Accurate modelling of knee joint biomechanics is essential for understanding ligament function, joint degeneration, and musculoskeletal (MSK) adaptations. However, conventional MSK models often oversimplify the knee as a rigid joint and neglect the three-dimensional (3D) interactions between the anterior and posterior cruciate ligaments (ACL and PCL). In this study, a novel finite element (FE) MSK model of the lower extremity was developed and validated. The model incorporated detailed 3D geometries and contact definitions for cartilage, menisci, and ligaments, enabling simultaneous estimation of muscle forces, joint kinematics, and tissue contact stresses under dynamic loading conditions. Model predictions indicated good agreement with experimental data for cartilage and ligament mechanics, joint axial contact forces, muscle forces, and kinematics. The model revealed that ACL-PCL contact was both activity- and phase-dependent, occurring during walking, stair ascent, and stand-to-sit movements, with peak contact pressure reaching 0.32 MPa during stand-to-sit. Although this contact had limited effects on overall joint loading, it markedly influenced tibial internal-external rotation, highlighting its biomechanical relevance. Furthermore, the model identified distinct gait-specific loading patterns: the ACL was the primary load-bearing ligament during walking, whereas the PCL was dominant during stair ascent and stand-to-sit. These findings underscore the importance of incorporating cruciate ligament contact mechanics in MSK modelling to accurately capture dynamic knee function. The proposed FE MSK model provides a robust platform for analyzing cruciate ligament behaviour and load-sharing mechanisms during functional activities, with applications in orthopedic research, injury prevention, rehabilitation, and surgical planning.
Crimping and deployment of coronary stents involve severe finite deformations, multibody contact, and complex loading-unloading sequences that critically influence their structural integrity and long-term performance. This study presents a 3D phase-field fracture framework for simulating the onset and evolution of metal stent failure during crimping and balloon-assisted deployment in coronary arteries modeled as an anisotropic, hyperelastic material. The proposed framework combines finite-strain elastoplasticity with a phase-field description of ductile fracture, implemented as a dedicated user element (UEL) in Abaqus and validated against experimental stress-strain data for stainless steel stents to accurately capture plastic deformation, damage initiation, and softening. In parallel, a second UEL is developed for the arterial wall, incorporating anisotropic hyperelasticity to represent the layered mechanical response of intima, media, and adventitia. Fully coupled simulations of the stent-balloon-artery system reproduce the complete crimp-hold-release and expansion sequence, explicitly capturing contact interactions, stress localization at crowns and connectors, and progressive damage accumulation under realistic physiological conditions. The simulations reveal that fracture is initiated already during the crimping phase and continues to evolve during balloon expansion, resulting in localized damage zones, residual stresses, and elastic recoil after balloon deflation. Comparative analyses of representative stent designs (e.g., open-cell and closed-cell configurations with varying strut thickness and geometry) demonstrate how design features, loading paths, and arterial anisotropy govern damage evolution, failure progression, and post-deployment mechanical performance. The proposed model establishes a robust computational framework for failure-aware evaluation of coronary stents under finite strains, providing new insights for optimizing stent design and deployment strategies. The corresponding source code in this study is openly available at https://doi.org/10.25835/666phabc to support further research.
