Biomechanics and Modeling in Mechanobiology最新文献

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.

The definition of the muscle insertion surface (MIS) is often simplified in micro-finite element (micro-FE) models despite its potential impact on the predictions of mechanical properties that are relevant to bone adaptation. This study investigated the effect of MIS size on local strain energy density (SED) in the mouse tibia. Micro-FE models of six mouse tibiae were developed from micro-CT images, and loading conditions were derived from musculoskeletal simulations of trotting gait. For the seven muscles with the highest contributions to tibial loading, three MIS configurations were tested: full anatomical surface, 50% reduced surface, and a single-node application. A total of 198 micro-FE models were run, both for individual muscle applications and multi-muscle configurations at key instants of the stance phase. SED values were compared across MIS conditions using the mean absolute difference, normalised by the median, mean, or 95th percentile of the SED. Significant differences in SED were observed between MIS configurations, with the largest effects near the MIS and boundary conditions. The effect of MIS modification on SED values was heterogeneous across the different muscles, highlighting the importance of anatomical features. Even with multiple muscle loading, SED differences exceeded 20% in most comparisons. These results demonstrate that simplifications of the MIS, especially to single-node applications, can considerably alter local SED predictions and are likely to compromise the accuracy of bone adaptation models. Accurate representation of the MIS is therefore essential for reliable mechanoregulation analysis and should be prioritised in multiscale models of skeletal loading.

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.

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.

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.

The efficacy of conventional chemotherapy in solid tumors remains limited due to tumor microenvironment barriers that impede efficient drug transport and compromise therapeutic outcomes. Thermosensitive liposomes (TSLs) combined with focused ultrasound-induced hyperthermia offer a promising strategy for localized, temperature-triggered drug release. Despite experimental progress, a quantitative understanding of the coupled physical and biological mechanisms underlying this therapy is yet to be fully elucidated. Here, a three-dimensional multiphysics computational model was developed to investigate the interplay between focused ultrasound-induced hyperthermia and temperature-sensitive liposome-mediated drug delivery in solid tumors, integrating acoustic propagation, tissue heating and temperature-dependent drug release. Model predictions were validated against published experimental data, demonstrating good agreement with tumor growth and intratumoral drug concentration data. Sensitivity analysis showed that focused ultrasound parameters and liposome properties strongly influence treatment efficacy. Prolonged focused ultrasound exposure (20–30 min) produced greater tumor reduction than frequency variations (2–5 MHz). Treatment timing was also critical: For highly proliferating tumors, early therapy yielded markedly improved outcomes. Faster drug release kinetics enhanced intracellular drug accumulation and tumor regression. Intermediate-sized TSLs (~ 50 nm in radius) achieved optimal efficacy under moderate vascular permeability conditions, while larger liposomes (~ 65 nm in radius) were more effective in tumors with highly permeable vessels due to increased extravasation. This work provides a computational framework with predictive potential for optimizing the combined focused ultrasound-thermosensitive liposomes therapy and assisting to the design of thermally triggered nanocarriers.

Cell culturing on soft substrates has become a widely applied method towards mimicking the compliant mechanical environment of cells in vivo, and substrate stiffness was shown to influence cell behavior. The cell-perceived stiffness, however, does not only depend on the modulus of the substrate material, but also on its thickness. In the present work, we propose an in silico approach to quantify the effect of substrate thickness on cell-perceived substrate stiffness. We coupled a discrete network representation of the cellular cytoskeleton including subcellular cell-substrate connections, i.e. focal adhesions (FA), with a continuum model of the substrate, and simulated the contraction of individual cells and cell patches. The results indicate that for substrates of heights larger than a multiple of the FA size (i.e. in the range of 5–10 (mu)m) the cell-perceived displacements and forces depend only on the substrate material, i.e. the latter is perceived as a semi-infinite continuum. The FA displacements depend more strongly on substrate thickness than FA forces. Based on the present model with subcellular resolution, the length scale of FAs emerges as the governing length scale for depth sensing, which is independent of the dimensions of the cells or cell patches.

Several methods exist for cell-level simulation of blood, due to the undeniable interest this topic holds in the biomedical field. Nevertheless, the amount of computational resources required for these resolved methods limits their application to large-scale problems. On the other hand, the single-phase approximation of blood as a non-Newtonian liquid, while attaining fast results, neglects its multiphase nature, hindering its application to all cases where the presence of cells cannot be overlooked. This work aims to bridge the gap between these two approaches by proposing an efficient upscaling strategy. An immersed boundary approach based on the lattice Boltzmann method (LBM), coupled with a nodal projective finite element solver (npFEM) for the dynamics of the red blood cell (RBC) membrane is adopted to construct a dataset for the properties of RBCs embedded in a flow. Different quantities such as RBC deformation and experienced drag and lift forces are then collected and utilized to derive closure models for an unresolved CFD–DEM solver, which allows the simulation of hundreds of thousands of RBCs with substantially lower computational costs. The strategy is tested against previous experimental studies and then used for real-scale applications such as the design of microfluidics devices for plasma separation and the investigation of channels with different degrees of stenosis mimicking cases of vein occlusion. The presented approach potentially allows cell-level blood flow simulations with up to O((10^6)) RBCs and returns accurate results when tested in scenarios including hundreds of thousands RBCs, as proven by our prior work. The original contribution of this upscaling approach is to bridge the scale separation gap in cell-level blood flow simulations. It holds considerable potential for the simulation of practical bio-physical problems, such as thrombus formation and the investigation of RBCs as biocompatible drug carriers.

Marfan syndrome (MFS) is a multisystemic connective tissue disorder caused by pathogenic variants of the gene encoding fibrillin-1, an important glycoprotein of the extracellular matrix. Among its diverse symptoms, the development of an ascending thoracic aortic aneurysm (ATAA) is the most concerning. An ATAA can fail due to dissection or rupture, both associated with substantial morbidity and mortality. Therefore, MFS patients typically receive medical treatment to slow aneurysm progression and reduce the risk of failure. However, the cellular mechanisms underlying ATAA development in MFS remain incompletely understood, reflected in suboptimal medical treatment options. To address this, we introduce a multiscale computational model of ATAA development in MFS mice as a reproducible, time- and cost-efficient complement to traditional animal experiments. The model implements a bidirectional coupling between a tissue-scale framework for aneurysm growth and remodeling and a cell-scale mechanobiological model for the ascending thoracic aorta. We calibrate and validate against experimental data from mouse studies capturing ATAA progression over time at both the tissue and cellular scales, either with or without pharmacological treatments. Following strong qualitative agreement with experimental observations, we employ the model for an in silico pharmacological treatment trial by simulating the inhibition or activation of various cell-scale model nodes. The simulations identify four novel medical treatments predicted to reduce the long-term failure risk of MFS-induced ATAAs, with inhibition of p38 mitogen-activated protein kinase emerging as the most promising option. Although simplified, the proposed model provides a robust, modular framework that can be readily extended or adapted in future research.