AI model to predict type 2 diabetes risk based on behavioral and psychosocial information. The digital twin model found that loneliness, insomnia, and poor mental health substantially raise a person's future risk of developing the disease.
Salk Institute researchers have developed a new biological platform for studying mitochondrial DNA in human physiology, adaptation, and therapeutic development. The platform allows scientists to investigate mitochondrial DNA variation in health and disease, enabling therapeutic innovation for mitochondrial disorders.
Researchers developed an immune-capable 'cervix-on-a-Chip' model to study sexually transmitted infections (STIs), enabling a more accurate understanding of these conditions and the potential for better treatments. The model replicated key aspects of human biology, including the cervical microenvironment, microbiomes, and immune system.
A team of researchers, led by Bistra Iordanova and Liang Zhan, are developing multiscale models of brain metabolism to predict cognitive decline and dementia. They will analyze data from brain imaging, blood flow, and neural activity to identify metabolic changes that affect brain function in aging.
Researchers used machine learning techniques to compress a large model of the visual cortex, creating smaller versions that predict neural responses with high accuracy. The compact models revealed specific computational patterns in how neurons detect important features, offering insights into how visual information is processed.
A new study reveals that astrocytes actively participate in motor-learning circuit rewiring by eliminating synapses in the striatum. The research identifies MEGF10 as a key molecular mediator of this process, which is regulated by dopamine signaling and neural activity.
Researchers propose a new framework to detect chronic diseases earlier by tracking individual biological changes. Advances in technology and AI enable the analysis of complex patterns from wearable devices and biological samples.
A study by RIKEN researchers identifies a MYCN-driven biomarker that predicts the risk of liver cancer. The biomarker, known as the MYCN niche score, uses machine learning to analyze gene expression patterns and indicates whether a tumor-free liver is at high risk for developing tumors.
Researchers developed a new computational approach to predict chemotherapy response in triple-negative breast cancer, outperforming current methods. The TmS biomarker accurately sorts patients into those with favorable or poor prognosis, highlighting its potential as an effective starting point for patient stratification.
Researchers developed a new theoretical framework that links individual animal movements to population dynamics across space and time. The range-resident logistic model incorporates interactions between multiple animals, providing a more accurate prediction of population sizes and helping inform real-world conservation recommendations.
A study published in Science of The Total Environment found that e-cigarette exposure alters gut microbiota composition and affects neurobehavior in zebrafish. The researchers observed disruptions in the gut microbiome, with reduced microbial network stability and altered community composition, suggesting potential health risks.
Steatotic liver disease can be precisely assessed using three-dimensional ultrafast vascular ultrasound. The technology visualizes subtle microvascular changes, enabling real-time monitoring of disease progression and therapeutic response.
A machine-learning-based approach has been developed to map real-time tumor metabolism in brain cancer patients, helping doctors discover effective treatment strategies. The digital twin uses patient data to calculate metabolic flux, predicting which treatments are likely to work for individual cases of glioma.
The study created a critical framework for understanding the architecture of the genome and its association with gene function in cells. The 4DN Consortium integrated data from over a dozen techniques to compile an extensive catalogue of looping interactions between genes and regulatory elements.
A new computational model of the brain based on biology and physiology learned a simple visual category learning task as well as lab animals, discovering counterintuitive activity in neurons. The model produced naturalistic dynamics and learning without training data, enabling researchers to identify new insights into brain function an...
Early gastric cancer cells become self-sufficient by producing WNT7B, creating a self-sustaining loop and activating WNT signaling internally. This mechanism is triggered by MAPK signaling activation and has been validated in genetically engineered mouse models and human patient-derived organoids.
Researchers create powerful computational tools, sCCIgen and QuadST, to map how cells communicate in diseases like Alzheimer's and cancer. These tools provide a controlled environment to test methods for detecting cell communication, enabling the discovery of new mechanisms and therapies.
A new study found remarkable variation in how populations evolve in variable environments, with some cases benefiting from changes and others being hindered. The research has implications for understanding evolution and adapting to climate change, as well as informing AI and machine learning.
The new LimbNET platform enables direct simulation of complex biological growth processes, empowering researchers to test and compare hypotheses. The project aims to enhance collaboration, transparency, and knowledge building in the limb development community.
Recent genetic advancements have created a biodiversity library, offering untapped opportunities for solving global challenges. Researchers are exploring unconventional models, such as electric eels, octopi, and bacteria, to develop new therapeutics and tackle environmental issues.
A new AI model uses machine learning to predict drug toxicity in humans by identifying biological differences between cells, mice, and humans. The model improved predictive power over existing state-of-the-art models and demonstrated practicality in predicting market withdrawal due to toxicity.
Researchers at ETH Zurich have successfully produced muscle tissue using a new biofabrication system called G-FLight in microgravity. The process enables rapid production of viable muscle constructs with similar cell viability and muscle fibers as those printed under gravity.
Researchers from Ateneo de Manila University have identified key proteins produced by Helicobacter pylori that can trigger a strong immune response. By analyzing these proteins using immunoinformatics, the team has pinpointed potential vaccine targets to prevent stomach ulcers and cancer.
Scientists at RIKEN Center for Brain Science found that fruit flies use separate circuits to compute pleasant and unpleasant odors, challenging the idea that 'good' is the opposite of 'bad'. The discovery may contribute to a better understanding of human brain's flavor appreciation mechanisms.
Biomedical engineers at Duke University developed a platform combining automated wet lab techniques and AI to design nanoparticles for drug delivery. The TuNa-AI platform resulted in a 42.9% increase in successful nanoparticle formation compared to standard approaches.
