Biomechanics and Modeling in Mechanobiology最新文献

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%), but location and magnitude of peak strains were subject-specific. While models including the subtendons predicted similar average strain values to those without, local heterogeneity increased substantially in the subtendon model (Full-Width at Half-Maximum increased from 0.03 to 0.08). Altering the collagen fiber orientation shifted the localization of higher strains (Full-Width at Half-Maximum = 0.11). In conclusion, both subtendons and realistic fiber orientation should be considered when modeling the mechanical behavior in the Achilles tendon as it strongly affects the strain distribution within the tendon. Accurate prediction of strain localization and distribution is important for further studies of tendon mechanobiology and for designing more effective rehabilitation protocols.

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% for both peak TPG and velocity. Larger discrepancies were observed for EOA due to patient variability and reliability of echocardiographic measurements. The model additionally quantified biomechanical and hemodynamic parameters, including leaflet stress, time-averaged wall shear stress (TAWSS), and blood residence time (BRT). The proposed fully coupled FSI framework enables patient-specific analysis of post-TAVI hemodynamics and valve mechanics for S3 balloon-expandable intra-annular system. The preliminary comparison with echocardiographic measurements showed good agreement for peak velocity and peak TPG, while larger EOA discrepancies highlight the need for further clinical benchmarking using larger cohorts and more direct measurement modalities.

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.

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(dP_Lv/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%). Shorter coupling intervals reduced premature beat contractility while enhancing PESP in subsequent beats. Systematic variation of preload, afterload, and intrinsic contractility revealed that calcium dynamics reproduce the observed PESP patterns, with loading conditions as modulators. Notably, ∆max(dP_Lv/dt) and ∆SBP responded differently, with ∆SBP exhibiting complex non-monotonic behavior. The model qualitatively reproduced pressure–volume patterns from a single clinical quadrigeminy case. This study demonstrates that a calcium-based force–interval formulation reproduces the qualitative features of PESP within this framework, with preload and afterload modulating the pattern of beat-to-beat pressure response. The divergent behavior among contractility metrics emphasizes the need for multimetric assessment. This framework enables distinguishing intrinsic myocardial dysfunction from extrinsic loading effects, facilitating patient-specific risk stratification in PVC-induced cardiomyopathy.

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% of the high-fidelity model, preserving stress/strain spatial distributions. Agreement between the two models was noted in their predicted tissue-level average and peak von Mises stress or minimum principal strain values. Computational time decreased approximately four-fold (2 vs. 8 days), while maintaining biomechanical fidelity. This demonstrates that vertebral bodies are not required to recover accurate spinal cord biomechanics in non-fracture cervical extension scenarios. These findings are based on this modeling framework, for the tested healthy geometry and extension scenario, with prescribed vertebral kinematics. This computational efficiency enables scalable parametric or population-level analyses of how individual differences in canal shape, cord morphology, or degeneration affect injury risk.

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.

The numerical simulation of metabolic processes in the human liver involves challenges related to model accuracy, physiological representation, and data uncertainty. Soft tissue models must capture complex, multiphase, and time-dependent behavior. However, the high computational cost of such analyses often restricts them to simplified deterministic scenarios that neglect biological variability and uncertainty. In this paper, the deterministic foundation of a liver perfusion-function model based on the Finite Element Method (FEM) is extended to incorporate material variability. To this end, a workflow is presented that integrates sensitivity analysis, surrogate modeling based on a Gradient-Enhanced Kriging (GEK) model, and uncertainty quantification. This approach enables the identification of critical parameters, enhances the interpretability of the model, and provides probabilistic outcome assessments for FEM-based simulations. The FEM model represents liver tissue on the lobule scale, incorporating metabolically active cells and vascular systems through a poroelastic multiscale framework with spatially coupled function-perfusion dynamics. It simulates liver-related conditions, such as tumor growth due to metabolic associated fatty liver disease (MAFLD), which affects microperfusion. Aleatoric uncertainties in material parameters are accounted for, and a local sensitivity analysis identifies critical nodes as a foundation for a GEK metamodel. Monte Carlo (MC) methods on the surrogate enable fast propagation of input uncertainty and probabilistic assessment of predicted outcomes. Rather than assuming deterministic accuracy, this approach offers a systematic way to explore and interpret model behavior under uncertainty.