Lateral ventricular enlargement is one of the most prominent features of the aging brain and is clearly visible on structural magnetic resonance imaging. Both longitudinal and cross-sectional imaging studies have shown that ventricular volume progressively increases with age and expands even faster in neurodegenerative diseases such as Alzheimer's disease and related dementias. Strikingly, however, we only have a limited understanding of ventricular shape changes and the corresponding mechanical loads that act on the ventricular wall as we age. Therefore, we propose a framework that uses nonlinear registration to quantify subject-specific brain deformations between two longitudinal scans, maps the resulting warp field onto a ventricular surface template mesh, and quantifies mechanical loading measures including displacement magnitude, curvature change, area stretch, and maximum principal wall strain. From the Alzheimer's Disease Neuroimaging Initiative, we selected a cohort of 50 cognitively normal subjects aged 70-75 years at baseline and with a follow-up scan 4-5 years later. In this group, we observed mostly uniform expansion of the lateral ventricles with an average displacement magnitude of 0.88 ± 0.3 mm across the whole ventricle. At the same time, there are distinct sections of the ventricular wall that experience high mechanical loads with respect to our mechanomarkers. Specifically, maximum mechanical loading consistently localizes along the ventricular edges and atrium while the ventricle's main body exhibits minimal loading. Based on the cohort included in this study, we did not observe sex-based differences with respect to any mechanomarker, noticed that on average 29.2 ± 9.3% of the ventricular wall experience wall area increase, and that on average only 4.4 ± 2.5% of the ventricular wall experience wall shrinking. Interestingly, regions of elevated mechanical loading showed reliable spatial correspondence with periventricular white matter hyperintensity locations in our subjects for whom FLAIR imaging was available (n = 39). Additionally, mechanomarkers showed increased magnitudes with periventricular white matter hyperintensity burden, with curvature change demonstrating the strongest group separation. These findings suggest that ventricular enlargement is associated with localized mechanical stresses that coincide with vulnerable white matter regions. Taken together, we present strong evidence in support of the hypothesis that the mechanical loading associated with age-related ventricular enlargement is intricately linked to periventricular white matter degeneration and corresponding cognitive decline.
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% relative to high-fidelity simulations, while providing computational speedups of over five orders of magnitude. In contrast, neural network (NN) surrogates exhibit relatively poor generalization in the data-scarce regime considered, highlighting the importance of model selection for scientific machine learning under limited high-fidelity data availability. Beyond predictive performance, global sensitivity analysis reveals a clear mechanistic separation in parameter influence: compliance is primarily governed by elastic stiffness and chest-wall coupling, whereas resistance is dominated by permeability. The weak interaction effects observed support an approximately additive response structure, enabling robust parameter identifiability and reduced-order representations of the inverse problem. The framework is validated against independent ventilator measurements from porcine lungs, showing strong agreement within clinically observed ranges. Overall, this study provides new insight into the structure of the inverse problem in poroelastic lung modeling and establishes a computationally efficient pathway for uncertainty-aware prediction and parameter estimation, with potential applications in personalized ventilation and preclinical study design.
Trackability of thrombectomy catheters through tortuous cerebral vessels is a key determinant of mechanical thrombectomy success, particularly for large-bore aspiration catheters. Yet, the underlying biomechanical challenges remain unclear. This study integrates in silico and in vitro analyses to investigate catheter navigation in a flexible intracranial vasculature model. A silicone patient-averaged tortuous vessel model was used for experimental studies in a circulatory flow loop and reconstructed from CT imaging for computational simulations. Regarding the in silico part of the study, in contrast to prior work relying on tip-dragging or centerline-based advancement, we implemented clinically realistic catheter pushing mechanics. We varied the vessel compliance and catheter-vessel friction coefficients to understand their sensitivity toward navigation. Strong qualitative agreement emerged between simulated and experimental catheter paths. Key findings include: (i) realistic pushing produced trajectories distinct from tip-dragging, with the catheter naturally aligning along the outer curvature to generate supportive contact and it matches with in vitro experiments; (ii) increased vessel flexibility (2 MPa) markedly improved catheter advancement, whereas stiffer vessels (10 MPa and rigid) promoted kinking; (iii) catheter-vessel interaction was observed to be a critical factor in navigation, with low friction coefficient (F) enhancing trackability (F < 0.1) and high friction (F > 0.15) triggering bending and kinking. Incorporating vessel flexibility and clinically representative pushing mechanics is essential for accurate thrombectomy modeling. The presented framework accurately reproduces catheter behavior, particularly in curved segments, and offers predictive capabilities for device performance. These insights offer quantitative design guidance for next-generation microcatheters and aspiration catheters, highlighting the critical role of catheter-vessel mechanics in distal cerebral arteries.
