Biomechanics and Modeling in Mechanobiology最新文献

Computational fluid dynamics (CFD) simulations were conducted to explore the impact of catheter angle on microsphere distribution in transarterial radioembolization (TARE). The models were designed to closely replicate conditions from previously conducted ex vivo machine-perfused porcine liver experiments and therefore improve the perspective on microsphere distribution patterns. TARE with holmium-loaded microspheres was performed on an ex vivo machine-perfused porcine liver. Catheter position was imaged using angiography and ultrasound, the hepatic arterial vasculature using 3D cone beam CT, and lobe-specific microsphere distributions using holmium-sensitive T2*-weighted MRI scans. An in-house CFD model based on lattice Boltzmann and discrete element methods was developed. Simulations were parameterized with ex vivo porcine liver geometry and flow conditions as input. To further explore the role of catheter angles beyond experimental findings, four catheter angles (0°, 15°, 30°, and 45°), gradually directed toward the hepatic arterial wall, were evaluated using CFD. Ex vivo ultrasound imaging showed that the catheter was oriented at an angle of 25.2° toward the hepatic arterial wall. The simulations revealed that catheter angle strongly influences microsphere distribution, particularly in the branches closest to the injection position (lobe 3–5). The correlation between ex vivo and simulated microsphere distributions increased with catheter angle: from poor at 0° (r = 0.123), to moderate at 15° (r = 0.511), to very strong at 30° and 45° (r = 0.809 and r = 0.807, respectively). A catheter angle directed toward the hepatic arterial wall can affect microsphere distribution. In current clinical practice, the radial catheter position is not routinely visualized, complicating the reproducibility of catheter tip positioning between scout and therapeutic doses. Future research could focus on improved catheter tip visualization and the development of more stable catheter designs to enhance the reproducibility and efficacy of TARE.

In primary mitral valve regurgitation (MR), a failing valve induces volume overload (VO) of the left ventricle (LV), triggering neurohormonal responses and myocardial remodeling. Surgical valve repair is the most effective therapy for MR, improving symptoms and in some patients reversing cardiac hypertrophy. However, at least 20% of patients develop postoperative impairment of LV function with detrimental effects on long-term outcomes, with no known cause. Many MR patients receive pharmacological treatment to alleviate symptoms, with drugs that alter cardiac function, hemodynamics, and molecular pathways that influence cardiac remodeling. To support the advancement of medical management of MR, we developed a multiscale model of cardiac hypertrophy that links ventricular mechanics, cardiovascular hemodynamics, and cardiomyocyte molecular signaling. We calibrated the model using the Markov chain Monte Carlo algorithm, a machine learning method, to integrate data from 76 studies of experimental VO in animals. The model’s predictions showed good agreement with experimental data on MR in dogs, including the chronic effects of four drugs on MR-induced hypertrophy. When simulating valve repair, our model suggests that full reversal of LV hypertrophy requires both the mechanical elimination of VO and the restoration of normal neurohormonal receptor activity. Finally, we explored selecting subsets of a large pool of pre-computed simulations to personalize model predictions for a small population or individual where insufficient data are available to refit the entire model.

Parkinson’s disease (PD) is a progressive neurological disorder often treated with Deep Brain Stimulation (DBS), a surgical procedure that involves implanting electrodes to improve motor function. Accurate electrode placement is critical for treatment efficacy but can be influenced by tissue deformation, insertion forces, and the mechanical properties of brain tissue. In this study, we investigated the role of layer interface angle as a controlled biomechanical factor affecting insertion forces and electrode deviation. A two-layer agar gel phantom was constructed to produce a prescribed stiffness transition between a lower-stiffness and a higher-stiffness gel layer, and its small-amplitude dynamic shear-stiffness contrast was characterized using magnetic resonance elastography (MRE) at 100 Hz. A stage-wise analytical model was formulated to describe the Z-direction axial force–depth response ({F}_{z}left(zright)) during indentation, fracture, relaxation, and frictional sliding, and a hybrid physics-guided residual neural network (PGNN) was explored as a secondary residual-correction approach for the remaining axial-force model discrepancies. The lateral interface-force change ({F}_{iy}) was obtained directly from the measured Y-direction force signal, while electrode deviation was quantified in a separate series of phantom experiments. Insertion experiments at interface angles of 0°, 15°, 30°, 45°, 60°, and 75° demonstrated that the lateral (Y-direction) interface force increased from 0.886 mN at 0° to 7.72 mN at 75°. For the deviation analysis, the mean value of the 0° group was defined as the zero reference; relative to this baseline, the largest group mean deviation was 0.0529 mm at 75°. These findings highlight the role of interface geometry in probe trajectory stability and provide a mechanistic baseline for future studies of probe insertion in two-layer media.

