Biomechanics and Modeling in Mechanobiology最新文献

Understanding how demographics and risk factors differentially affect the nonlinear orthotropic mechanical properties of human femoropopliteal arteries (FPAs) is critical for improving computational models of device–artery interactions. However, directly assessing these effects is challenging due to their intricate interrelationships and multifaceted impact on arterial mechanics. This study aimed to quantify the independent and combined effects of aging, diabetes, obesity, hypertension, and coronary artery disease on the mechanical behavior of human FPAs using machine learning (ML). FPAs from 450 tissue donors 12–99 years old (average age 51 ± 18 years, 66% male) were evaluated using multi-ratio planar biaxial extension. ML models, optimized through hyperparameter tuning and cross-validation, were used to describe subject-specific nonlinear orthotropic arterial properties based on demographics and risk factors. Age accounted for 60% of the variability in arterial mechanics and had a stronger influence longitudinally than circumferentially. The presence of diabetes and coronary artery disease each added 13 and 11 years to vascular age circumferentially but < 3 years longitudinally. Obesity and hypertension each added 4 years to vascular age circumferentially and less than 3 years longitudinally. Compound effects of diabetes, hypertension, and obesity aged the artery more than 21 years circumferentially and 7 years longitudinally. These findings highlight the differential impact of risk factors on orthotropic arterial mechanics and demonstrate the potential of our approach for predicting subject-specific vascular properties. Incorporating these findings into computational models can enhance the accuracy of device–artery interaction simulations by accounting for individual-specific vascular characteristics.

The tracheal stent is one of the treatment modalities for tracheal stenosis. However, the mismatch of mechanical properties between the tracheal stent and the trachea may lead to stent migration. The aim of this study is to design a tracheal stent with J-shaped load–deformation behavior based on a multi-objective optimization method. Four design parameters were selected as optimization variables. The optimization objectives were the loads at 5%, 10%, 15%, and 20% deformation during uniaxial tensile test. The optimal Latin hypercube sampling was used to generate training samples, and Kriging surrogate model was constructed between tracheal stent design parameters and mechanical properties. An optimized stent model was established after obtaining the optimal stent design parameters by NSGA-II algorithm. Additionally, a commercial silicone stent model was established as the control. The results indicate that ligament angles and the width of circular arc connected ligaments play a prominent role in the load–deformation curve of the stent. The radial supporting performance (39.79 MPa vs. 4.63 MPa) and anti-migration properties (16.1 N vs. 13.7 N) of the optimized stent are superior to those of the silicone stent. This work demonstrates that a tracheal stent exhibiting a J-shaped load–deformation behavior was designed, which could reduce stent migration.

Developing clinically viable tissue-engineered structural cardiovascular implants—such as vascular grafts and heart valves—remains a formidable challenge. Achieving reliable and durable outcomes requires a deeper understanding of the fundamental mechanisms driving tissue evolution during in vitro maturation. Although considerable progress has been made in modeling soft tissue growth and remodeling, studies focused on the early stages of tissue engineering remain limited. Here, we present a general, thermodynamically consistent model to predict tissue evolution and mechanical response throughout the in vitro maturation of passive, load-bearing soft collagenous constructs. The formulation utilizes a stress-driven homeostatic surface to capture volumetric growth, coupled with an energy-based approach to describe collagen densification via the strain energy of the fibers. We further employ a co-rotated intermediate configuration to ensure the model’s consistency and generality. The framework is demonstrated with two numerical examples: a uniaxially constrained tissue strip validated against experimental data and a cruciform-shaped biaxially constrained specimen subjected to load perturbation. These results highlight the potential of the proposed model to advance the design and optimization of tissue-engineered structural cardiovascular implants with clinically relevant performance.

Alternatives to Diffusion-Tensor-Imaging tractography methods for determining fibre orientation fields in skeletal muscle include Laplacian flow simulations. Such methods require flux boundary conditions (BCs) at the tendons and/or along the inner aponeuroses, which can significantly influence the gradients of the resulting Laplacian flow. Herein, we propose a novel method based on solving the 3D steady-state thermal heat equations to determine the fibre architecture in a bi-pennate muscle, specifically the m. rectus femoris. Additionally, we propose a semi-automated algorithm that provides the geometrical representation of the anterior aponeurosis, which, along with the thermal-based fibre field, is particularly well suited for Finite Element (FE) simulations. The semi-automated reconstruction of the aponeurosis shows a good correlation with manual segmentation, yielding a dice coefficient (DSC) of 0.83. The metamodel-based approach resulted in fluxes with a mean angular deviation of (14.25^circ ,pm ,10.36^circ) and a fibre inclination from the muscle’s longitudinal axis of (0.44^circ ,pm ,4.48^circ). Comparing the mechanical output of the same m. rectus femoris muscle geometry informed by the two respective fibre architectures showed that the most significant contributing factor was the relative fibre inclination. Compared to the standard deviation in the undeformed configuration ((0.44^circ ,pm ,4.48^circ)), the standard deviation of relative fibre inclination during passive stretching at low applied loads, for instance, at (30%) of the maximum applied load, showed a significant decrease ((0.49^circ ,pm ,2.24^circ)). Similarly, at maximum isometric contraction, the relative fibre inclinations at (10%) initial fibre pre-stretch are (0.19^circ ,pm ,1.23^circ), indicating a drop in standard deviation from the undeformed configuration ((0.44^circ ,pm ,4.48^circ)). The current study demonstrates that despite the initial deviations in fibre orientations and relative fibre inclinations, thermal flux-based fibre orientations not only exhibit comparable results to DTI-based fibre tractography for the macroscopic analysis of the m. rectus femoris but also result in homogeneous stretch fields and improved numerical convergence. The proposed methods may be applied to determine inner aponeuroses of other bi- or multi-pennate muscles, enabling efficient in-silico computations of the musculoskeletal system.