The intervertebral disc (IVD) plays a fundamental role in load absorption and redistribution during daily activities. Its mechanical behavior arises from the intricate interplay between the structural anisotropy of the annulus fibrosus (AF) and osmotic swelling driven by fixed charge density in the nucleus pulposus (NP). This behavior is further governed by fluid redistribution through the porous matrix and across its boundaries, including interactions with the adjacent endplates and the surrounding physiological environment. Despite advances in IVD modeling, few computational tools accurately capture these coupled mechanisms while accounting for progressive degeneration under realistic loading conditions. This study introduces a microstructure-informed and degeneration sensitive finite element model of the human IVD, integrating regional fiber architecture, biphasic fluid-solid interactions, and osmotic swelling within a unified mechanistic framework. A multiscale calibration strategy is employed to identify the solid, osmotic, and fluid transport parameters, based on targeted mechanical experiments. Degenerative changes are incorporated at both macroscopic (e.g., IVD height loss) and microscopic (e.g., fiber uncrimping, proteoglycan depletion, and increased matrix porosity) levels. Model predictions are compared against physiological loading scenarios representative of everyday life-including lying down, standing upright, and trunk motions-revealing the evolving contribution of each mechanism with degeneration, while model robustness is assessed through a parametric sensitivity analysis. This framework provides a mechanistic understanding of the evolving roles of IVD constituents across degeneration and defines representative parameter sets for different degenerative states, providing a basis for future patient-adapted modeling approaches. It also enables the exploration of degeneration-dependent mechanical responses and loading sensitivities, offering perspectives for improved mechanobiological understanding of IVD degeneration.
The development of computational models for predicting bone fracture healing process holds strong potential to optimize therapeutic management in non-unions and delayed healing, reducing healthcare costs and disability-adjusted life years. The main goal of this study is to provide a thoroughly comparative analysis to computational models already proposed to predict fracture healing, including methodologies, mathematical frameworks, validation techniques, and comparative findings across different studies. This review analyzes 60 computational models selected through a systematic search in the Scopus database (2000-2025) using targeted keywords and rigorously screened according to predefined inclusion and exclusion criteria for simulating bone fracture healing, focusing on mechanical, biological, mechanobiological, ultrasound, and bioelectronic dynamics, employing FEM and artificial intelligence techniques. The effectiveness of each model in predicting healing progression was assessed by analyzing their computational frameworks, accuracy, and limitations. Comparative analysis revealed that both mechanical and biological models provide fundamental predictions related to fracture healing (e.g., stress distribution and vascularization), but they often lack the physiological complexity demanded for clinical application. Mechanobiological models accurately predict tissue differentiation by combining mechanical stimuli, with strong qualitative agreement with in vivo histological patterns. Ultrasound models have been effective for non-invasive structural assessment, despite existing limitations due to simplified boundary conditions. Notably, the development of bioelectric models has been demonstrating a highly sensitive approach for assessing fracture healing. This study highlights that multidomain computational frameworks combining mechanobiological dynamics with dielectric properties hold significant potential to personalize the clinical management of delayed bone healing.
Mechanical ventilation is a life-saving therapeutic intervention for patients with impaired pulmonary function, yet it carries the risk of ventilator-induced lung injury (VILI). At bedside, physicians face the challenge of keeping lung tissue in a healthy state while ensuring sufficient gas exchange. Gas exchange occurs between the air in the alveoli and the dense network of pulmonary blood vessels in their walls, and it strongly depends on the balance between ventilation and perfusion. Mismatches between them are a major cause of impaired gas exchange in pulmonary diseases. However, the precise effects of ventilation, including tissue straining on the pulmonary circulation and the connected gas exchange, are largely unknown. Here, we therefore present an approach to computationally model the respiratory and circulatory systems of the human lungs, including gas exchange. Motivated by the lung's hierarchical structure, our model represents larger airways and blood vessels as spatially resolved discrete networks of zero-dimensional (0D) models that are embedded into a multiphase porous medium (3D). The porous medium models the smaller respiratory and vascular structures, including lung tissue mechanics, in a homogenized way. Additionally, the respiratory gases-oxygen and carbon dioxide-are incorporated as chemical subcomponents of air and blood, with an exchange model in the porous domain. To connect the homogenized (porous domain) and the discrete (networks) representations of airways and blood vessels, we use a 0D-3D coupling method that allows a non-matching spatial discretization of both domains. This comprehensive coupled approach is physics-based, i.e., based on the underlying physical mechanisms, allowing us to investigate the (often unknown and unmeasurable) interplay between ventilation, tissue deformation, perfusion, and its effects on gas exchange dynamics. We anticipate our approach to be an important milestone towards better addressing clinically relevant questions in respiratory care in silico, which will contribute to developing improved ventilation strategies and better patient outcomes.