Rapid surrogate modeling of knee joint stress fields requires informative input representations, but explicit load information is often difficult to obtain reliably in practical settings. Whether physiological signals can provide complementary information for stress-field prediction when explicit load information is unavailable, and whether low-dimensional muscle synergy is more suitable than surface electromyography (sEMG), remain unclear. Nine healthy adult male participants performed running trials with synchronized kinematics, ground reaction forces, and 12-channel sEMG. Finite element simulations driven by joint posture and reaction forces were converted into fixed-topology graph samples with tissue-level von Mises stress labels. Using a shared MeshGraphNet backbone and grouped threefold cross-subject validation, six input conditions were compared: full pose–load input, pose only, pose + real sEMG, pose + real muscle synergy, and their shuffled physiological-control counterparts. Performance was evaluated using whole-field error, high-stress tail error, hotspot-region overlap, hotspot localization, and real-specific gain metrics. Full pose–load input showed the most favorable overall prediction performance, although its advantage over pose only varied across metrics. Adding sEMG linear envelopes did not improve the pose-only baseline and generally increased prediction errors. In contrast, muscle synergy showed performance generally comparable to pose only and outperformed sEMG linear envelopes across global metrics and several high-stress and hotspot-related measures. Shuffled-control analysis showed no consistent advantage of the real muscle synergy input over its shuffled counterpart; positive median gains in the selected global and hotspot-overlap metrics did not reach statistical significance after correction. The NMF-derived muscle synergy representation was more suitable than sEMG linear envelopes as a physiological auxiliary input for load-limited surrogate modeling of knee joint stress fields. These findings may inform physiological input design for rapid stress-field prediction when explicit load information is unavailable or unreliable.

To address the inadequate representation of key physical processes in numerical simulations of transcatheter aortic valve replacement, a high-fidelity mechanical analysis framework that more closely approximates clinical reality has been developed. Based on a comprehensive structural model incorporating the skirt, leaflets, and stent, this framework incorporates the clinically standard 18 Fr crimping dimension and a release mechanism based on axial extraction from the capsule. It systematically investigates the coupled effects of full crimping to clinical standards, deployment strategies, and implantation depth on the mechanical behavior of the valve. The results demonstrate that deployment approaches based on incomplete crimping and radial recoil fail to capture the pronounced spatiotemporal non-uniformity observed in realistic deployment processes. In contrast, the axial pullback mechanism induces multi-stage stress peaks and complex contact evolution, significantly enhancing the anchoring performance between the stent and the vascular wall. Furthermore, implantation depth modulates structural constraint conditions, driving a transition from localized load-bearing to global cooperative load sharing. While this improves overall stability, it also introduces potential physiological risks. Notably, quantitative analysis based on eccentricity reveals a clear correspondence between stent cross-sectional deformation and its geometric configuration. Local geometric features are shown to dominate the post-expansion shape distribution and directly influence the uniformity of radial deformation. Paravalvular leakage exhibits pronounced spatial heterogeneity, with its formation governed by the combined effects of structural interference and contact uniformity. These findings reconstruct the physical picture of the TAVR deployment process from a mechanical perspective, providing new theoretical insights for valve design optimization and patient-specific implantation strategies.

Cardiac strains provide significant information to evaluate cardiac performance. They can be evaluated using full field voxel-wise displacements measured, for example, using displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) or from features and textures that are measured using Cine MRI. While DENSE MRI provides a more complete description of myocardial motion, it remains challenging to acquire and its adoption is not widespread. In contrast, Cine MRI is routinely acquired, and Cine-based cardiac strains could be more easily adopted in a clinical setting. To evaluate the feasibility of automatically estimating cardiac strains from Cine MRI, we compare slice-wise circumferential, longitudinal, and radial strains obtained with three mid-ventricular models: a two-slice DENSE model, a one-slice DENSE model, and a one-slice Cine model. Strains are computed after automatic image segmentation, and the models are evaluated using a computational deforming phantom and data acquired in forty ((N=40)) healthy volunteers. When evaluated against the computational phantom, all models perform well in terms of circumferential strains, while differences are present in longitudinal and radial strains depending on model and location. When applied to volunteer data, the one-slice Cine model agrees reasonably well with the DENSE-based models in terms of endocardial circumferential strains while, overall, it leads to higher (in magnitude) radial and longitudinal strains, and lower (in magnitude) epicardial circumferential strains. The DENSE models lead to nearly identical estimates for circumferential and radial strains, while longitudinal strains are not correctly estimated by the one-slice DENSE model paired with automatic segmentation. The strain differences are discussed both group-wise and at the individual subject level.

