Biomechanics and Modeling in Mechanobiology最新文献

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.

Glioblastoma multiforme (GBM) remains a formidable challenge due to its aggressive proliferation, heterogeneity, and invasiveness. This review synthesizes biomechanical models for GBM prediction, from classic proliferation–invasion (PI) frameworks—based on reaction–diffusion equations—to continuum biomechanical models that quantify tumor-induced stress and tissue interactions. We highlight multiphysics approaches integrating fluid dynamics, nutrient transport, and solid mechanics to simulate the tumor microenvironment, alongside numerical methods like FEM and meshless techniques. Treatment modeling, including radiotherapy and emerging therapies, is critically evaluated for optimizing clinical strategies. Challenges in validation and parameterization are addressed, with a forward-looking emphasis on hybrid physics-informed and machine learning models to enable personalized prediction. By bridging biophysics, computation, and clinical needs, this work aims to guide future research toward improved GBM therapeutics.

The number of patients undergoing hemodialysis has been steadily increasing in recent decades. Arteriovenous fistula (AVF) is the gold standard for ensuring vascular access in these patients. Despite the prominent role of AVFs in hemodialysis treatment, their maturation and long-term functionality continue to pose challenges as less than a third of fistulas remain patent without further interventions in a 3-year follow-up. Computational biomechanics has become an essential tool for clarifying mechanical conditions accompanying the pathogenesis of various vascular complications, including suboptimal maturation and AVF stenosis. Constitutive description plays a crucial role in the design of computational models and without it simulations remain only at the rigid tube level. However, literature on the mechanical properties and constitutive modeling of upper extremity veins is lacking. This study aims to fill this gap by characterizing the mechanical properties of the human basilic vein (BV) and comparing it to the great saphenous vein (GSV). Uniaxial tensile tests in two perpendicular directions were used to obtain the mechanical response of the tissue. The results suggest that BVs do not significantly differ from GSVs in their elastic properties expressed by means of the tangent modulus. Overall anisotropy, understood as the difference in elastic moduli obtained in different directions, seems to be reduced in BVs. The 4-fiber family exponential model of the strain energy density function was adopted to fit the experimental data. The model fitted the data well, as suggested by the coefficients of determination R², which ranged from 0.97 to 0.99 for the majority of the average curves. The resulting parameter values can be used within the modeling of the mechanical behavior of veins in computational simulations of vascular access performance.

Venoarterial extracorporeal membrane oxygenation (VA ECMO) is an advanced life-saving therapy for patients with severe cardiopulmonary failure. Understanding the performance of the drainage cannula is critical to minimizing complications such as thrombosis formation, platelet activation, and circuit failure. This study utilizes computational fluid dynamics (CFD) to analyze the flow characteristics within the drainage cannula under both normal vessel conditions and vessel collapse scenarios. The simulations focus on flow behavior, shear stress distribution, and regions prone to platelet accumulation and thrombus formation. In the collapsed vessel scenario, significant alterations in flow patterns were observed, including elevated shear stress, increased velocities near the cannula tip, and flow redistribution along the cannula holes. While the collapsed condition exhibited higher mechanical platelet activation due to increased shear forces, improved washout resulted in a lower accumulation of activated platelets compared to the normal condition. Additionally, thrombosis-prone regions were identified, particularly near the cannula tip for normal drainage condition. The findings of this study highlight the fluid flow mechanisms contributing to thrombosis risk in the drainage cannula during VA ECMO. These insights can inform cannula design improvements to minimize thrombosis and optimize ECMO performance.

Modeling the mechanical behavior of human tissues, particularly tumor tissues, poses significant challenges due to the difficulty in acquiring samples. In this study, we performed a total of ten measurements on five freshly excised peritoneal metastasis samples, alongside ten measurements from two healthy colon samples, to develop mechanical models using the standard linear solid (SLS) model and its generalized forms. The peritoneal metastasis samples included colon cancer (2 samples), ovarian cancer (1 sample), and rectal cancer (2 samples). An ex vivo static indentation test was conducted to assess the stress relaxation behavior of both tumor and healthy tissues using a step indentation protocol. A novel cross-validation approach was employed for model selection, based on mean square error (MSE) values. Due to the irregularity and complexity of tumor tissues, 80% of the tumor measurements required more complex models with additional parameters compared to the healthy colon tissues. The five-element double Maxwell–Wiechert (DMW) arm model was suitable for describing the mechanical behavior of all healthy colon tissue measurements. In contrast, the seven-element triple Maxwell–Wiechert (TMW) arm model best described 80% of the tumor tissue measurements, while the DMW model was adequate for the remaining 20%. Further histopathological analysis of the tissue samples may help elucidate the relationship between biological composition and mechanical properties.

Computed tomography (CT)-based finite element (FE) models can non-invasively assess bone mechanical properties, but their clinical application in paediatrics is limited due to fewer datasets and models. Statistical Shape-Density Model (SSDM)-based FE models using statistically inferred shape and density have application to predict bone stress and strains; however, their accuracy in children remains unexplored. This study assessed the accuracy of stress–strain distributions estimated from SSDM-based FE models of paediatric femora and tibiae. CT-based FE models used geometry and densities derived from 330 CT scans from children aged 4–18 years. Paediatric SSDMs of the femur and tibia were used to predict bone geometries and densities from participants’ demographics and linear bone measurements. Forces during single-leg standing were estimated and applied to each bone. Stress and strain distributions were compared between the SSDM-based FE models and CT-based FE models, which served as the gold standard. The average normalized root-mean-square error (NRMSE) for Von Mises stress was 6% for the femur and 8% for the tibia across all cases. Principal strains NRMSE ranged from 1.2% to 5.5%. High correlations between the SSDM-based and CT-based FE models were observed, with determination coefficients ranging from 0.80 to 0.96. These results illustrate the potential of SSDM-based FE models for paediatric application, such as personalized implant design and surgical planning.