Pulmonary arterial hypertension (PAH) is a complex disease characterized by chronically elevated pulmonary arterial pressure, with early onset and progression linked to structural, metabolic, and morphological changes in the pulmonary vasculature. Understanding the interplay between hemodynamics and arterial wall mechanics is essential to capture the pathology of the distal vasculature in PAH. This study aims to develop a data-driven framework that establishes a baseline state of PAH vasculature, incorporating key features of arterial wall constituents, geometry, and their interaction with PAH-specific hemodynamics. Illustrative examples of symmetrically bifurcating arterial trees are used to define representative baseline characteristics of PAH-affected pulmonary arteries. Compared with healthy homeostatic vasculature, the computational results demonstrate pronounced geometric and mechanical alterations: Arterial stiffness increases from approximately 7-10 kPa in healthy arteries to 300-800 kPa in PAH, representing a ~ 40-85 times increase across generations. Because wall thickening is imposed from histological measurements while outer diameter is preserved, the diameter-to-thickness ratio (D/h) decreases from ~ 14 in healthy arteries to ~ 3.8 in PAH, reflecting severe lumen narrowing and medial hypertrophy. In addition, the metabolic energy cost per unit length in PAH is more than double that of healthy arteries when assuming unchanged metabolic consumption per unit volume, whereas enforcing equal total energy cost yields a reduced per-volume metabolic consumption of ~ 450-500 W/m3. These findings suggest that maintaining constant metabolic consumption per unit volume would impose excessive energetic demand on the pulmonary vasculature in PAH, whereas redistribution of metabolic expenditure through altered wall composition may represent a more physiologically plausible adaptation. Furthermore, this framework provides a quantitative baseline state for PAH vasculature and lays the groundwork for future integration of growth-and-remodeling analyses and pharmacological pathway modeling to evaluate treatment response.
Myocardial infarction (MI) remains one of the leading causes of mortality worldwide, with significant long-term consequences on cardiac structure and function. Over the past decades, computational modeling techniques have advanced substantially, providing powerful tools to improve the understanding, diagnosis, and treatment of MI. Among these techniques, the finite element method (FEM) has emerged as a framework for investigating the complex biomechanical, electrophysiological, and structural alterations that occur following infarction. This review provides an overview of the diverse applications of FEM in myocardial infarction research across multiple interconnected areas. Initially, image-based geometric reconstruction and kinematic analysis of the left ventricle are discussed which enable patient-specific modeling. Then, electrophysiological and electromechanical simulations are addressed that capture infarct-induced alterations in electrical conduction and mechanical contraction. The estimation of myocardial material properties, including passive and active constitutive behavior, is reviewed as a critical component for accurate model prediction. Furthermore, growth and remodeling models are analyzed to highlight how infarct progression and ventricular adaptation can be computationally characterized. The role of FEM in studying ischemic and functional mitral regurgitation is also presented, emphasizing valve-ventricle interaction. Finally, FEM-based treatment strategies, including surgical, device-based, and biomaterial interventions, have been reviewed. This review provides a comprehensive overview of current FEM applications in myocardial infarction and highlights key challenges and future research directions for advancing translational and patient-specific cardiac mechanics.
