Biomechanics and Modeling in Mechanobiology最新文献

Blood clots’ mechanical properties are important in both their physiological role and in the initiation and progression of thromboembolic diseases. Because studying blood clot properties in vivo is difficult, many prior studies have investigated the properties of in vitro clots instead. However, much remains to be understood about in vitro clots, especially those derived from human blood. For example, the association between subject-specific factors and clot mechanical properties is currently unknown. Our objective is to fill this knowledge gap and study the sensitivity of in vitro blood clots to subject-specific factors, including sex, age, and blood composition. We drew blood from healthy adults aged 19–46, coagulated clots into mechanical test specimens, and characterized their properties. Specifically, we quantified clot stiffness, fracture toughness, contractility, and hysteresis. We then quantified the relative dependence of those properties on subject-specific factors, including sex, age, and blood composition. We found that there is significant variation in clot properties within healthy subjects. Clots from female subjects’ blood are stiffer, more resistant to fracture, and show more hysteresis compared to clots from male subjects. However, we found no association between clot properties and age and only a weak association with clot composition, e.g., hematocrit. Finally, even together, sex, age, and blood composition only moderately explain the observed variability in clot mechanical properties. Our work therefore suggests that in vitro clots may capture relevant information not reflected in standard clinical data. Future studies should investigate in vitro clots’ potential as biomarkers for thrombotic risk and treatment response.

According to various experimental studies, the role of axons in the brain's white matter (WM) is still a subject of debate: Is the role of axons in brain white matter (WM) limited to their functional significance, or do they also play a pivotal mechanical role in defining its anisotropic behavior? Micromechanics and computational models provide valuable tools for scientists to comprehend the underlying mechanisms of tissue behavior, taking into account the contribution of microstructures. In this review, we delve into the consideration of strain level, strain rates, and injury threshold to determine when WM should be regarded as anisotropic, as well as when the assumption of isotropy can be deemed acceptable. Additionally, we emphasize the potential mechanical significance of interconnections between glial cells-axons and glial cells-vessels. Moreover, we elucidate the directionality of WM stiffness under various loading conditions and define the possible roles of microstructural components in each scenario. Ultimately, this review aims to shed light on the significant mechanical contributions of axons in conjunction with glial cells, paving the way for the development of future multiscale models capable of predicting injuries and facilitating the discovery of applicable treatments.

Voronoi tessellation is a powerful technique for designing random structures for bone tissue engineering applications. In this study, an innovative algorithm for scaffold design that controls trabecular orientation while maintaining an overall random architecture is presented. Morphological analyses and numerical models were employed to comprehensively characterize the scaffolds. The results indicate that the effective stiffness and permeability of the scaffolds are directly influenced by the trabecular orientation. In contrast, other parameters, such as porosity, trabecular thickness, trabecular spacing, and curvatures, can be kept constant with respect to the trabecular orientation. These findings, in conjunction with mechano-biological considerations, provide a robust design workflow to optimize the micro-environment for bone growth. This framework offers a valuable tool for selecting the most suitable scaffold architecture according to the specific external loads, thereby enhancing the efficacy and reliability of bone scaffolds in clinical applications. Through this approach, the aim is to improve the precision and outcomes of bone tissue engineering, contributing to the development of advanced therapeutic solutions for bone repair and regeneration.

