In the literature: April 2026 highlights

Zattoni, Marta, et al. “In silico modelling of aortic annuloplasty: hemodynamic assessment through in vitro experiments and in vivo MRI.” Computer Methods and Programs in Biomedicine (2026): 109246.

Aortic annuloplasty (AA) is an innovative surgical technique for aortic root (AR) enlargement. It is performed by implanting sutures, bands, or rings, either externally or internally the AR, hereby reducing its diameter. This study evaluates the impact of AA approaches on AR hemodynamic by employing a porcine-specific workflow combining in vivo magnetic resonance imaging (MRI), in vitro experiments and in silico fluid-structure interaction (FSI) simulations investigating external single ring AA. CAD models of native and post-annuloplasty ARs were segmented from in vivo porcine MRI data and served as the basis for fabricating 3D-printed resin phantoms and implementing computational digital twins. The former were tested on a pulsatile flow-loop, whereas the latter were integrated in FSI simulations, with time-dependent boundary conditions based on the resultant experimental pressure waveforms. Additionally, a proof-of-concept validation of the in silico model against in vivo data is proposed. Computational results of the two cases were compared in terms of fluid velocity, vorticity, helicity, and wall shear stresses, providing a step towards understanding the complex interactions between the AR and blood flow dynamics. Results suggested that the presence of the ring increased the systolic jet flow and post-valve velocities (three-fold increase), reduced the backward, vortical flow during diastole (∼ 9% decrease), and induced modifications in bulk flow and wall shear stresses distribution. Furthermore, the development of an animal-specific digital twin of a post-AA AR represents a significant advancement in the field, providing a valuable tool for future research and for clinical applications to aid AA decision-making process.

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Davico, Giorgio, et al. “Exploring the effect of different neural strategies on the knee joint contact forces during walking in adults.” Scientific Reports (2026).

Identifying the different neural strategies that a person may adopt to perform simple locomotor tasks, such as walking, may enable the definition of rehabilitation plans aimed to reduce joint loads while preserving joint kinematics. Abnormal detrimental loading conditions that would likely reduce a person’s quality of life, especially among the elderly, could thus be prevented. Leveraging on previous works, we employed musculoskeletal models and biomechanical simulations (1) to explore how healthy young and elder adults recruit their muscles to perform a walking task, and (2) to determine whether the use of electromyography data to inform the simulations would allow to reduce the solution space. For the 15 tested subjects (10 young, 5 elderly), we estimated 10k sets of muscle and knee joint contact forces combining a static optimization approach with a Markov-chain Monte Carlo algorithm. We observed that the bands of solutions were narrower among the young adults than the elderly, showing how different neural strategies that prioritize the use of different muscles while ensuring the same kinematics are more likely to result in larger changes in the joint contact forces among older adults. In addition, while the neural strategies associated to the maximal knee contact forces were similar between populations, some differences emerged when analysing the strategies to minimise the knee loads. Last, the use of electromyography data allowed for a reduction of the solution band by up to 69%.

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Kowalski, Jérôme, et al. “Derivation and validation of compartment models: implications for dynamic imaging.” IEEE Transactions on Medical Imaging (2025).

Compartment models are mathematical representations of the transport of a chemical substance in the human body, assuming uniform concentration within each compartment, which corresponds to a distinct body part. Widely used in pharmaceutical and medical imaging through pharmacokinetic and tracer kinetic (TK) models, they are formulated as systems of time-dependent ordinary differential equations. However, these models do not account for the spatial dependence of physiological processes within individual compartments. This work aims to explore the physical interpretation of these models through mathematical derivation and to analyze their limitations. Three TK models are derived from more complex models regarding the physiological processes represented. The derivation introduces the hypotheses relevant to these processes. The most notable is the well-mixed hypothesis, resembling concentration homogeneity in the region of interest. The hypotheses are numerically tested by simulating the more complex model and evaluating the reduced models’ residuals. For a given tracer molecule, the validity of TK models is found to decrease as voxel size increases. This work proposes an algorithm to compute the maximum voxel size, above which the reduced models’ residuals exceed a chosen error threshold. For a 3 % error, voxel sizes larger than 250 $\upmu $ m are found critical for highly permeable vessels, while sizes smaller than 4 mm induce less than 3 % error in non-permeable vessels. Tested on literature-derived datasets, these findings identify diffusion as the underlying phenomenon leading to well-mixed compartments, and highlight the need for high spatial resolution imaging to improve TK parameters ground truth estimation.

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Frederickx, Nancy, et al. “Advancing the adoption of oncology Decision Support Tools in Europe: Insights from CAN. HEAL.” Frontiers in Digital Health 8: 1784519.

