The Achilles tendon is the thickest tendon in the human body, and it is composed of three subtendons connected to the calf muscles (soleus, medial and lateral gastrocnemius). This study aimed to evaluate the importance of explicitly modeling subtendons and collagen fiber orientation on the predicted strain distribution within tendon tissue during mechanical loading. Magnetic resonance images from seven subjects were used to create subject-specific finite element models. The segmented tendons were modeled in two ways: the entire tendon as one structure, then dividing it into the three subtendons. A fiber-reinforced poro-visco-hyperelastic material model was used to describe the mechanical response. The collagen fibers were first oriented in the proximal–distal direction, then twisted following the subtendon orientation. Principal strains were analyzed, assessing the impact of subject-specific geometry on strain magnitudes, localization and distributions. Average strains were similar across subjects (coefficient of variation = 18
To develop a fully coupled, patient-specific fluid–structure interaction (FSI) framework for quantitative assessment of post–transcatheter aortic valve implantation (TAVI) hemodynamics and valve biomechanics, and to compare selected simulation-derived hemodynamic indices with post-procedural echocardiographic measurements. Patient-specific geometries were reconstructed from pre-operative computed tomography angiography in five subjects treated with SAPIEN 3 Ultra (S3) devices. Structural TAVI deployment was simulated using Abaqus/Explicit and subsequently coupled with FlowVision for performing a two-way post-TAVI FSI analysis. Personalized boundary conditions were derived from clinical measurements, including heart rate, blood pressure, and echocardiographic flow data. Predicted peak velocity, effective orifice area (EOA), and transvalvular pressure gradients (TPG) were quantitatively compared with post-procedural echocardiography using empirical cumulative distribution functions and area-based error metrics. The FSI framework reproduced realistic leaflet kinematics and patient-specific flow patterns, highlighting marked inter-patient variability despite identical device types. Average of predicted peak systolic velocity (2.66 ± 0.59 m/s) and TPG (22 ± 10.7 mmHg) showed good agreement with echocardiographic measurements as the area metric was below 10
Premature ventricular complexes (PVCs) are common cardiac arrhythmias that can lead to cardiomyopathy when frequent. Post-extrasystolic potentiation (PESP), which is the transient increase in contractility following a PVC, may serve as a predictive marker for heart failure risk; yet, the underlying calcium-mediated mechanisms and their relative contribution compared to loading conditions remain poorly understood. We integrated a mechanochemical model coupling intracellular calcium dynamics to sarcomere mechanics within the CircAdapt closed-loop cardiovascular framework. A novel calcium source model incorporating the force–interval relationship was calibrated using experimental canine data. We simulated single PVCs across varying coupling intervals and systematically investigated the contributions of calcium dynamics versus loading conditions to PESP, quantified as changes in systolic blood pressure (∆SBP), maximum rate of left ventricular pressure rise (∆max(dPLv/dt)), and left ventricular ejection fraction (∆LVEF). The calcium-based force–interval relationship reproduced experimental mechanical restitution curves with high accuracy (RMSE 9.59 ± 0.08
Finite element (FE) models are used to study spinal cord biomechanics and injury mechanisms. However, most existing models incorporate full vertebral geometry, substantially increasing computational cost and limiting the ability to perform large-scale parametric studies or apply modeling in clinically feasible timelines. In central cord syndrome, movement of the vertebrae is important while vertebral deformation contributes minimally to cord biomechanics. This study tested whether modeling the spinal canal geometry alone (versus full vertebral geometry) is sufficient to capture spinal cord stresses and strains under extension loading, offering a computationally efficient alternative to high-fidelity subject-specific models. Two FE models were developed: (1) a high-fidelity model including vertebrae, discs, ligaments, and neurological tissues, and (2) a computationally efficient (CE) model retaining only the spinal canal, with boundary conditions applied to represent the kinematics of each vertebra. Tissue-level stress and strain distributions, and computational performance were evaluated under extension. The CE model reproduced whole-cord and tissue-level stresses within 15