A new model developed by University of Illinois researchers can accurately predict fluctuations in the relative abundance of tree species in a forest. The model uses genomic data and a one-time tree count to forecast changes in forest diversity, which is crucial for understanding how forests will change in the future.
A new open-source AI tool, ProRNA3D-single, has significantly increased accuracy in predicting and visualizing protein-RNA complex structures. This method can generate finely detailed images of molecules in 3D, enabling drug developers to design treatments by analyzing where viruses attach to human proteins.
Researchers developed a mathematical model that replicates the muscle movements of the esophagus during swallowing and reveals insights into esophageal motility disorders. The model includes signaling from the brain and nerves, as well as muscle contractions and relaxations, and can simulate various disorders.
The European consortium, funded by €4.5M, will recruit and train 15 PhD researchers to develop new models and methods for understanding complex biological systems. The network, coordinated by the University of Edinburgh, aims to create a framework grounded in physics that can be applied systematically.
Researchers from FAU and the Sensing Institute created a detailed computer model of the mouse brain's vasculature, simulating how brain blood vessels respond to hemodynamics and vasodynamics. The model shows that transitional vessels play a critical role in regulating flow and protecting the brain during increased activity.
Researchers at Penn State have simulated a new class of protein misfolding using atomic-scale models, revealing a type of entanglement that disrupts protein function and persists in cells. The findings support the existence of this long-lasting type of misfolding, which is thought to contribute to aging and disease.
Researchers have identified a hidden molecular mechanism involving two proteins that allows tumors to resist treatment. A new gelatin-based nanoparticle has been developed to shut down both proteins simultaneously, showing promising results in early studies with mice.
A new computational method combines 4D flow MRI, CFD, and data assimilation to estimate blood flow in brain aneurysms with greater accuracy and efficiency. The approach focuses on the aneurysm region, reducing computational cost while improving flow estimation.
Researchers at OIST introduce a mouse motion capture method using marker-based approach to track high-quality data on complex movements. The method avoids pitfalls associated with smaller animals, enabling detailed studies of neuroscientific and physiological foundations of mouse movement.
Researchers at Cold Spring Harbor Laboratory have developed a unified theory for gauge freedoms in models of biological sequences, which could revolutionize fields like plant breeding and drug development. The new approach provides efficient formulas for scientists to interpret research results with greater confidence.
Terrence Sejnowski has been elected to the Royal Society and the American Philosophical Society, a testament to his groundbreaking contributions to computational neuroscience. His pioneering work includes inventing the Boltzmann machine and NETtalk program, which revolutionized artificial neural networks and speech recognition.
Scientists at the University of Michigan have developed an improved model system to study embryo development, revealing insights into critical periods of formation. The new system allows for the prolonged culture of gastruloids, enabling researchers to visualize key processes such as mesoderm migration and gene expression.
The Mass Query Language (MassQL) tool empowers scientists to uncover previously unknown pollutants in massive chemical datasets. It has identified toxic compounds hidden in plain sight, including organophosphate esters and chemicals from breaking down over time.
The 4th Annual MPS World Summit brings together over 1,500 international experts to explore advancements in Microphysiological Systems (MPS) research. This event focuses on drug and chemical safety, disease modeling, and regulatory testing.
A new study reveals promising progress toward predicting how patients with major depressive disorder will respond to antidepressant medications. Brain connectivity patterns were found to significantly improve predictions of treatment response across two large, independent clinical trials.
Researchers at Texas Tech University Health Sciences Center have received a $1.94 million grant to study inhibitors that target peripheral neuropathic pain. The project aims to develop novel non-opioid and non-addicting therapies capable of effectively managing chronic pain.
Insilico Medicine's Pharma.AI Day 2025 will showcase the latest AI breakthroughs and updates, including precision target discovery engine PandaOmics and generative biologics platform Generative Biologics. The company aims to accelerate drug discovery and advance life sciences research with its proprietary platform.
The Vegetation Photosynthesis Model (VPM) 3.0 has been introduced, delivering a major leap in the accuracy of global gross primary production (GPP) estimates. The model enhances our understanding of terrestrial carbon dynamics and provides a powerful tool for climate change studies and ecosystem monitoring.
A novel approach combines large language models and quantum computing to predict Salmonella antimicrobial resistance. The SARPLLM algorithm outperforms other models in prediction accuracy.
A Lehigh University researcher is developing a new platform to enhance flu forecasting through a human judgment temporal forecast system, which aims to improve evidence-based public health decision making. The system will collect unbiased opinions and forecasts from public health experts and the general public.
The researchers propose a novel defense algorithm, Wavelet-Based Adversarial Training (WBAD), to protect medical digital twins. The two-stage defense mechanism achieves 98% accuracy in breast cancer prediction, even under adversarial attacks, providing a comprehensive and effective defense against cyberattacks.
Marine turtles' shells exhibit complex trade-offs between toughness, stiffness and flexibility to protect against predators while optimizing movement. The shell's design accommodates demands of both survival and efficient locomotion in aquatic habitats.
Researchers warn of misunderstandings in handling AI models, highlighting conditions for confidence in predictions. Explainability methods are crucial to understand algorithmic decisions, but interpreting results requires caution due to AI limitations.
Researchers discovered that mammalian membranes have drastically different phospholipid abundances between their two leaflets, contradicting a major assumption of cell biology. The asymmetry is enabled by cholesterol's unique properties, which act as a buffer to redistribute between the leaflets and maintain robust barriers.