Although there has been considerable progress in understanding the factors that determine the invasiveness of Plasmodium falciparum merozoites, the collective role of the biophysical characteristics of erythrocyte deformability in the invasion process is poorly understood. Cell shape, cytoplasmic viscosity, and membrane stability are the main determinants of erythrocyte deformability, but it remains unknown how these properties affect the merozoite invasiveness. This study aimed to investigate computationally (i) the role of erythrocyte morphology and merozoite-induced erythrocyte membrane damage in merozoite invasion and (ii) the suitability of mechanical markers of merozoite-induced erythrocyte membrane damage for screening of invasion-blocking antimalarial drugs. Finite element models were developed to represent a human erythrocyte and a spherocyte, their invasion by a malaria merozoite, and erythrocyte compression and nanoindentation as mechanical assays for membrane damage. Smoothed particle hydrodynamics represented the erythrocyte cytoplasm, and merozoite-induced erythrocyte membrane damage was implemented with a constitutive model. The invasiveness of the merozoite decreases with increased erythrocyte sphericity associated with genetic disorders such as hereditary spherocytosis. The invasiveness is larger when membrane damage is induced in the erythrocyte at an early invasion stage than throughout the invasion process. The minimum force required for a malaria merozoite to invade a human erythrocyte was predicted to be 11 pN. The findings on the invasion mechanics can guide future studies into the invasiveness of the merozoite. The nanoindentation simulations point to the potential of nanoindentation to determine erythrocyte membrane damage for screening novel invasion-blocking antimalarial drugs.
Filamentous actin (F-actin) constitutes the primary contributor to cell elasticity and structural integrity, forming dynamic, crosslinked networks in the actin cortex. Existing mechanical models for F-actin and crosslinked filament networks successfully describe filament- and network-level behavior, but are often limited in accounting for biological dynamic processes and inherent material uncertainty and variability. We develop a stochastic modeling framework that integrates Polynomial Chaos Expansion (PCE) surrogates using the Finite Element Method (FEM). These surrogates replace filament-scale equations for compliant crosslinked F-actin networks, efficiently enabling uncertainty quantification and sensitivity analysis of key material parameters. The first and second statistical moments from the PCE are incorporated into a micro-sphere network model and implemented via a user-defined material subroutine. Validation was performed against 10 000 Monte Carlo simulations (MCS) for each of four FEM test cases: three simple deformation modes applied to a unit length cubic element, and a thin gel layer under shear mimicking a parallel plate rheology setup. In every test, the surrogate predicts the expected value of relevant stress quantities at maximum deformation with under 1% relative error versus the MCS reference. Moreover, the surrogate captures the network's variability as measured by second-order moments, demonstrating its ability to deliver rapid, statistically faithful predictions of both mean response and standard deviation in simple element tests and experimentally relevant rheology geometries. The proposed methodology provides a scalable route for incorporating intrinsic material variability into F-actin mechanical modeling, with implications for studying cell motility, division, and pathologies related to cytoskeletal remodeling.
To study the mechanics of a biological tissue is crucial to understand its behavior under a variety of realistic loading conditions. In particular, an accurate estimation of the mechanical properties of breast tissues is essential to enhance current diagnostic techniques, such as mammography, and to improve clinical treatments, including tissue engineering approaches. This study focuses on the mechanical characterization of human breast tissues, harvested from women who underwent a mammary reduction surgery. Ex vivo experiments, consisted in indentation tests, were performed to determine the elastic properties. Additionally, using inverse finite element analysis, the hyperelastic properties were obtained for the Yeoh and Ogden (N = 3) constitutive models. The samples were grouped based on the characteristics of the female population (i.e., age, body mass index and menopausal status) and according to the breast side (tissue sample site). Significant differences in the Young's modulus were only observed in association with age and menopausal status, in the first linear region (3.75-11.5% strain) of the experimental curve. In the second linear region (22.5-30% strain), although samples presented a higher stiffness, no significant differences were observed. Regarding the hyperelastic properties, Yeoh and Ogden (N = 3) models accurately fit the experimental data, presenting errors lower than 3.26% and 3.86%, respectively. In this work, the model developed successfully converged with a 3 mm of indentation (corresponding to 30% of deformation), enabling reliable analysis in the large deformations domain. The findings of this study provide valuable insights that can contribute to future clinical applications and research, including improvements in diagnostic techniques, treatments and esthetic reconstructions.