Arterial growth and remodeling (G&R), in response to biomechanical stimuli, plays a pivotal role in vascular health. Disruptions in G&R, often seen in conditions such as aneurysms and atherosclerosis, can lead to pathological changes and pose significant health risks. Assessing risk should not only consider the current state of the aneurysm but also how it develops over the subsequent months. Herein, we make a controlled, subject-specific assessment of maladaptive aortic tissue growth using data previously obtained for the Fbln4^SMKO mouse model. The computational model uses a locally applied continuum G&R approach coupled with fluid–structure interaction (FSI) simulations. Ten mice were studied, exhibiting varying degrees of aneurysm formation over time. This investigation focused on the ascending aorta, where aneurysms develop in the Fbln4^SMKO mouse. A continuous G&R model was tuned and evaluated using information from 2, 4, and 6 months obtained from CT scans. A G&R model with uniform growth laws showed variable accuracy in predicting circumferential growth across different mice, exhibiting both under- and over-estimations compared to in vivo measurements. Modeling prediction showed to be improved by multiple-domain modeling. There is correlation between (1) the fitted circumferential growth time constants and the observed ascending aorta Young’s modulus and (2) the fitted axial growth time constant and the tortuosity index. Furthermore, the ratio of the circumferential growth time constant to the circumferential stress correlated with mouse lifespan more strongly than diameter change, suggesting that analysis of a G&R model may be valuable in predicting risk of aneurysm rupture.

The plasma membrane is a liquid lipid bilayer containing both dissolved proteins and proteins anchoring the membrane to the underlying actin cortex. Membrane tension, a 2D analog of pressure in a 3D liquid, is believed to play a crucial role in organizing essential processes within cells and tissues. This, along with recent, conflicting data on the speed of membrane tension propagation, highlights the need for a comprehensive mechanical model to describe tension in the cortex-anchored plasma membrane as a function of transmembrane hydrostatic pressure difference and excess membrane area due to cortex contraction. In this study, we present a mechanical model of plasma membrane compartments, separated by "picket fences" of cortex-anchoring proteins permeable to lipids. Beyond hydrostatic pressure, the model incorporates the 2D osmotic pressure exerted by membrane-dissolved proteins. Our findings reveal that the tension-area relationship within a membrane compartment exhibits a seemingly paradoxical feature: in a specific range of membrane surface area, an increase in area leads to a rise in tension. We further model the tension-area relationship for an ensemble of membrane compartments, which exchange membrane area through shared borders, and discuss potential biological implications of this model.

Biomechanical modeling of the human trunk is crucial for understanding spinal mechanics and its role in ergonomics and clinical interventions. Traditional models have been limited by only considering the passive structures of the spine in finite element (FE) models or incorporating active muscular components in multi-body musculoskeletal (MS) models with an oversimplified spine. To address those limitations, we developed a subject-specific coupled FE-MS model of the trunk and explored its applications in ergonomics and surgical interventions. A parametric detailed FE model was constructed, integrated with a muscle architecture, and individualized based on existing datasets. Our comprehensive validation encompassed tissue-level responses, segment-level mechanics, and whole-spine behavior across multiple subjects and loading conditions, demonstrating satisfactory performance in ergonomics (i.e., wearing exoskeleton) and clinical interventions (nucleotomy and spinal fusion). The model accurately predicted tissue-level stresses (in uni- and biaxial loading), whole-spine motion (i.e., moment rotation response was in agreement with in vitro measurements), intradiscal pressures (RMSE = 0.12 MPa; R² = 0.72), and muscle activities (matching EMG trends across 19 subjects during forward flexion). Wearing an exoskeleton reduced intradiscal pressures (1.9 vs. 2.2 MPa at L4–L5) and peak von Mises stresses in the annulus fibrosus (2.2 vs. 2.9 MPa) during forward flexion. Spinal fusion (at L4–L5) increased the intradiscal pressure in the upper adjacent disc (1.72 MPa vs. 1.58 MPa), but nucleotomy had a minimal effect on the intact intradiscal pressures. Nucleotomy substantially affected the load transfer at the same level by increasing facet contact loads and annulus radial strains. Unlike conventional MS models with simplified spine, and in contrast to passive models (without active components), this model provides crucial outputs such as strain/stress fields in discs/facets (essential for a comprehensive risk analysis). This integrated approach enables more accurate surgical planning, workplace safety design, and personalized rehabilitation strategies, helping reduce spine-related injuries by identifying risk factors and optimizing interventions for individual patients.

There is growing motivation to exploit computational biomechanical modeling of the heart as a predictive tool to support clinical diagnoses and therapies. Existing patient-specific cardiac models often rely on data collected under highly standardized conditions in hospitals. However, disease progression and therapy responses often depend on stressors, encountered in daily life, that cannot be captured in a traditional clinical setting. To achieve clinical translation, existing modeling frameworks must be refined and extended to include such influences. The “digital twin” concept, in which models of specific systems are continually updated with new data, is a promising avenue for integrating and interpreting these data streams. However, this endeavor calls for novel approaches to model development and data acquisition and integration. We review modeling approaches addressing specific stressor types (caffeine, exercise, sex-dependent factors, sleep, the environment) to identify knowledge gaps, assess emerging technical challenges, and suggest potential model developments to extend the scope and reach of biomedical cardiac simulations.

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