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.
Pressure ulcers remain a persistent and serious complication in clinical care, often originating in deep soft tissues before becoming visible on the skin surface and leading to suffering, prolonged hospital stays, and increased healthcare costs. Individual variability in soft tissue composition and mechanical properties plays a critical role in modulating internal stress and strain distributions during prolonged loading. In this study, we used anatomically representative finite element models to investigate inter-individual differences in tissue vulnerability under localized pressure. Two multilayered models, incorporating variations in epidermal, dermal, adipose, and muscular thickness, density, and stiffness, were subjected to clinically relevant pressure magnitudes (2-10 kPa), simulating conditions associated with immobility and device-related compression. Mechanobiological metrics, including effective stress, effective strain, and percentile-based exposure thresholds, were computed to quantify internal tissue load transmission and damage risk. Model outputs revealed that high stress localized in superficial layers, while strain peaked in deeper tissues, especially adipose and muscle. Simulated reductions in tissue stiffness, reflecting age- or disease-related softening, further exacerbated internal loading, increasing stress-exposed tissue volume by up to 1.5 times and strain-exposed volume by up to 1.2 times. These results highlight the biomechanical consequences of anatomical and material variability and support the development of personalized risk assessment tools. The proposed modeling approach contributes to mechanobiology-informed strategies for pressure ulcer prevention in high-risk populations.
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 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.
The rising prevalence of central nervous system (CNS) diseases has imposed substantial social and economic burdens on healthcare systems. The blood-brain barrier (BBB), a highly selective physiological barrier in the CNS, severely restricts the delivery of most therapeutic agents to the brain, thereby limiting treatment efficacy. Stable cavitation of microbubbles induced by focused ultrasound (FUS) offers a promising strategy for transiently and non-invasively opening the BBB, holding significant clinical potential. However, the underlying biophysical mechanisms remain challenging for understanding, particularly the disruption of tight junctions (TJs) during stable cavitation and the mechanical responses of the BBB under long-term loading. In this study, a three-dimensional (3D) finite element simulation is conducted to model the mechanical behavior of endothelial cells and TJs using the Yeoh hyperelastic model and a modified standard linear solid (MSLS) model, respectively. This framework enables simulation of coupled interactions among oscillating bubbles, surrounding fluid, and the BBB. Numerical results reveal that stable cavitation induces pronounced periodic deformation of the BBB, with localized stress concentrations prominently occurring in TJ regions during bubble expansion. The occurrence of the flow recirculation is correlated to the stress imposing on the BBB. Compared to a linear elastic model, the present nonlinear material formulation demonstrates enhanced deformation and effectively suppressed peak shear stresses of the BBB. We find the fluid stress exerted on the BBB obtained is not large enough to lead to rupture of the TJs. Furthermore, our results indicate a typical fatigue-like feature in TJs under cyclic loading, wherein the von Mises stress is characterized by an initial softening followed by hardening. This suggests that fatigue-like behavior under long-term loading might be the dominant mechanism for the failure of TJs under stable cavitation. These findings contribute to the understanding of the biomechanical mechanisms underlying FUS-microbubble-mediated BBB opening (FUS-BBB) and provide a theoretical foundation for its application in CNS drug delivery and brain disease treatment.
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.
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.
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.
This study is focused on a critical blind spot in the soft tissue biomechanics field-spatial mechanical heterogeneity. Despite abundant experimental evidence indicating that soft biological tissues exhibit regional heterogeneity, particularly in their mechanical properties, incorporation of this heterogeneity into material descriptions of finite-element models has been limited. In this work, gradual spatial variation of mechanical properties is modeled by adopting principles of the theory of functionally graded materials. Using regional biaxial data and the Holzapfel-Gasser-Ogden constitutive model, this paper demonstrates a method to average the mechanical response from tested regions to estimate the response of untested intermediate tissue regions and the use of Fourier functions to capture continuous spatial variations of material parameters. This spatial material parameter dependency was then implemented in a finite-element model's material description using the USDFLD subroutine in Abaqus (2022). This model is referred to as the continuous heterogeneous model and was compared with two other approaches that are used to account for spatial mechanical heterogeneity in soft biological tissues: 1) the homogeneous model that utilizes the averaged mechanical response from all tested specimens, and 2) the segmental heterogeneous model that employs distinct material descriptions for geometrically divided segments of the tissue model. All three approaches to modeling were demonstrated using two biomechanically relevant idealized geometries and boundary conditions: the human ascending aortic aneurysm simulated by a thin-walled cylinder and the back skin simulated by a planar strip. Results demonstrate that implementing spatial heterogeneity markedly affects the stress/displacement fields compared to the homogeneous model. Moreover, between the segmental and continuous heterogeneous approaches, the latter offers advantages such as mitigating stress discontinuities due to abrupt property changes. These findings highlight the impact of accounting for spatial mechanical heterogeneity in finite-element modeling of soft biological tissues and provide a foundation for future research exploring the improved material description in computational models and simulations of soft tissue biomechanics.