Several conditions can increase the incidence of vertebral fragility fractures, including metastatic bone disease. Computational tools could help clinicians estimate the risk of vertebral fracture in these patients; however, comparison with in vitro data is mandatory before using them in clinical practice. Nine spine segments were tested under compression and imaged with micro-computed tomography (µCT). The displacement field was calculated for each vertebra using a global digital volume correlation (DVC) approach. Subject-specific homogenised finite element models of each vertebra were built from µCT images, applying experimentally matched boundary conditions at the endplates. Numerical and experimental displacements, reaction forces, and locations showing higher strain concentrations were eventually compared. Additionally, given that µCT cannot be performed in clinical settings, the outcomes of a µCT-based model were also compared to those of a model built from clinical CT scans of the same specimen. Good agreement between DVC and µCT-based FE displacements was found, both for healthy (R² = 0.69 ÷ 0.83, RMSE = 3 ÷ 22%, max error < 45 μm) and metastatic (R² = 0.64 ÷ 0.93, RMSE = 5 ÷ 18%, max error < 54 μm) vertebrae. Strong correlations were found between µCT-based and clinical CT-based FE model outcomes (R² = 0.99, RMSE < 1.3%, max difference = 6 μm). Furthermore, the models qualitatively identified the most deformed regions identified with the experiments. In conclusion, the combination of experimental full-field technique and in-silico modelling enabled the development of a promising pipeline to validate bone strength predictors in the elastic range. Further improvements are needed to analyse vertebral post-yield behaviour better.

Objective

To understand regional biomechanical differences within the healthy and aneurysmal ascending aorta.

Methods

Aortic tissue was collected from the inner (IC) and outer (OC) curvature of aneurysms excised during elective surgery (n = 102) and normal aortas from organ donors (n = 25). Biaxial tensile testing and peel testing were performed to derive a comprehensive set of biomechanical parameters.

Results

In normal aortas, the OC exhibited greater energy loss, lower tangent modulus at low strain, and lower transition zone stress compared to the IC. In aneurysmal aortas, similar findings were observed. All IC and OC biomechanical parameters were linearly correlated in aneurysmal aortas, including delamination strength. Healthy and aneurysmal aortas exhibited similar degrees of difference between IC and OC for most biomechanical properties. Aneurysms with greater biomechanical differences between IC and OC trended toward being older (p = 0.096) with larger diameters (p = 0.051) compared to other aneurysms. Asymmetric bulging exhibited lower stiffness and transition zone stress in the OC, but no difference in delamination strength between regions.

Conclusions

Regional biomechanical differences exist in aneurysms of the ascending aorta to a similar extent as in healthy aortas. In aneurysms, biomechanical properties of the IC and OC regions were strongly linearly correlated, suggesting that the regional differences in ascending aortic biomechanics are less important than the large biomechanical variability that exists between patients.

Veno-Arterial Extracorporeal Membrane Oxygenation (VA-ECMO) is a type of mechanical circulatory support used, among others, in case of cardiogenic shock, consisting in percutaneous cannulation of the femoral artery. Despite the widespread use of this procedure in clinical practice, a deeper understanding of the complex interaction between native and ECMO output, as well as the fluid dynamics and perfusion of aorta and its branches is still required. Herein, a numerical and experimental approach is presented to model a VA-ECMO procedure on a patient-specific aortic geometry. For both approaches, cardiogenic shock was modeled by considering three different severities of left ventricular failure (mild, moderate, and severe), corresponding to a reduction in cardiac output of 30%, 50%, and 70% relative to the healthy condition, respectively. For each case, different levels of the ECMO support were simulated, ranging from 0 to 6 l/min. The performance of the VA-ECMO configuration was evaluated in terms of both afterload increase and flow at all aortic branches. Both methods highlighted the afterload increase in high levels of ECMO support. Furthermore, numerical and experimental data revealed the existence of a trade-off level of ECMO support that guarantees healthy perfusion of all vessels with the lowest afterload. This correlation opened a pathway for the definition of a tool for determining a suitable level of ECMO support on the basis of the knowledge of patient-specific data.

Musculoskeletal function is pivotal to long-term health. However, various patient groups develop torsional deformities, leading to clinical, functional problems. Understanding the interplay between movement pattern, bone loading and growth is crucial for improving the functional mobility of these patients and preserving long-term health. Multi-scale simulations in combination with a mechanobiological bone growth model have been used to estimate bone loads and predict femoral growth trends based on cross-sectional data. The lack of longitudinal data in the previous studies hindered refinements of the mechanobiological model and validation of subject-specific growth predictions, thereby limiting clinical applications. This study aimed to validate the growth predictions using magnetic resonance images and motion capture data—collected longitudinally—from ten growing children. Additionally, a sensitivity analysis was conducted to refine model parameters. A linear regression model based on physical activity information, anthropometric data and predictions from the refined mechanobiological model explained 70% of femoral anteversion development. Notably, the direction of femoral development was accurately predicted in 18 out of 20 femurs, suggesting that growth predictions could help to revolutionize treatment strategies for torsional deformities.