Central airway obstruction can be caused by lung cancer and may severely diminish respiratory function to necessitate airway stenting. However, the mechanical properties of airway tissues remain poorly characterized, leading to a mismatch between stent mechanical behavior and airway compliance, which can reduce stent biocompatibility and clinical effectiveness. Such mismatches can result in abnormal stress transfer at the stent-airway interface and cause stent migration, local tissue irritation or inflammation, and impaired long-term performance. Predictive models are often used in treatment planning, however, without a comprehensive understanding of airway properties, advancements in modeling remain highly limited. In this study, we develop an experimental to numerical pipeline for identifying the mechanical properties of the human airway branching network, specifically the trachea, right bronchus, and left bronchus tissues. Biaxial planar tensile tests are used to capture the tissues' unique, anisotropic mechanical response in order to inform the computational routine-where inverse finite element analysis is employed to calibrate a Holzapfel-Gasser-Ogden constitutive model. To overcome the computational burden of traditional IFEA, a neural network (NN) surrogate model is trained to enable rapid material identification. We identify parameter values for the various tissues, such as C10 = 0.53 ± 0.25 kPa, k1 = 0.17 ± 0.30 kPa, k2 = 6.1 ± 2.0, and κ = 0.08 ± 0.01 for the trachea. Notably, this NN-approach reduced computational time from weeks to mere minutes, enabling fast material characterization. Thus, we also provide an efficient modeling framework and user-friendly MATLAB application for future studies. Ultimately, the findings here critically contribute to our current understanding of airway biomechanics and provide essential data for accurate modeling and optimization of airway stenting strategies.
To investigate the mechanical correlations between intraocular pressure (IOP) variations and glaucoma, this study presents a linear transversely isotropic poroelastic model of the lamina cribrosa (LC) based on Reissner-Mindlin plate theory. A key feature of the proposed framework is its analytical tractability, which allows the governing poroelastic equations to be solved in closed form under appropriate mechanical and hydraulic boundary conditions. Within this setting, linearity is used to capture the reversible component of the tissue response, providing a baseline description of the coupled solid-fluid feedback on which more complex time-dependent phenomena, such as viscoelastic effects and remodelling, may build. The results indicate that both strain and stress measures (in the form of shear strain and deviatoric stress measures) peak in the peripheral region of the LC, which is currently suspected to be the initial site of glaucomatous damage. These quantities increase with IOP, suggesting a pressure-dependent mechanical insult to the retinal ganglion cell (RGC) axons. In parallel, the model predicts a monotonic reduction in fluid content as IOP rises, which may contribute to ischemic phenomena and disc haemorrhages. The influence of material anisotropy was also examined, revealing that isotropic assumptions tend to overestimate the fluid content while underestimating shear strain. Given the current experimental challenges in measuring blood flow within the LC, the proposed model provides a valuable framework for exploring the coupled mechanical-hemodynamic behavior of the tissue and for inverse estimation of its mechanical parameters, such as the stiffness of the opening for the central retinal vessels.