Effective cancer care increasingly depends on digital decision support tools (DSTs) to interpret complex clinical, molecular, and genomic data and guide personalised treatment decisions. However, the oncology DST (oncDST) landscape remains fragmented, with limited interoperability, inconsistent standards, and uneven clinical adoption across healthcare systems. This fragmentation hinders routine clinical use and impedes the demonstration of robust clinical benefit. To address these challenges, the CAN.HEAL consortium proposes the EU-oncDST digital framework, a conceptual, harmonised, interoperable, and modular architecture designed to integrate existing oncDSTs across Europe. Developed through consortium-wide consultations, an EU-level survey and comprehensive mapping of both public and private solutions, the framework provides a practical pathway for implementing interoperable oncDSTs while fostering stakeholder collaboration and innovation. It also promotes the improvement of data-driven precision oncology, highlighting the integration of artificial intelligence, enabling continuous patient follow-up, and supporting the development of a learning cancer system. At its core, the framework empowers Molecular Tumour Boards (MTBs) to operate efficiently at institutional, national, and European levels. By offering a harmonised, interoperable, and modular architecture designed to integrate clinical, molecular and genomic data, the framework strengthens evidence-based and personalised treatment recommendations. A phased action plan links MTB deployment to the implementation of oncDSTs. Early phases focus on piloting and validating oncDST use within MTBs, optimising patient-centred consultations, harmonising variant annotation, and enhancing clinical trial matching. Overall, the EU-oncDST digital framework aims to provide a practical and collaborative pathway to strengthen oncology decision-making and accelerate the translation of precision medicine into clinical benefit across Europe.

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Cruts, J. M. H., et al. “In silico analysis of patient specific coagulation and flow effects on fibrin clot formation.” Scientific Reports (2026).

The coagulation cascade, triggered by tissue factor (TF) exposure after endothelial injury, drives fibrin formation and may result in thrombotic events such as stroke. The mechanisms driving differences in thrombus extent among patients remain poorly understood, but interactions between patient-specific coagulation and local blood flow are thought to be critical. This study presents a unified workflow with an assay-calibrated, experimentally validated in silico model that links coagulation assays to flow-resolved simulations in patient-specific geometries. Plasma from ischemic stroke patients was analyzed with a thrombin generation (TG) assay, and a 0D computational model was fitted to TG curves to infer patient-specific coagulation parameters. These parameters were validated against thrombodynamics (TD) outcomes using 1D computational reaction–diffusion simulations. The framework was extended to 2D computational flow domains to assess the influence of shear rate, TF patch size and location, and geometric features such as stenosis. Finally, 3D carotid simulations combined patient-specific vascular geometries with plasma parameters. The 0D model reproduced TG data, while 1D simulations matched TD outcomes for clot size, fibrin growth, and thrombin wave speed. In 2D, fibrin formation was reduced at higher shear or smaller TF patches, and 3D simulations demonstrated the combined effect of flow, geometry, and plasma composition on fibrin formation. This approach provides a bridge from bench assays to hemodynamic contexts and offers a potential path toward individualized thrombotic risk assessment.

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Lai, Juntong, and Damien Lacroix. “A computational study of adiposity-associated factors in the inflammatory process of osteoarthritis.” Journal of Theoretical Biology (2026): 112429.

Chronic inflammation is a key factor in the degenerative changes of osteoarthritic joints. Obesity significantly raises the risk of osteoarthritis (OA), since excess body fat (adipose tissue) not only systemically increases the level of inflammation but also locally stimulates the inflammatory responses within osteoarthritic joints. In this context, physical activity is a practical approach in OA prevention and intervention, whereas current therapeutic strategies remain empirical and lack patient-specific tailoring. This makes it challenging to determine the appropriate dose and timing of physical activity therapy for diverse individuals. Building on our previous work of an adipokine-mediated inflammation model, this study aimed to analyse the effects of obesity and physical activity on OA inflammation by parameterising the inflammatory activities. In this model, five key mediator groups (pro- and anti-inflammatory cytokines, matrix metalloproteinases, adipokines and fibronectin fragments) were included. A global sensitivity analysis was conducted in the estimated parametric space and revealed the critical role of adiposity-associated factors in regulating inflammation. In addition, the inflammatory activities were simulated by tuning two adiposity-associated parameters, body mass index (BMI) and physical activity level (PAL), factoring in a simulated injury. The effectiveness of three physical activity intervention strategies was assessed by examining the inflammatory responses of the representative cases with four BMI profiles. A marked sensitivity to the timing (window period) of physical activity implementation was found. Results underscored the importance of accounting for both the adiposity level and the extent of tissue damage when designing intervention strategies of physical activity and optimising their timing for managing OA inflammation. This novel computational study analyses the adiposity-associated effects of physical activity on OA inflammation, illustrating that the effective window period of physical activity interventions varies from 0 to 15 months, depending on the level of adiposity and mechanical damage. Outcomes from the evaluation of the time window can strategically contribute to optimising physical activity interventions for the management of OA risk at an early stage.

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