In the literature: September 2026 highlights
Discover the recently published papers from our community!
Ganesan, Rajarajeswari, et al. “AI-driven hybrid framework for the generation of population-specific cardiovascular virtual cohorts.” Royal Society Open Science 13.8 (2026)
In silico clinical trials (ISCTs) offer a promising approach to accelerate the commercialization of cardiovascular devices. In this study, we present a novel synthetic data generation framework for generating clinically relevant cardiovascular geometries for testing the devices on a large population. A hybrid framework comprising a combination of artificial intelligence (AI) and statistical shape modelling (SSM) approach has been proposed for the generation of distinct cardiovascular geometries (aortic valves and coronary arteries) along with the pathologies. Synthetic aortic valves are generated for male and female populations, respectively. The evaluation of the anatomical features of these synthetic valves exhibits clear evidence of clinical realism. In addition, valves with unique anatomical variations are generated, and assessment of sub-population-specific features confirms the effectiveness of the framework. The framework further extends to the generation of another sub-population for synthetic coronary arteries. The study presents a widely applicable and scalable hybrid synthetic data generation framework for the generation of distinct cardiovascular geometries for specific populations. The results evidently show that the framework is highly suitable for the generation of anatomically precise and population-specific virtual cohorts to support future ISCTs.
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Riebel, Leto Luana, et al. “In silico optimization of regenerative cell therapy in the infarcted human ventricles to mitigate arrhythmic burden.” Stem Cell Reports 21.8 (2026)
Myocardial infarction remains a frequent cause of heart failure and mortality. Cell therapy has shown promise in regenerating the damaged tissue, but delivered cells may beat spontaneously and produce ventricular arrhythmias, hindering clinical application. Here, we conducted multiscale computer simulations of the electrical activity of the infarcted human ventricles including the cardiac conduction system to identify and mitigate pro-arrhythmic mechanisms following cell delivery. Firstly, our simulations show how arrhythmic risk increases from before to after cell injection and further during the first two weeks post-delivery. Secondly, we suggest that concurrently targeting the funny current, the inward rectifier potassium current, the sodium-potassium pump current, and the rapid delayed outward rectifier potassium current may reduce automaticity and re-entry while maximizing calcium transient amplitude and thus contractility. Our study demonstrates how modeling and simulation enables the design of anti-arrhythmic strategies to improve therapy safety while preserving efficacy.
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De Vecchi, Adelaide, et al. “Digital twins for predictive modelling of thrombosis and stroke risk: current approaches and future directions.” Thrombosis and Haemostasis (2026).
Thrombosis drives substantial global mortality across atrial fibrillation, venous thromboembolism, and atherosclerosis. However, clinical scores treat risk as a static variable and omit evolving comorbidities, functional biomarkers, anatomy, and treatment exposure, leading to misclassification and preventable events. This statement advances a unified scientific agenda for patient-specific digital twins that dynamically integrate multimodal longitudinal data with mechanistic insight to predict thrombogenesis risks. We position these digital twins as hybrid models anchored in physics and data-driven algorithms that can simulate disease progression and therapy. The goal of this approach is to refine stroke and bleeding estimation beyond current clinical rules. Continuous updating from imaging data, laboratory test results, wearables, and electronic health records supports dynamic risk trajectories and adaptive care pathways, facilitating continuous risk reassessment. This statement analyzes gaps in data quality, calibration, validation, and uncertainty quantification that presently limit the clinical translation of this technology. Research priorities are then proposed for multiscale thrombosis modelling, physics-informed learning, probabilistic forecasting, and regulatory-compliant data stewardship. Finally, we outline translation to in silico trials, regulatory alignment, and hospital workflows that link predictions to decisions. By articulating shared challenges across thrombosis-driven diseases and reframing risk as a time-varying measurable quantity, this statement lays a foundation for developing digital twin approaches that support a shift from population heuristics towards precise, timely thrombosis care. These advances are essential for translating digital twin technology from research to clinical practice, enabling dynamic risk prediction and personalized anticoagulation therapy.
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Carpenedo, Linda, and Luigi La Barbera. “An efficient framework for fully deformable finite element musculoskeletal spine modeling: development, sensitivity analysis, and validation.” Journal of Biomechanics (2026).
Computational musculoskeletal models are fundamental tools for investigating spinal biomechanics. However, currently available approaches still rely on major simplifications. In particular, fully deformable finite element (FE) models capable of quantifying load-sharing across all spinal structures rarely incorporate the action of muscles. As a consequence, detailed predictions of internal loads deriving from realistic loading conditions still lack. This study integrates an active muscle architecture into a lumbar FE model using a multi-stage dimensionality reduction strategy, combining Plackett-Burman screening, Latin Hypercube, and Farthest Point Sampling, to develop surrogate Generalized Linear Models. These models, built on a reduced set of simulations, efficiently predict spinal kinematics as a function of muscle forces. Using these surrogates, an optimization procedure identified the muscle activations that satisfy kinematic targets while minimizing an energy criterion. The results accurately captured physiological trends: abdominal activations and intradiscal pressure increased with trunk flexion. Compressive forces (CFs) at L4-L5 in standing were 74 % of body weight, within the 58 %-98 % literature range. The model’s full deformability also allowed for a detailed load-sharing assessment. For instance, in standing, the disc consistently carried over 44 % of CFs, followed by facet capsules (FCLs) contributing up to 26 %. During flexion, disc load nearly doubled, while FCLs became markedly tensioned (reaching 49 % at 20°). Also, shear forces (anterior 31 % to posterior 17 % of body weight) matched literature values, with the disc providing the primary contribution. These findings confirm the model’s ability to predict complex loading patterns while reducing the computational cost of musculoskeletal FE simulations.
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Marino, Michele, and Alessandro Mastrofini. “ERC Consolidator Grant VISTA: rethinking vascular inflammation through computational engineering.” European Heart Journal, Volume 47, Issue 32, 21 August 2026.
Cardiovascular medicine has entered an unusual paradox: our ability to treat vascular disease has never been greater, yet our ability to predict who will benefit remains limited. The scale of cardiovascular disease makes this challenge particularly striking. Since 1990, the number of people living with cardiovascular diseases (CVD) has nearly doubled, now exceeding 600 million worldwide and making it the leading cause of death globally. In the European Union alone, cardiovascular disease imposes an economic burden exceeding €280 billion annually.
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Perri, Letizia Maria, et al. “In Silico Modeling and Validation of Post-Dilation in TAVI Patients.” Annals of Biomedical Engineering (2026): 1-15.
The purpose of this study is to develop and validate a balloon post-dilation model into patient-specific transcatheter aortic valve implantation (TAVI) simulations, addressing the current lack of numerical models capable of reproducing this procedural step.
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