Biomechanics and Modeling in Mechanobiology最新文献

False lumen expansion is a major factor that determines long-term survival of uncomplicated type B aortic dissection (TBAD). The objective of this study was to investigate whether structural wall stress distributions computed from patient-specific acute TBAD geometries can be used to predict aortic growth rates. Three-dimensional (3D) computed tomography angiography (CTA) of 9 patients with acute uncomplicated TBAD was obtained at initial hospital admission and at their most recent follow-up visits. Patient-specific structural wall stress distributions were computed from the initial baseline CTA using a forward penalty method. Spatially varying blood pressure distributions, derived from computational fluid dynamics (CFD) simulations informed by patient-specific brachial blood pressure (BP) measurements, were incorporated into the forward penalty stress analysis. For 5 patients, transthoracic echocardiography (TTE) data were also available and used to prescribe patient-specific inlet flow conditions in the CFD simulations. Aortic growth rates were quantified and visualized within the 3D TBAD geometries using the initial baseline and follow-up scans. Linear mixed-effects regression analyses were performed to evaluate the spatial correlations between biomechanical markers (structural wall stress, wall shear stress, and pressure) and aortic growth rates. Utilizing initial baseline patient-specific CTA and BP data, along with TTE data when available, the forward penalty analyses revealed hemodynamic and structural mechanics insights of acute uncomplicated TBADs. The linear mixed-effects model indicated that the fixed-effect association between acute structural wall stress and estimated aortic growth rate distributions was statistically significant (p = 0.036), which demonstrated that aortic segments experiencing higher structural stress in the acute phase exhibited more rapid growth. Fixed-effect associations were not significant when predicting growth rate using wall shear stress (p = 0.88) or pressure (p = 0.65) distributions computed from the acute TBAD geometry. Significant Pearson correlation coefficients (p < 0.05) were observed between acute structural wall stress and aortic growth rate in all patients. Higher structural wall stress in the acute TBAD geometry was associated with regions of increased aortic growth rates. When modeled as a solid, false lumen thrombus was linked to lower structural wall stress and may have a protective effect against rapid aortic growth. Further studies are needed to investigate the biphasic nature of thrombus. Structural stress, which in this study was derived using the forward penalty approach, may be a novel predictor of aortic growth rate in acute TBAD.

The simulation of tissue perfusion based on highly detailed synthetic vasculature that often consists of multiple supplying and draining trees with millions of vascular segments is computationally expensive. Converting highly detailed synthetic vasculature into a homogenized continuum flow representation offers a computationally efficient alternative. In this paper, we investigate such a modeling approach that retains the essential features of potentially deforming hierarchical vascular networks. It is based on multi-compartment homogenization, where each compartment represents homogenized perfusion via a Darcy-type flow model associated with vascular segments at a specific spatial resolution in one individual tree of the network. The compartments are coupled through a pressure-dependent mass exchange, applied in a smeared manner everywhere within the perfusion domain. Key parameters, namely the permeability tensors of each compartment and the intercompartmental perfusion coefficients, are estimated directly from the vascular segments of the synthetic vasculature using averaging techniques. Our approach leverages spectral decomposition and a reduced set of representative vessel segments to balance computational efficiency with physical fidelity. For scenarios involving deformation, such as in a pumping heart or a regenerating liver, we introduce a computationally efficient parameter update based on geometric mapping, avoiding full re-homogenization in nonlinear simulations. We demonstrate the effectiveness and accuracy of the approach for several benchmark examples, including a full-scale multi-compartment liver perfusion simulation that explicitly incorporates three non-intersecting vascular trees, reflecting the hepatic artery, portal vein, and hepatic vein.

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.

Pulmonary arterial hypertension (PAH) induces chronic pressure overload on the right ventricle (RV), leading to progressive remodeling and eventual failure. While PAH is more prevalent in women overall, men and postmenopausal women have worse clinical outcomes. Here, we investigated how sex and ovarian hormones influence RV remodeling during the progression of PAH. Using the sugen–hypoxia (SuHx) rat model, we assessed RV hemodynamics, tissue mechanics, and collagen composition in male, ovary-intact female, and ovariectomized (OVX) female rats across four disease stages. While all three groups experienced elevated pulmonary and ventricular pressures and rapidly responded with hypertrophy and stiffening, RV remodeling progressed differently in the absence of ovarian hormones. Male and OVX rats exhibited marked increases in end-diastolic pressure and myocardial stiffness, as well as higher chamber elastances. Ovary-intact female rats largely preserved diastolic function with milder stiffening. Collagen accumulation was observed in all groups, but only male and OVX rats exhibited significant elevations in pyridinoline cross-linking—aligning with the most severe additional mechanical changes, namely increased passive stiffness. This suggests that ovarian hormones moderate the severity of SuHx-induced RV remodeling by limiting myocardial stiffening and collagen cross-linking. These findings emphasize the need to consider sex and hormonal status in preclinical PAH research and suggest that extracellular matrix cross-linking may be a targetable contributor to maladaptive right heart remodeling.

The tympanic membrane (TM) plays a pivotal role in auditory transduction by converting acoustic signals into mechanical energy. Damage to the TM, whether from chronic perforations, infections, trauma, or pathological changes such as tympanosclerosis, can significantly impair its biomechanical function and lead to conductive hearing loss. Tympanoplasty remains the standard intervention, traditionally utilising autologous grafts such as temporalis fascia or cartilage. However, these biological materials present limitations, including donor site morbidity, variable mechanical properties, and potential for long-term resorption. To overcome these challenges, synthetic scaffolds engineered from polymers such as polylactic acid (PLA), polycaprolactone (PCL), and silk fibroin have emerged as promising alternatives. This review provides a comprehensive examination of the mechanical characteristics of both healthy and diseased tympanic membranes, offering insights into their structural integrity and mechanical performance. Emphasis is placed on key parameters such as thickness, Young’s modulus, tensile strength, strain tolerance, and viscoelastic behaviour, drawing from ex vivo and in vivo studies. The discussion extends to computational strategies, particularly finite element modelling (FEM) and inverse FEM, which enable accurate simulation of TM responses under physiological and pathological conditions. By linking empirical mechanical data with computational analyses, this review supports the rational design of synthetic grafts that closely mimic native tissue properties. Additionally, emerging trends in TM tissue engineering, including 3D printing and biomimetic scaffolds, are also highlighted for their potential to improve surgical outcomes in tympanoplasty.

