New open-access platform helps researchers understand genetic changes
Every person carries small differences in their DNA. Some of these differences, often called variants, do very little. Others can change the amino-acid sequence of a protein, which may affect how that protein is built, folded, moved inside the cell, or how well it does its job. Because proteins carry out many of the body’s essential functions, even a small change can sometimes have important biological or medical consequences.
The challenge is figuring out which changes matter. Researchers often need to weigh many different clues, including whether a variant is rare or common in human populations, whether it has been reported in people with disease, whether it falls in an important or highly conserved part of the protein, whether it may change the protein’s shape or nearby interactions, what experiments have measured, and what the scientific literature says. Each type of evidence has strengths and caveats, and no single source is usually enough on its own. In practice, this often means moving between many separate databases and analysis tools, then manually piecing together a fragmented trail of evidence to decide whether a variant is likely to be harmless, disruptive, or still uncertain.
CATVariant was created to make that process easier and more informative. The platform uses automated data mining to retrieve and organize variant-related evidence from genetic variant databases, protein resources, population datasets, experimental assay collections, disease and pharmacology knowledge bases, and the scientific literature. It then goes further by mapping variants onto the protein sequence and available protein models, comparing them with known functional regions and nearby reported changes, and analyzing broader patterns such as mutation-sensitive regions, structural clusters, and residue connections across the protein. The result is an interactive report that helps users move from a broad protein-level view to detailed review of individual variants without manually stitching the evidence together across multiple resources.
CATVariant is especially useful when direct laboratory or clinical evidence is limited, which is true for many variants. The platform brings together a broad set of computational predictors, with 12 directly surfaced predictor or effect-estimation inputs, and interprets them alongside the rest of the evidence rather than in isolation. These models draw on different kinds of biological signal, including evolutionary conservation, protein sequence patterns, biochemical context, protein shape, and RNA splicing. Because the models capture different signals, CATVariant lets users see where the computational evidence agrees, where it conflicts, and how those predictions line up with structural, population, experimental, and literature evidence.
In short, CATVariant is designed to help researchers turn scattered clues into testable ideas about how a genetic change might affect protein function. The platform is open access and free to use. Visit catvariant.com to learn more.
Emilie Roncali was a Keynote Speaker at the Virtual Imaging Trials in Medicine Workshop 2026
April 2026 — Associate Director for Computational Biomedicine, Emilie Roncali recently gave a keynote on theranostics digital twins and in silico models at the Virtual Imaging Trials in Medicine workshop 2026. This was a two day online gathering of the Virtual Imaging Trials in Medicine Community for virtual discussions and scientific exchange in the world of in-silico trials, digital twins, and quantitative imaging.
In her talk, Roncali introduced core principles of theranostics digital twins, stressing the difference with in silico models. She discussed examples related to nuclear medicine and detailed some of her research on liver digital twins for liver cancer treatment that integrate computational fluid dynamics and radiation physics modeling to optimize treatment. She briefly discussed some ethical considerations to build diverse, equitable, and accessible digital twins.
Cellular mechanisms of radiation-induced myocyte dysfunction: effects on calcium handling, ion channel regulation and mitochondrial energetics
April 2026
Abstract
Ionizing radiation induces a range of cellular responses in cardiomyocytes that vary with the dose, duration of exposure and metabolic state. Although historically attributed to microvascular injury and fibrosis, radiation-induced cardiac dysfunction is now recognized to originate from direct perturbations of myocyte calcium handling, ion channel regulation and mitochondrial energetics. Low to moderate radiation doses generate sustained reactive oxygen species (ROS) that activate oxidation-dependent calcium/calmodulin-dependent protein kinase II (CaMKII) signalling, leading to disrupted sarcoplasmic reticulum calcium cycling, altered sodium and calcium currents and increased susceptibility to early and delayed after-depolarizations. Mitochondrial structural and energetic instability further amplifies ROS–CaMKII feedback, promoting a pro-arrhythmic electrophysiological substrate. High-dose radiation exposures, such as those used in cardiac stereotactic body radiotherapy, lead to a distinct electrical reprogramming phenotype characterized by coordinated upregulation of sodium channels, calcium channels, potassium channels and gap junction proteins. The resulting emergent effects are to enhance conduction velocity and electrical homogeneity that together provide a mechanistic explanation for the rapid anti-arrhythmic effects observed clinically, even independent of fibrosis. Across the radiation dose spectrum, the mitochondria serve as key integrators of redox stress and calcium overload, shaping the transition from reversible signalling alterations to persistent remodelling. This review synthesizes mechanistic patterns underlying radiation-induced myocyte dysfunction, highlights unresolved discrepancies across experimental models and discusses how computational modelling might be the ideal tool to predict optimal therapeutic radiation delivery while mitigating long-term cardiotoxicity.
Natural language processing of biomedical text to map and prioritize protein–disease associations in HFpEF
March 2026
Abstract
The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in cardiovascular disease (CVD) is hindered by a vast and fragmented biomedical literature, which now exceeds 38 million publications indexed in PubMed. To address the central challenge of navigating and synthesizing a huge fragmented biomedical literature base, we applied our validated machine learning–based text-mining algorithm containing natural language processing (NLP) and incorporated this into a ValIdated Text-mining using Advanced Language model (VITAL) as a complementary framework.
Society welcomes inaugural Editors-in-Chief for The Journal of Precision Medicine: Health and Disease and The Journal of Nutritional Physiology
Following the announcement of The Physiological Society’s partnership with Elsevier to launch a new journal, we are delighted to introduce the Editor-in-Chief (Colleen E. Clancy) and Deputy Editor in Chief (Vladimir Yarov-Yarovoy) of The Journal of Precision Medicine: Health and Disease.