Intimal hyperplasia is a pathological mechanism underlying arterial growth and remodeling in numerous vascular diseases, in which key biological processes are regulated by mechanical fields such as wall shear stress (WSS) and circumferential stress within the artery walls. In the present study, we hypothesize that chronic exposure to hand-arm vibrations (HAV) contributes to the development of intimal hyperplasia in the digital artery through vibration-induced reductions in WSS. Accordingly, a mechanobiological framework coupling an agent-based model (ABM) with a finite element model (FEM) was developed. The ABM captures the hemodynamics-driven and mechanoregulated cellular and molecular mechanisms involved in this pathology, including mediator secretion by endothelial and smooth muscle cells (SMCs), SMCs proliferation and migration, and extracellular matrix (ECM) synthesis and degradation. WSS values, reflecting the presence or absence of vibration during long-term working conditions, were used as model inputs. Circumferential stresses were computed using the FEM, which describes the mechanical behavior of the digital artery. The model parameters were identified from our experimental findings and literature data. Over a 5 year period of vibration exposure (4 h/day), our simulations revealed that the constitutive law of the arterial walls had a negligible impact on the progression of stenosis. Moreover, reductions in circumferential stress associated with arterial wall thickening led to ECM degradation in the media layer due to an increase in the production of matrix metalloproteinase-2. This mechanobiological framework provides a computational tool for estimating vibration-induced stenosis rates and can be extended to study intimal hyperplasia in diverse biomechanical and pathological contexts.
Existing in vitro and numerical studies lack consensus regarding whether and how coronary arteries should be incorporated. This study aims to systematically investigate the effects of coronary artery outlets on the hemodynamic environment within the native sinus and neo-sinus after transcatheter aortic valve implantation (TAVI). Three idealized aortic root models (without coronaries, single coronary, and bilateral coronaries) were fabricated. A VENUS self-expanding valve was implanted at five depths (0 mm, ± 5 mm and ± 10 mm). A pulsatile in vitro flow platform combined with particle image velocimetry (PIV) was applied to quantify velocity fields, vorticity, and particle washout. Correlations between implantation depth and hemodynamic parameters were further assessed. In control models, mean native sinus velocity without coronaries was 0.58 ± 0.49 cm/s and decreased further after TAVI. Introducing a single coronary increased mean velocity to 1.34 ± 0.95 cm/s and generated high-velocity jets (> 10 cm/s) near the ostium; bilateral coronaries produced comparable effects. Vorticity decreased in all post-TAVI configurations. Particle washout analysis demonstrated pronounced stasis without coronary flow but markedly improved clearance when coronary inflow was present. With coronary flow, particle washout was markedly enhanced compared with the no-coronary condition, but did not vary monotonically with implantation depth; instead, it appeared to be governed by the combined effects of local flow environment. Under the present conditions, coronary flow substantially increased velocity magnitude, vorticity, and particle washout within the corresponding native sinus and neo-sinus after self-expanding valve implantation. Neglecting coronary outlets may lead to a substantial underestimation of sinus flow velocity and washout. However, when evaluating the hemodynamics of an individual coronary sinus, inclusion of its corresponding coronary artery alone is likely to be sufficient to capture the essential flow characteristics.
The sacroiliac joints (SIJs) and pubic symphysis (PS) form a ‘pelvic ring’ that plays a critical role in load transfer between the spine and lower extremities. Direct in vivo measurement of pelvic joint loading remains challenging, and existing musculoskeletal models often simplify or exclude pubic joint contributions. This study developed a female pelvis musculoskeletal model incorporating both SIJs and PS, integrated with a personalisation framework including: (1) a pelvis shape-scaling workflow based on a female statistical shape model; and (2) an inertia estimation workflow using 3D full-body scans. Kinematic and kinetic data were collected from eight healthy female participants during bilateral standing, single-leg standing and walking. Compressive and superior–inferior shear loads at the SIJs and PS were estimated using inverse dynamics and static optimisation. The model produced physiologically plausible joint loads consistent with previous studies. During bilateral standing, pubic joint loads were minimal, and SIJ loads showed minor asymmetries. Single-leg standing induced SIJ tensile (1.9 N/kg), pubic compression (2.2 N/kg), and superior–inferior shearing at the support-side SIJ (4 N/kg). During walking, pubic joint loads were closely related to SIJ loading patterns and appeared to facilitate load transmission from the stance-side SIJ to the swing-side SIJ. This pelvis musculoskeletal model provides a feasible tool for investigating SIJ and PS reaction forces in females, enabling a more physiologically realistic assessment of pelvic joint biomechanics than previously available models. The proposed framework is adaptable to other populations and supports future research on pelvic pain mechanisms, pregnancy-related adaptations, and sex-specific musculoskeletal disorders.