Computational head models are essential tools for predicting the risk of mild traumatic brain injury (mTBI). However, computational models vary in the level of anatomical details, most notably the cortical folds. This study aims to determine the effect of modeling cortical folds on mTBI risk assessment. We compared gyrencephalic (with cortical folds) and lissencephalic (without cortical folds) finite element (FE) head models of 18 subjects aged 9-18 years, subjected to a rotational head acceleration of 10 krad/s 2 (10 ms duration) about each principal head axis. We analyzed the effect of cortical folds on different tissue-level mTBI injury metrics, including maximum principal strain (MPS95), maximum principal strain rate (MPSR95), and cumulative strain damage measure (CSDM15). The inclusion of cortical folds consistently yielded higher injury metrics across all individuals and rotational directions, with a bias (mean ± std. dev. relative to maximum lissencephalic values) of 21.7 ± 9.1 % in MPS95, 17.1 ± 7.6 % in MPSR95, and 14.4 ± 11.3 % in CSDM15. Differences in the spatial strain distribution were also found between the models, with the DICE similarity coefficient ranging between 0.07 - 0.43 and 0.42 - 0.70 for the peak MPS and CSDM15, respectively. Increases in peak injury metrics (up to ∼ 50%) were found for brain regions such as the corpus callosum, cerebellum, and brain stem. This study finds that the inclusion of cortical folds significantly alters the pattern of deformation in the brain and results in a prediction of higher mTBI risk.
Hemodynamic analysis is an essential tool for predicting the behavior of blood flows and assessing the risk of renovascular diseases. In this paper, by employing a CFD-based finite element method coupled with an efficient parallel algorithm for the unsteady incompressible Navier-Stokes equations, we conduct a comprehensive investigation of renal hemodynamics and the impact of outflow boundary conditions in patient-specific models of normal, stenotic, and aneurysmal arteries featuring rich small-branch networks. Based on the hemodynamic analysis for the severe stenosis (area stenosis > 87 % ) and the aneurysm (diameter = 13.3  mm), we observe a pressure drop exceeding 10 mmHg and a distal-to-proximal pressure ratio below 0.9 for these lesions, which is considered hemodynamically significant and likely induces renovascular hypertension. Furthermore, we reveal that the low wall shear stress and complex vortices with bidirectional flow occur on the inner wall downstream of this stenosis, which play a critical role in driving atherosclerotic plaque formation. Through virtual aneurysm reconstruction and numerical simulation, we demonstrate that the presence of a renal aneurysm alters local flow patterns and pressure distributions. Numerical results for both healthy and pathological renal arteries show that outflow boundary conditions have a significant impact on the global distribution of pressure and local flow patterns near the outlets. Compared with constant pressure and resistance outflow boundary conditions, the two-element Windkessel model, through adjustments of its resistance and capacitance parameters, can provide more physiological flow and pressure distributions, particularly in capturing realistic pulsatile waveforms, pressure ranges, and distal flow patterns. Moreover, a sensitivity analysis of the resistance in the Windkessel boundary condition shows a negligible impact on the pressure drop and only a minor effect on the renal fractional flow reserve (a change of less than 3 % for a 20 % variation in resistance). When focusing solely on the hemodynamics within stenotic and aneurysmal lesions located far from the outlets, both the constant pressure and Windkessel boundary conditions yield comparable results for key lesion-specific hemodynamic indicators, including renal fractional flow reserve and pressure drop in the stenosis, and wall shear stress and oscillatory shear index in the aneurysm.