Bone was shown to adapt to mechanical loading through the concept of a mechanostat that regulates cell activity to maintain a specific strain signal within the tissue. Current computer models simulate bone resorption and formation in the presence of key biological agents, reproduce a realistic architecture of trabecular bone along principal stresses and estimate changes in bone strength related to immobilisation, overloading, metabolic diseases or drug therapies. However, clinical diagnostics of bone diseases in vivo rely primarily on X-ray-based densitometry and computer tomography that do not have the resolution to describe bone microarchitecture in full detail and evaluation of personalised bone strength is therefore based on a homogenised description of bone mechanical properties using density and fabric. Continuum-level bone adaptation theories rely primarily on bone density and do not involve local optimisation principles to predict fabric. The inverse problems of predicting applied loads from bone morphology typically exploit density but not fabric. Accordingly, this work formulates and provides analytical solutions for optimal bone adaptation at the homogeneous, anisotropic RVE level using bone density- and fabric-mechanical property relationships for three different mechanostat criteria. Two of these criteria elicit different adaptive responses for tensile and compressive strains. Forward solutions for density and fabric are provided at a continuum point for a given local stress, while inverse solutions for local stress are derived for given density and fabric for all three criteria. The 3D solutions are specialised to 2D and 1D for comprehension and compared among the different criteria. In the future work, the obtained solutions will enable simple forward simulation of personalised bone adaptation and inverse estimation of bone loading for clinical diagnostic tools such as high-resolution peripheral quantitative computed tomography (HR-pQCT) or photon counting computed tomography (PCCT).
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.
This numerical study employs a computational model of human hepatic blood flow to investigate the hemodynamic consequences of cirrhosis. The liver receives blood through a dual-inlet system (portal vein and hepatic artery) that perfuses a complex network of sinusoids; however, cirrhosis-induced fibrosis distorts these channels, increasing hydraulic resistance. In turn, portal pressure is elevated, leading to the potential recanalization of portosystemic collaterals. We extended a lumped parameter hydraulic network model of the human liver, with demarcation of Couinaud segments, to capture the effects of fibrosis by incorporating a portosystemic collateral pathway and an "apparent viscosity" formulation that accounts for the non-Newtonian properties of blood. Our simulations indicate that segment-wise reductions in mean sinusoid conductance, combined with increased heterogeneity, drive the flow redistribution characteristic of the clinical "atrophy-hypertrophy complex". This results in diminished perfusion and wall shear stress in right-lobe segments, while flow and wall shear stress is preserved or even elevated in the left lobe. Sensitivity analysis suggests that the onset of portal hypertension is driven primarily by the reduction of mean sinusoid conductance, not increased heterogeneity. However, increased heterogeneity likely exacerbates liver dysfunction, as the model predicts that a disproportionately large fraction of blood passes through the relatively few sinusoids with the lowest hydraulic resistance. Additionally, results demonstrate that while the dilatation of portosystemic collaterals effectively offloads the portal system, it leads to a substantial increase in overall blood flow, contributing to hyperdynamic circulation (a hallmark feature of cirrhosis). These predictions provide quantitative, mechanistic insights into the hemodynamic and anatomical alterations of cirrhosis, offering a computational framework that helps explain clinical observations and may assist in future patient-specific surgical planning.
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.
Ventricular tachycardia following myocardial infarction is often sustained by complex reentrant circuits that are challenging to characterize and treat using conventional electroanatomical mapping. Computational modeling provides a powerful complementary approach to understanding conduction pathway dynamics more effectively and supporting ablation strategies. Here, we present a reproducible and data-driven clinically guided computational framework for the retrospective analysis of post-infarction ventricular tachycardia and ablation procedures. The method integrates patient-specific electroanatomical mapping data-including local activation times, voltage maps, and electrograms-to build a personalized model that captures both structural and functional remodeling via a viability-based scalar field. A novel calibration procedure is introduced to locally estimate tissue conductivity, enabling accurate reproduction of observed activation patterns. The model is used to simulate arrhythmia inducibility and sustainability, and to retrospectively evaluate the impact of clinical radiofrequency ablation, accounting for lesion size and transmurality. In silico exploration of alternative ablation strategies is also performed to minimize lesion volume while maintaining arrhythmia suppression. The entire workflow is designed for rapid execution using a GPU-accelerated monodomain solver and is fully compatible with existing clinical practices, offering a practical tool for substrate interpretation and patient-specific ablation planning.
Abdominal aortic aneurysm (AAA) is often asymptomatic, making early detection challenging. As the aneurysm expands, it alters the hemodynamic forces exerted on the arterial wall, which in turn influence disease progression. This study investigated longitudinal association between geometric structure and hemodynamics, along with changes in maximum diameter, volume, total surface area, and aneurysm sac surface area, using a longitudinal dataset of 36 computed tomography angiography (CTA) scans collected from eight patients over multiple time points. Hemodynamic parameters-including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), endothelial cell activation potential (ECAP), velocity shear rate, and pressure-were derived via computational fluid dynamics (CFD) simulations. These parameters were analyzed alongside geometric indices such as the asymmetry index, saccular index, tortuosity index, and deformation diameter ratio. To account for the longitudinal nature of the data and inter-patient heterogeneity, a linear mixed model (LMM) was employed for the statistical analysis. Results revealed that across the AAA region, TAWSS and v ˙ mean decreased significantly during aneurysm expansion, while ECAP and pressure showed strong positive associations with marginal R 2 values exceeding 0.9 (e.g., ECAPmax R 2 = 0.977; Pressuremax R 2 = 0.979). Geometric factors like the saccular index and deformation diameter ratio also strongly associated with aneurysm growth ( R 2 > 0.94), whereas the asymmetry index showed no significant relationship. These findings suggest that longitudinal associations exist between aneurysm morphology and CFD-derived hemodynamic surrogate measures, providing complementary descriptive information alongside conventional morphology-based assessment.