The abnormal accumulation of low-density lipoprotein (LDL) can lead to aortic atherosclerosis. However, the aortic LDL transfer and its relationship with hemodynamics are still not fully explored. This study aims to reveal the mechanism of heat and LDL transfer in healthy aortas, leveraging a fluid–structure interaction (FSI) model. Two healthy aortic geometry models were reconstructed based on clinical computed tomography angiography images. The flow rate used as the inlet boundary condition was taken from another previous publication, and non-invasive blood pressure measurement data were exploited to determine the parameters of the three-element Windkessel model for boundary conditions of the aortic outlets. The aortic wall was assumed to be uniform in thickness, and the hyperelastic material was simulated by Yeoh second-order model. Our two-way FSI method was further developed to predict the heat and LDL transfer. Results show that the correlation coefficients of time-averaged LDL, temperature, and wall shear stress (WSS)-related indices between the rigid and hyperelastic aortic wall are high (> 0.914) except for the topological shear variation index (TSVI). The interaction between the blood flow and the aortic wall is suggested to be considered to accurately capture the distribution of oscillatory shear index, relative residual time (RRT), and TSVI. Besides, we find that there is a positive correlation (> 0.596) between the concentration of LDL and aortic wall temperature. The long RRT region also coincides with the high LDL area, which negatively correlates with WSS and TSVI. This study demonstrates the heat and LDL transfer in healthy aortas using the FSI model, and the findings would inform novel strategies to measure LDL concentration and regulate its accumulation.

Large-scale axonal dynamic simulation is critical to study white matter injury but is prohibitive in computational cost. We solve this challenge by training a convolutional neural network (CNN) that takes fiber strain profiles as inputs to instantly estimate multimodal axonal injury parameters. First, tractography-based fiber strains are derived based on subject-specific simulations of N = 46 head impacts from a male ice hockey player. To generate the minimum training dataset, the brain is subdivided into coarse cubes (isotropic resolution of 6 mm; N = 4979 voxels). A stratified (one sample per cube) and adaptive (by controlling a similarity threshold) sampling strategy is devised to iteratively identify the most distinct profiles from N = 45 head impacts used for training (with the remaining one reserved for independent validation). They serve as the input to a male axonal injury model for simulation. A CNN is then trained to estimate the peak strains in microtubule and axolemma as well as the failure percentages of tau proteins and neurofilaments. The CNN is cross-validated to determine the minimum training samples of N = 2000 to reach ({R}^{2})>0.90. Under the “worst case scenario” for independent validation (N = 75 testing samples identified), the CNN achieves an ({R}^{2}) of 0.91–0.98 and a normalized root mean-squared error (NRMSE) of 2.7–5.0%. Finally, we showcase the CNN by generating high-resolution multimodal axonal responses for the entire white matter within 12 s (isotropic resolution of 2 mm with ~ 92,500 voxels), vs. an estimated ~ 12 years using conventional direct simulations (~ 31.5-million-fold efficiency gain). This study demonstrates the potential of deep learning to enable large-scale mechanistic investigations of white matter injury in the future.

Aortic valve replacement is a cornerstone treatment for severe aortic valve diseases, including stenosis and regurgitation. Suboptimal valve seating can elevate the transvalvular pressure gradient, while valve orientation and size may produce flow jets that impinge on the ascending aorta, potentially weakening the vessel wall. Such hemodynamic complications can compromise valve performance and patient outcomes. This study presents a computational fluid dynamics framework, derived from medical CT images, for preprocedural hemodynamic assessment of aortic valve replacement. The framework minimizes user input and delivers rapid results, enabling efficient evaluation of valve types, orientations, and their hemodynamic impact. The results demonstrate that non-optimal implantation angles substantially increase pressure drop across the valve, thereby imposing higher workload on the heart. This automated and efficient simulation framework demonstrates strong potential for clinical application, supporting precise planning and execution of valve implantation procedures to improve patient care.

Osteoarthritis induces profound structural degeneration of articular cartilage, with existing treatments remaining largely ineffective. This study pioneers a mechanoregulatory model utilizing histology-based finite element analysis to predict depth-dependent glycosaminoglycan (GAG) adaptations in both intact and damaged human cartilage under mechanical loading. Uniquely calibrated through rigorous one-week longitudinal in vitro experiments in intact cartilage, our model correctly predicts depth-dependent GAG content adaptation, also in damaged cartilage. Notably, the model reveals potential effects of fluid velocity and dissipated energy on an increase in GAG content, while highlighting the degenerative effects of maximum shear strain under physiological loading conditions. Interestingly, it replicates enhanced GAG production in damaged cartilage, consistent with our experimental observations. Beyond advancing the fundamental understanding of mechanical loading in cartilage homeostasis, this innovative model offers a robust platform for in silico trials, enabling the development of personalized rehabilitation protocols to optimize mechanical loading strategies for degenerative joint diseases. Our work represents a significant leap forward in leveraging computational tools to address the challenges of osteoarthritis treatment. All findings are based on human explants from one donor and should be interpreted as preliminary proof-of-concept.