Right ventricular (RV) dysfunction due to pulmonary and tricuspid valve regurgitation remains understudied despite its critical role in adverse cardiac outcomes. We present a biventricular computational model that integrates regurgitant valves in the RV with a kinematic growth framework. Updated reference configurations are used to allow saturated growth in each growth cycle. Acute regurgitation scenarios and long-term adaptation are modelled to quantify structural and functional adaptations in the RV and their further impacts on left ventricular (LV) performance. Results demonstrate that persistent regurgitation drives dominant eccentric growth in the RV, leading to severe cavity dilation, septal displacement, and impaired LV filling and systolic function. Simulations incorporating both eccentric and concentric growth reveal a limited compensatory role for concentric thickening, even under severe volume overload. The simulated haemodynamic and functional responses are broadly consistent with clinical observations and capture clinically plausible trajectories of RV growth under sustained regurgitation. These findings suggest that biomechanical modelling of myocardial adaptation could provide mechanistic insights into RV adaptation under severe valve regurgitation, and may support clinical decision-making regarding RV failure once fully validated. Future work should focus on validating myocardial growth laws using experimental and clinical data, and extending the framework to patient-specific scenarios for predictive modelling of RV dysfunction due to valve regurgitation.
Aortic dissection is a life-threatening pathology characterized by the progressive delamination of adjacent lamellar units within the aortic media. Because this internal damage propagates predominantly along the radial direction of the arterial wall, radial tensile testing has emerged as a particularly relevant experimental configuration to reproduce the mechanical conditions associated with dissection. Several experimental studies have reported the mechanical response of arterial tissues under radial tension, highlighting pronounced viscoelasticity, fluid-driven effects, and progressive damage. However, despite these advances, a coherent constitutive framework capable of reproducing the full mechanical response of arterial tissue subjected to radial tensile loading is still lacking. In this study, we propose a computational model specifically designed to describe arterial tissue behavior under radial tensile testing. The model combines a biphasic formulation, accounting for fluid–solid interactions, with a reactive viscoelastic damage framework to capture time-dependent response and progressive mechanical degradation. Implemented within the FEBio environment, the model is calibrated using experimental radial tensile tests on aortic tissue. The proposed formulation accurately reproduces key experimental features, including stress relaxation, nonlinear stiffening, and damage progression. These results demonstrate that the model provides a physically consistent description of arterial tissue behavior under radial tension and represents a relevant tool for investigating the mechanical mechanisms preceding aortic dissection.
The mammalian cerebellum is a densely folded structure composed of lobules separated by deep fissures, while individual lobules display striking diversity in shape and size. Although multicellular processes such as granule cell proliferation and migration drive cerebellar morphogenesis, mechanical mechanisms that generate diverse lobular morphologies after initial folding remain poorly understood. Spatially heterogeneous cortical growth arising from multicellular dynamics is considered critical for the formation of characteristic lobular morphologies. In this study, we employed mathematical modeling and computer simulations to investigate how heterogeneous cortical growth influences cerebellar lobular morphology. We developed a mathematical model of cerebellar cortical growth based on continuum mechanics and simulated lobular deformation under spatially heterogeneous cortical growth using the finite element method. Our simulations indicated that heterogeneous cortical growth modulates the rates of increase in lobular height and width; however, under most conditions, lobules elongate during cortical growth, forming columnar morphologies because of the strong constraints imposed by anchoring centers, i.e., the bases of the initial fissures. In contrast, fan-shaped lobules emerged only when relatively large cortical growth occurred in a flat cortical region at the lobular apex, resulting in expansion along the anterior–posterior axis. These results confirm that the interplay between spatially heterogeneous cortical growth and initial lobular morphology is a key mechanical requirement for generating diverse cerebellar lobular morphologies, highlighting the utility of computational approaches for dissecting complex morphogenetic processes.