We present a fully coupled, patient-specific 3D-0D computational framework for hearts supported with left ventricular assist devices (LVAD) that enables controlled in silico experimentation. The approach monolithically integrates three-dimensional CFD of the left ventricle (LV), left atrium (LA), aortic root, and LVAD cannulae with a closed-loop 0D lumped parameter network of the full circulation. Mitral and aortic valve dynamics are governed by transvalvular pressure and flow with patient-specific regurgitant orifice areas, and the LVAD is represented via a pressure-flow (H-Q) relation. This manuscript provides the complete mathematical formulation, coupling strategy, and parameterization required to build a reproducible pipeline from dynamic CT, 2D transthoracic echocardiography, and right heart catheterization. This methodology is demonstrated in a patient under long-term support of LVAD and concomitant mitral and aortic regurgitation. The personalized, fully coupled 3D-0D models reproduced available clinical targets with a mean error of 8.6%, enabling controlled in silico interrogation of valve repair strategies. In the patient-specific state, simulated mitral and aortic regurgitant volumes were 6.6 and 6.5 mL per cycle, yielding a forward cardiac output of 3.16 L/min despite an LVAD flow of 3.7 L/min. In silico isolated mitral valve (MV) repair, isolated aortic valve (AV) repair, and combined MV+AV repair increased forward output to 3.41, 3.33, and 3.55 L/min, respectively; however, aortic valve opening and increased aortic pressure pulsatility (up to 38.9 vs. 13.5 mmHg) were observed only when MV repair was involved. These left-sided improvements propagated through the cardiopulmonary circulation, reducing pulmonary pressures and right ventricular loading, with the largest benefit observed following combined repair. We show that the modeling platform presented provides a powerful means to study mechanical circulatory support, enabling patient-specific evaluation of surgical interventions in patients with LVAD and delivering quantitative insight into clinically important metrics-such as aortic pulsatility, RV afterload, and chamber-level flow patterns.
Arterial walls contain large amounts of water and have conventionally been modeled as incompressible. However, recent experimental studies have reported non-negligible arterial compressibility, with volumetric changes on the order of 10% or larger depending on loading conditions. To clarify the mechanical origin and its implications, this study develops a biphasic modeling framework for arterial mechanics, in which apparent compressibility arises from interstitial fluid transfer within the arterial wall. The arterial wall is modeled as a saturated biphasic material consisting of a solid skeleton and interstitial fluid, in which the solid skeleton is modeled as an anisotropic, hyperelastic material with macroscopic volumetric deformability, and the fluid motion is governed by Darcy's law. Assuming steady, axisymmetric plane-strain deformation, the resulting nonlinear mechanical equilibrium is reduced to a one-dimensional radial boundary-value problem and solved numerically using a finite element method. Systematic parametric analyses demonstrate that radial and circumferential deformations, as well as the resulting volumetric changes, are consistent with experimentally observed mean values, with deviations within 2% under the same loading conditions. Such volumetric expansion, driven by the hydrostatic pressure of the interstitial fluid, induces tensile stress components in the radial direction within the solid skeleton, revealing a mechanical consequence of fluid-solid interactions that is not directly accessible from apparent deformation measures alone. These findings suggest that biphasic modeling provides a mechanically interpretable framework for examining arterial wall responses in regimes where fluid-solid interactions are relevant.
Obesity is a well-known prominent risk factor for knee osteoarthritis (OA). The onset and development of knee OA are affected by multifactorial interplays, involving the degeneration of articular cartilage. Emerging evidence shows the negative effects of obesity on cartilage degeneration across multi-scales. Specifically, obesity can stimulate inflammation and alter the biomechanical responses of the joint. However, the pathology of obesity-associated knee OA is still poorly understood. The aim of this study was to develop a multi-scale modelling framework to simulate and evaluate the mechanobiological roles of obesity in the degenerative process of cartilage. This framework integrated the inflammatory and biomechanical effects of obesity on cartilage degeneration in knee OA. A validated finite element model of a subject-specific knee joint was coupled with a mathematical model of adipokine-mediated OA inflammation. In the algorithm, excess stress resulted in mechanical damage that activated obesity-related inflammatory responses. The degeneration of cartilage was driven by both mechanical damage and body mass index (BMI). Parameter sensitivity analysis showed a good adaptivity of this framework to simulate cartilage degeneration. In addition, BMI and the stress threshold were sensitive to the degenerative process. Results indicate that a higher BMI level could not only increase the degeneration level but also lead to a larger degenerative volume of cartilage. Due to the elevated baseline of inflammation in the obese joint, the relative contributions of inflammation and mechanical damage might vary as cartilage degeneration progressed. This computational framework combines for the first time obesity-associated inflammation and mechanical loading in knee OA. It could be extended by specifying different degenerative pathways in cartilage degeneration. With further calibration, the framework has the potential to empower the identification of different phenotypes and endotypes of OA.