In the literature: January 2026 highlights

Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discovery, and virtual trials. This review categorises these methods by context of use, critically appraises their strengths and limitations, and identifies barriers to translation, highlighting key opportunities and ongoing challenges in advancing computational strategies for rare disease research.

Glycemic control (GC) is hard to implement safely in intensive care due to patient variability. GC has been wrongly blamed for increased hypoglycemic risk instead of protocol design, limiting its adoption. Stochastic TARgeted (STAR) is a model-based, patient-specific, risk-based GC framework modulating intravenous (IV) insulin and nutrition, accounting for both inter- and intra-patient variability. This study assesses STAR GC’s ability to provide safe and effective control across a large cohort.

Introduced as an alternative to open-heart surgery for elderly patients, Transcatheter Aortic Valve Implantation (TAVI) has recently been extended to younger patients due to comparable performance with the gold-standard. However, the long-term durability of the bio-prosthetic TAVI valves is limited by Structural Valve Deterioration (SVD), an inevitable degenerative process whose pathogenesis is still unclear. In this study, we aim to computationally investigate a possible relationship between aortic hemodynamics and SVD development. To this aim, we collect data from twelve patients with and without SVD at long-term follow-up exams. Starting from pre-operative clinical images, we build early post-operative virtual geometries and perform Computational Fluid Dynamics simulations by prescribing a personalized flow rate based on Echo Doppler data. In order to identify a premature onset of SVD, we propose three computational hemodynamic indices: Wall Damage Index (), Leaflet Delamination Index (), and Leaflet Permeability Index (). Additionally, to each index we associate a score and, using the Wilcoxon rank-sum test, we find that each score individually shows a statistically greater median value in the SVD sub-population (: , : , : ). Finally, we define a synthetic scoring system that clearly separates between SVD and non-SVD patients. Our results suggest that aortic hemodynamics may drive a premature onset of SVD, and the synthetic score could potentially assist clinicians in a patient-specific planning of follow-up exams to closely monitor those patients at high SVD risk.

A Port-a-cath is an implantable device placed under the skin to draw blood or deliver treatments such as intravenous fluids, medications, or transfusions. Patients often worry about accidentally hitting the device or nearby tissues, as it slightly protrudes from the skin. This concern can limit participation in daily activities, including sports, affecting quality of life. In this study, we propose a framework to investigate the risk of accidental tissue trauma by simulating a ball impact in the area surrounding an implanted Port-a-cath using finite element analysis. The simulation revealed increased stress in tissues surrounding the device (approximately five times higher in both the skin and the muscle when the device is implanted), suggesting a potential risk for muscle injury upon impact. These findings highlight the need for awareness regarding external forces acting on the Port-a-cath region during physical activity. To our knowledge, this is the first study using biomechanical modeling to explore trauma risk associated with Port-a-cath impacts. This study represents the first attempt to elucidate this risk in the literature. Overall, this study contributes to improving patient safety and quality of life by providing a quantitative understanding of the mechanical risks associated with implanted Port-a-caths.

Thoracic endovascular aortic repair (TEVAR) is the standard of care for thoracic aortic pathologies, and its clinical success is related to the choice of stent-grafts (SGs). In this study, we conducted a comprehensive assessment of four commercial SGs (Valiant Captivia (VC), Terumo RelayPro Bare Stent (TBS), Cook Zenith Alpha (CZA), and Gore CTAG (CTAG)) to evaluate their mechanical performance in idealised and patient-specific conditions. High-fidelity finite element models were developed and validated against experimental tests and in vitro TEVAR procedures in 3D-printed rigid phantoms. The validation showed strong agreement between simulations and experiments (average error < 5%).

Then, the SGs were virtually deployed in two aortic models to investigate device-wall interaction through geometrical and mechanical parameters. A greater metal density led to increased graft apposition (up to 94%) and increased radial forces (up to 354 N vs 116N). Conversely, sparser metal structures produced lower but more localised stress regions: maximum values of 0.25 MPa versus 0.49 MPa with denser metal. Higher stresses may contribute to improved device fixation and, when associated with greater apposition, may reduce the risk of endoleak. Nevertheless, high stresses could potentially induce long-term vascular remodelling.

These results underscore the influence of SG’s design on TEVAR outcomes and support the integration of validated computational simulations into pre-operative planning. The SG performance varied across patient anatomies: this study highlights the importance of personalized device selection and establishes a foundation for using in silico methods to optimize TEVAR strategies and mitigate procedural risks.