Transcatheter aortic valves (TAVs) typically operate on non-circular rings, but the impact of annular ellipticity on valve mechanics remains insufficiently quantified. In this study, we present a finite element (FE) framework of a 27 mm Allegra TAV that reproduces the complete model, including stent and pericardial skirt and leaflets. The leaflets were represented using a general shell formulation that decouples the in-plane and bending responses, leading to a hybrid shell–membrane finite element model. A linear elastic constitutive law was calibrated through inverse FE analysis based on cantilever bending experiments performed on bovine pericardium. The model was validated against in vitro pulse duplicator tests for circular and highly elliptical geometries, reproducing distinctive features such as full systolic opening and the asymmetric ‘pinwheel’ pattern during diastolic closure. Once validated, the model is used to investigate the impact of annular ellipticity across six annular aortic geometries. Each geometry was evaluated in two limiting orientations of the ellipse’s major axis (0 ^∘ and 90 ^∘ ) to analyse the model response in terms of valve coaptation. These results identify annular geometry and alignment as major factors in valve leaflet coaptation asymmetry and this asymmetry was found to correlate with increased stress concentration, which could compromise long-term valve function.
The role of local hemodynamics on atherosclerosis at the carotid bifurcation has been the subject of study by computational fluid-dynamics (CFD) simulations for over three decades. Nevertheless, questions still swirl about the inherent rigid-wall assumption, especially with the introduction of increasingly predictive—but also increasingly intricate—hemodynamic parameters. Two-way-coupled fluid–structure interaction (FSI) simulations were performed on a cohort of 10 carotid bifurcations with ostensibly normal lumen geometries, along with CFD simulations assuming rigid arterial walls. In FSI simulations, carotid wall mechanical properties were assumed to be anisotropic via a fiber-reinforced hyperelastic material model, also accounting for prestress and external tissue support. Three-element Windkessel models were used to impose pressure conditions consistent with patient-specific measured inflow rates and outflow divisions. Maximum cross-sectional area changes over the cardiac cycle were generally less than 21
This study aims to investigate the biomechanical behaviour of the carotid artery in patients with fibromuscular dysplasia (FMD) disease. Carotid FMD is an arterial disease lacking either inflammatory or atherosclerotic pathology, which is characterised by segmental disruptions in arterial wall architecture. It is of considerable interest to examine carotid FMD haemodynamics for the identification of clinically meaningful biomechanical biomarkers. Thus, a two-way coupled three-dimensional (3D) fluid–structure interaction (FSI) model was developed that integrates patient-specific vascular geometries, non-Newtonian turbulent blood flow, an orthotropic hyperelastic representation of the arterial wall, and a Windkessel boundary formulation, with emphasis on characterising haemodynamic biomarkers and biomechanical wall responses. The results showed distinct severity-dependent trends among healthy, focal, non-focal, and severe non-focal carotid geometry types. FMD cases exhibited increased velocities and wall shear stresses, while their pressure gradients at the distal end decreased. Additionally, elevation of OSI (oscillatory shear index) and RRT (relative residence time) values was observed in each FMD model indicating higher levels of flow disruption, oscillatory shear, and localised flow stagnation. Non-focal phenotypes showed the largest radial deformation, whereas the focal configuration displayed the highest von-Mises stresses. These findings indicate that as FMD progresses through increasing complexity in its morphological structure, it will be subjected to increasingly adverse haemodynamics and mechanical forces, which are likely to promote endothelial dysfunction and further progression of the disease.Kindly check and confirm the corresponding author of the article and the first/last name of the authors are correctly identified.All others' names and affiliations have been checked.