Lower urinary tract symptoms (LUTS), particularly urinary incontinence (UI), represent a significant global health challenge, affecting millions of patients worldwide. The artificial urinary sphincter (AUS) remains one of the most effective intervention for severe UI, with its design relying on a detailed understanding of the urethral biomechanics. Given the ethical and logistical constraints of using human tissue, porcine urethras, which share anatomical and mechanical similarities with human urethras, are widely employed in preclinical studies. This study investigates the uniaxial mechanical characterization of porcine urethral tissue under controlled conditions. Fresh porcine urethral samples were subjected to uniaxial tensile testing along both the longitudinal and circumferential directions to characterize their anisotropic mechanical properties. Experimental results were compared with existing datasets to validate findings. Additionally, conventional hyperelastic models were assessed to fit experimental results, and a novel anisotropic constitutive model with physical parameters was developed. This fiber model, which incorporates fiber modulus, volume, and orientation, uses a single set of parameters to predict behavior in both directions. It demonstrated improved accuracy, reaching the performance of the Gasser-Ogden-Holzapfel (GOH) model, with root mean square errors (RMSEs) of 9.24% and 12.98% in the circumferential and longitudinal directions, respectively. In contrast, the Yeoh and Ogden models were unable to fit both directions using a single set of parameters, yielding RMSEs values exceeding 30%. With its enhanced physical relevance, the fiber model having a more physical meaning holds promise for applications in the biomechanical analysis of fiber-composed soft tissues.

This paper presents a mathematical and numerical framework for modelling and parametrising tumour evolution dynamics to enhance computer-aided diagnosis and personalised treatment. The model comprises six differential equations describing cancer cell and blood vessel concentrations, tissue stiffness, Ki- 67 marker distribution, and the apparent velocity of marker propagation. These equations are coupled through S-functions with adjustable coefficients. An inverse problem approach calibrates the model by fitting adjustable coefficients to patient-specific clinical data, thereby enabling disease progression and treatment response simulations. By integrating historical and prospective patient data supported by machine learning algorithms, this framework holds promise as a robust decision-support tool for optimising therapeutic strategies.

Using finite element method (FEM) to compute wall stress is now a common way to assess ascending thoracic aortic aneurysms (ATAA) severity. Medical images can provide aortic geometry for FEM, but thickness information is lacked and the geometry is at loaded state. Therefore, in this study, an unloaded geometry with a non-uniform thickness distribution is reconstructed. Measurements of wall thickness are taken from resected tissue to accurately replicate the thickness distribution. Subsequently, a novel method, derived from the existing fixed-point iterative (FPI) approach, is developed and applied to estimate the unloaded aortic geometry. This new method involves updating the relaxation factor at each iteration to improve robustness by constraining it within a threshold and normalizing it. Compared to the traditional FPI method, this novel approach is better tailored to the aortic geometries examined in this study. The study compares stress results obtained from models with uniform and non-uniform aortic wall thickness, both with and without assuming unloaded conditions. Findings indicate that stress distribution of non-uniform geometry matches better to the measured damage extent. Stress distribution of unloaded geometry is similar to that of loaded geometry, while the use of unloaded geometry enhances the stress gradient. The stress analysis reveals variations across different directions and regions, with the second principal stress (SPS) magnitude that is more sensitive to the circumferential region than the first principal stress (FPS) and von Mises stress (VMS). There is an overlap area between the high SPS region and the most expanded region. The most dilated area usually matched with high SPS region for loaded and unloaded geometry or uniform and non-uniform geometry. Thus, although magnitude of SPS is smaller than that of FPS and of VMS, it is suggested to pay more attention to SPS in severity assessment of ATAA aneurysm.