Patellar osteochondral allograft (OCA) transplantation treats focal cartilage defects, but donor-recipient cartilage thickness and mechanical compatibility are not routinely quantified during graft selection. This idealized computational study evaluated associations of donor-to-recipient (D/R) thickness ratio, absolute stiffness, stiffness contrast, and depth-dependent stiffness with interface-local strain. Two-dimensional axisymmetric finite element models were subjected to a spatially uniform nominal pressure of 1.00 MPa. Nine D/R ratios (0.33–3.25) and eleven homogeneous donor/recipient (DC/RC) Young’s modulus combinations (2.50-7.00 MPa) yielded 99 homogeneous cases; nine functionally graded material (FGM) cases were evaluated. Primary outcomes were 95th percentile (P95) values of the nodewise compressive strain magnitude and tensor maximum shear strain within a fixed 0.25-mm local interface region of interest (ROI). This robust upper-tail measure is less sensitive than a single nodal maximum. In the matched homogeneous control, P95 values were 0.031 and 0.015. The largest homogeneous values were 0.412 and 0.347 at D/R=3.25 and DC/RC=7.0/2.5 MPa. Same-side concentration factors normalized local P95 to median strain in the same model’s 0.75-1.00 mm far-field band. Pooled factors were 3.71 ± 2.04 for compression and 6.03 ± 3.79 for maximum shear in homogeneous cases (n = 198 model-sides), and 3.51 ± 2.17 and 5.69 ± 4.03 in FGM cases (n = 18). FGM values were lower than both extreme homogeneous mismatch references at every D/R ratio. Across formulations, thickness disparity remained associated with interface-local upper-tail strain. These results support evaluating donor-recipient cartilage thickness and material compatibility alongside surface congruity and osseous integration in future experimental and patient-specific modeling studies.

Red blood cells play a dominant mechanical role in blood flow and strongly influence the transport and arrest of circulating tumor cells during metastasis. Circulating tumor cells (CTCs) are subject to physical interactions with blood components and adhesive interactions with endothelial cells, both of which critically influence their margination and likelihood of arrest. However, the impact of red blood cells (RBCs), the most abundant cellular component of blood, on CTC adhesion remains understudied. Multiscale computational methods provide a unique platform for investigating RBC–CTC interactions by enabling precise parameter control and quantitative analysis. Nevertheless, traditional explicit simulations have been limited in physiological relevance due to the high computational demand of modeling sub-micron scale adhesion dynamics in millimeter-scale vasculature. Here we employ and extend an adaptive physics refinement (APR) framework that couples a high-resolution region of interest to a coarsely resolved bulk fluid domain. We incorporate both RBC interactions and adhesive dynamics in the high-resolution region, enabling simulation of adhesive CTC transport with drastically reduced computational memory. Simulation results demonstrate that RBC–CTC interactions and RBC orientation significantly impact CTC trajectory and adhesive behavior. Compared to traditional explicit fluid-structure-interaction methods, the APR approach results in an order of magnitude decrease in memory usage, enabling analysis of larger vessel domains pertinent to metastasis. By providing a computationally efficient tool for simulating RBC interactions and adhesive dynamics, the extended APR framework paves the way for systematic investigation of tumor cell transport with increased physiological relevance.

In early years of life, the cranium rapidly changes in size and shape to accommodate brain growth, primarily driven by mechanical stress from brain expansion. Developmental disorders such as premature fusion of sutures in craniosynostosis disrupt normal growth process, leading to abnormal cranial shapes. Thus, understanding the interplay between biomechanical forces, soft tissues, and individual bone plates is crucial for understanding their role in shaping infant cranial development. This study develops a mechanically driven growth model to simulate healthy cranial growth in the first year. The algorithm considers simultaneous and coupled growth of brain, cranial bones, sutures, with volumetric brain expansion as the primary driver, with strain-based feedback governing growth in bone and suture tissues. A bulk bone formation approach accounts for evolving mechanical properties, with elastic moduli of bone and sutures increasing monthly. The growth algorithm was applied on an idealised geometry model of a healthy cranium, then on individual fused sutures cases, and dysmorphologies due to craniosynostosis were developed. The results showed good agreement with the typical features of clinical observations. Stress at bone–suture interfaces and elevated contact pressure under fused sutures highlighted biomechanical impacts due to the disorders. Sensitivity analysis explored how material properties and growth rates affect cranial morphology in the absence of longitudinal clinical data. This framework enhances understanding of cranial growth and is potential to be further modified and validated to support treatment planning for craniosynostosis.

The Achilles tendon is the thickest tendon in the human body, and it is composed of three subtendons connected to the calf muscles (soleus, medial and lateral gastrocnemius). This study aimed to evaluate the importance of explicitly modeling subtendons and collagen fiber orientation on the predicted strain distribution within tendon tissue during mechanical loading. Magnetic resonance images from seven subjects were used to create subject-specific finite element models. The segmented tendons were modeled in two ways: the entire tendon as one structure, then dividing it into the three subtendons. A fiber-reinforced poro-visco-hyperelastic material model was used to describe the mechanical response. The collagen fibers were first oriented in the proximal–distal direction, then twisted following the subtendon orientation. Principal strains were analyzed, assessing the impact of subject-specific geometry on strain magnitudes, localization and distributions. Average strains were similar across subjects (coefficient of variation = 18%), but location and magnitude of peak strains were subject-specific. While models including the subtendons predicted similar average strain values to those without, local heterogeneity increased substantially in the subtendon model (Full-Width at Half-Maximum increased from 0.03 to 0.08). Altering the collagen fiber orientation shifted the localization of higher strains (Full-Width at Half-Maximum = 0.11). In conclusion, both subtendons and realistic fiber orientation should be considered when modeling the mechanical behavior in the Achilles tendon as it strongly affects the strain distribution within the tendon. Accurate prediction of strain localization and distribution is important for further studies of tendon mechanobiology and for designing more effective rehabilitation protocols.