The rapid development of advanced cardiac imaging and modeling technologies has significantly increased the number of parameters required to accurately characterize cardiac function, thereby improving model accuracy at the cost of increased complexity. Identifying and understanding the relationships between model parameters and clinically relevant outputs has therefore become a central challenge in both cardiac modeling and pathology-specific personalization. In this work, we propose a new approach to perform interpretable global sensitivity analysis by leveraging causal discovery. More precisely, causal discovery is employed to disentangle and quantify the joint effect of multiple parameters of an electromechanical cardiac model on some clinical biomarkers, enabling a robust multi-output global sensitivity analysis. We further explore how these parameter–biomarker relationships vary across cardiac geometries, comparing a healthy heart with two pathological conditions: hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). The resulting causal impacts highlight geometry-dependent sensitivities and identify a reduced subset of influential parameters for each biomarker, providing actionable guidance for model calibration under pathological conditions, and supporting the definition of pathology-informed priors for personalization workflows. Finally, compared to classical global sensitivity analysis techniques such as Sobol and Pawn, our proposed approach yields more stable and interpretable results, even when only a limited number of simulations is available. Overall, this study demonstrates that causal discovery offers a powerful and reliable alternative to perform sensitivity analysis in complex cardiac models, particularly in data-limited settings where biological constraints alone are insufficient. The proposed workflow is made publicly available on GitLab . This work extends our previous study (Al-Ali et al. 2025) by broadening the sensitivity analysis beyond the classical biomarkers–ejection fraction (EF) and the maximum rate of pressure change in the left ventricular cavity (max(dP/dt))–to include two additional clinically relevant outputs: the isovolumic relaxation time (isor), as an indicator of diastolic function, and the early passive filling of the left ventricle (QRSE). Moreover, we considered two pathological cases to study the variability of parameters-outputs impacts, and we employed an additive noise model (ANM) to further support and validate our causal based global sensitivity analysis.
Glioblastoma progression is strongly influenced by evolving mechanical interactions between the tumor and surrounding brain tissue. However, the extent to which finite-deformation mechanics and constitutive assumptions improve subject-specific prediction as tumor burden evolves remains unclear. We introduce a sequential Bayesian inference and dynamic model selection framework that assimilates longitudinal murine magnetic resonance imaging (MRI) data to calibrate spatially varying tumor diffusivity, proliferation rate, and tissue stiffness in biomechanical tumor growth models. Competing formulations were compared at each imaging time, including reaction-diffusion without mechanics and reaction-diffusion coupled to linear elasticity or hyperelastic mechanics, using posterior model plausibility to adapt model choice for individualized one-scan-ahead prediction as new MRI scans are acquired. Across the studied animals, mechanically coupled models were consistently more plausible than the uncoupled reaction-diffusion model, and the evolution of model plausibility indicated an increasing role of mass effect and stress-mediated feedback of tumor growth during progression. While linear and hyperelastic coupled tumor growth models often produced similar tumor morphology, they yield distinct stress, deformation, and inferred stiffness fields, with the hyperelastic formulation often receiving higher posterior plausibility at later imaging times. These results indicate that, within the present longitudinal murine dataset, mechanical coupling is favored for image-informed glioma growth prediction and that constitutive assumptions should be evaluated sequentially for each subject rather than fixed a priori.
Accurate and rapid characterization of lung mechanics remains a central challenge in respiratory disease management. Physics-informed poroelastic finite-element (FE) models resolve detailed tissue–airflow interactions but are computationally prohibitive for real-time or large-scale clinical applications, while lumped-parameter models sacrifice mechanistic fidelity for efficiency. In this work, we present a porcine-specific, multi-fidelity computational framework that integrates poroelastic FE modeling with machine learning to enable rapid, uncertainty-aware estimation of respiratory compliance ( C_rs ) and resistance ( R_rs ). High- and low-fidelity simulations are generated from CT-derived porcine lung geometries by sampling a physiologically relevant parameter space, and the resulting pressure–volume dynamics are used in an inverse modeling procedure to infer global respiratory mechanics. A key result is that multi-fidelity Gaussian process (MF-GP) surrogates achieve accurate predictions of C_rs and R_rs with errors below 5
Arteries serve primarily a biomechanical function. Critical insight into arterial structure, properties, and function thus derives from knowledge of mechanosensitive gene expression, associated microstructural organization, and biomechanical metrics such as compliance and vasoactive capacity. This review focuses on time-course changes in hemodynamic loads and associated changes in the transcriptional profile, mural composition, overall geometry, and mechanical properties of arteries during postnatal development, namely, from birth to a healthy adult. Although we examine the postnatal period, we allude to key findings during the prenatal period; although we draw on findings from multiple species and vessels, most data come from studies of the thoracic aorta in mice as an archetype vessel; and although we focus on normal development, we highlight four pathologic cases in which emergent homeostasis is compromised. Collectively, the data suggest that tissue-level mechanical homeostasis typically emerges following postnatal growth, with set-point values for multiple metrics dictating subsequent adaptations to changing hemodynamic loads in maturity, though with congenital defects, pathogenic variants, and disease conditions compromising homeostatic processes. Understanding the normal developmental program is essential for studying early-onset conditions, early surgical and pharmacological intervention, and ultimately aging as well as disease progression and its treatment in maturity.
4D Flow Magnetic Resonance Imaging (MRI) is the state-of-the-art technique for measuring blood flow and provides valuable data for inverse problems in the cardiovascular system. However, acquiring 4D Flow MRI data requires long scan times, placing a burden on healthcare resources and causing discomfort for patients. To mitigate this, only part of the k-space is typically acquired, requiring additional assumptions for image reconstruction, introducing inaccuracies that can degrade the results of inverse problems. Moreover, a wide range of sampling patterns is available, and it is often unclear which one is most suitable. Here, we present a parameter estimation framework that directly uses highly undersampled k-space measurements. We solve the resulting problem numerically using a Reduced-Order Unscented Kalman Filter. We show that this approach yields more accurate estimates of boundary-condition parameters in a synthetic aortic blood flow model than approaches based on compressed-sensing reconstructions of the flow images. We also compare different sampling patterns and show how estimation accuracy depends on the sampling strategy. The results demonstrate substantially higher accuracy than inverse problems based on velocity fields reconstructed via compressed sensing. Finally, we validate these findings using real MRI data from a mechanical phantom.
Cell migration plays a central role in numerous physiological and pathological processes and emerges from the coordinated interplay between intracellular force generation, adhesion dynamics, and mechanical interactions with the environment. A minimal, mechanistically grounded understanding of these processes is required to disentangle the respective contributions of cell-intrinsic and environmental cues. Here, a two-dimensional in silico cell motility model is introduced to describe mesenchymal migration driven by intracellular traction forces generated within actin-rich protrusions anchored to a substrate. The model explicitly accounts for adhesion nucleation, maturation, force buildup and rupture, and relies on a small set of physically interpretable parameters. A systematic mechanical analysis identifies parameter regimes that permit effective cell translocation and delineates conditions leading to stalled or mobile cells. Within motile regimes, the model reproduces a broad spectrum of cell morphologies and migratory behaviours. In particular, cell trajectories exhibit the statistical features of a persistent random walk, with a crossover from ballistic to diffusive motion that arises solely from adhesion dynamics and force balance, without imposing polarization or directional bias. Cell morphology is shown to strongly regulate migration speed, persistence, and pausing behaviour. Altogether, this model provides a minimal reference framework for cell migration on non-deformable substrates and establishes a baseline for future studies of mechanically driven guidance. By construction, it is well suited for extension to deformable fibrous substrates, where cell-induced matrix remodeling and stiffness feedback are expected to bias migration and regulate cell encounters relevant to tissue morphogenesis and anastomosis.
While continuum fibre-reinforced constitutive models of collagen-rich soft tissues incorporate microstructural information via structure tensor invariants, most of their numerical implementations assume spatially uniform fibre orientations. This study examined how spatially heterogeneous orientations, captured by high-resolution imaging and embedded in image-based finite element models, could provide novel mechanistic insights into tissue micromechanics. Serial block-face scanning electron microscopy (SBF-SEM) captured collagen architecture from fresh human skin dermis. Voxel-level 3D fibre orientations were extracted via structure tensor analysis. Voxel-based hexahedral meshes with element-level orientations were implemented in Abaqus/Standard with custom UMATs® user subroutines for invariant-based transversely isotropic hyperelasticity, and compared to classical models with spatially uniform fibre distributions under various loading conditions. Spatial heterogeneity significantly altered micromechanical responses. Under pseudo-homogeneous uniaxial extension aligned with the mean fibre orientation, Models 1A (uniform orientation, no dispersion), 1B (uniform orientation with dispersion), and 1C (spatially heterogeneous orientations) yielded nominal stresses at 44.6
