Biomechanics and Modeling in Mechanobiology

The mechanical behavior of right ventricular (RV) myocardium is governed by its anisotropic microstructure, yet constitutive models that account for fiber dispersion and enable reliable parameter identification remain limited. In this study, we propose a physics-embedded constitutive neural network framework for automated discovery of strain energy functions and microstructural parameters from experimental data. The model is formulated within an incompressible, orthotropic hyperelastic setting using invariant-based representations. Fiber, sheet, and normal directions are incorporated through a rotated structural basis, and dispersion effects are modeled using a generalized structure tensor approach. The framework is trained on multi-axial mechanical data from ovine RV myocardium, including uniaxial tension-compression and simple shear tests. We investigate two training scenarios: (i) full datasets containing both tensile and compressive regimes and (ii) datasets restricted to tensile loading. In both cases, the model accurately reproduces the measured stress-strain responses and identifies sparse, interpretable constitutive models which involve isotropic, anisotropic, and coupling invariants. However, the identifiability of microstructural parameters strongly depends on the available loading conditions. While tensile-only data yield higher predictive accuracy, they result in non-unique or biased estimates of fiber dispersion. In contrast, inclusion of compressive data enables consistent identification of dispersion parameters by separating fiber and matrix contributions. These results highlight the importance of multi-axial loading data for robust parameter identification and demonstrate the capability of constitutive neural network-based approaches for data-driven modeling of anisotropic soft tissues.

The aorta shows significant regional variation in geometry and composition. This complexity makes numerical modeling challenging, as it requires identifying material parameters. Typically, the Holzapfel-Gasser-Ogden model is used. However, it suffers from nonuniqueness and sensitivity to outliers, which can obscure biological variation. In addition, standard compressible formulations with a volumetric-isochoric split fail to couple volumetric and anisotropic responses. To address these issues, a regularized dual-estimation framework was introduced. This framework combines a global baseline estimator with local refinement while maintaining structural material continuity. Furthermore, it uses a Modified Anisotropic model to improve the representation of compressibility physics. For validation, the approach included uniaxial extension and protein quantification from Wistar rats. The results show that the proximal ascending/aortic-arch segment is most compliant at low stretch, whereas the abdominal aorta stiffens earlier and becomes fiber-dominated at lower stretch levels. Notably, these trends align directionally with regional composition. However, the fitted stress components are model-based descriptors rather than direct measurements of individual constituents.

The control of uterine activity during pregnancy is a complex process that involves regulating myometrial excitability across multiple scales. While numerous studies have investigated various regulatory mechanisms and established the contributions of ion channels and gap junctions, how these mechanisms interact to produce observed changes in uterine activity remains poorly understood. Pivotal to these efforts are computational models that effectively capture gestational changes in excitability across scales. In this study, we propose a multiscale computational modeling framework that can reproduce measured activity at the cellular and tissue scales at a given gestational stage. At the cellular level, we identify key ion currents underlying the observed electrophysiological properties based on a literature review of their regulation and a sensitivity analysis of the Tong 2011 uterine smooth muscle cell activation model. The conductances of these ion currents are then fit to reproduce characteristic resting membrane potentials and burst properties using Bayesian optimization. To extend to the tissue level, we employ an anisotropic monodomain model, parameterized by the resistivity of late pregnancy uterine muscle, to investigate electrical propagation in a two-dimensional section of uterine tissue. We then apply the multiscale model to study myometrial activation in late pregnancy and elucidate the contributions of ion channel and gap junction regulation in transitioning the uterus from a quiescent state to labor. Our resulting model successfully reproduces measured electrophysiological properties at the cellular level and characteristic single-spike and burst-propagation patterns at the tissue level across the three late-pregnant time points analyzed (days 16/17, 18/19, and 20/21) in a murine model. Furthermore, our results suggest that the regulation of the conductances of the voltage-dependent potassium current (IK1), L-type calcium current (ICaL), and sodium current (INa) is most important in determining preterm uterine excitability. The framework established here will promote the development of more gestationally relevant models to better understand labor progression and the factors involved in dysfunctional labor.

Aortic dissection is life-threatening due to continued loss of medial integrity that may culminate in secondary rupture within hours to days. While pre-existing defects or hemodynamic loads compound structural deterioration of the aorta, pathological progression from symptomatic dissection channel to lethal transmural tear is poorly understood. We examined the structure of referent and acutely dissected ascending aortas by microscopy. Elastic, collagen, and cellular components of non-dissected media were intricately interconnected. Medial damage in dissection lesions was traced from ingress to central to peripheral areas. Entry tears broke cleanly through successive laminae leading to cavernous false lumens in which medial structure was destroyed. Nearby laminae with widening between flanking elastic lamellae (termed minor delaminations) were filled with blood and showed severe medial damage. Farther laminae without delamination but containing red blood cells (termed blood extravasation) displayed moderate medial damage. More distant, non-delaminated laminae with accumulation of albumin but not red blood cells (termed plasma extravasation) exhibited mild medial damage. Varying medial hemorrhage with scattered sloughing of laminae was observed along the entire false lumen. We conclude that hydraulic fracturing of residual dissected media by pressurized blood via communications from the false lumen contributes to further structural weakening of the aortic wall.

In the current clinical practice, the diagnosis of spinal disorders and their surgical planning are critically based on imaging data. To complement this data, patient-specific finite element models have been developed and showed to be powerful tools for evaluating spine mechanics. Most of them rely on Computational Tomography (CT) scans - which have a high resolution but are seldom available in routine clinical practice - while only a recent few models are on less invasive Magnetic Resonance Imaging (MRI). Yet, despite the proliferation of these computational models, encompassing detailed anatomical and functional information, the rheological assumptions they are built upon are based on tissue-sample mechanical response data, which leaves a gap in the quantitative analysis on how such assumptions influence the macroscopic response of a functional spinal unit. Aiming at addressing these shortcomings, the main purpose of this work is to introduce a quantitative computational assessment of the macroscopic impact of commonly adopted rheological models - from linear elasticity to fiber-reinforced nonlinear hyperelasticity - in several loading conditions, focusing on a lumbar unit which is considered as a typical benchmark system. We also propose a reconstruction procedure to accurately describe subject-specific anatomy from MRI data, including the intervertebral disc and its nucleus pulposus. Bones are modeled as linear elastic media, whereas for the AF, we consider three different mechanical models - namely, isotropic linear elasticity and the Holzapfel-Gasser-Ogden model with and without fiber reinforcement. Model verification on an idealized geometry demonstrates numerical consistency, while parametric orthostatic simulations highlight the need for nonlinear formulations to capture anisotropy and strain-stiffening behavior of the intervertebral disc. Then, we carry out flexion, lateral bending, and torsion tests on a subject-specific reconstructed functional unit, for which we provide parametric analysis in terms of momentum magnitude and resulting range of motion. These tests further confirm the need for a nonlinear rheology of the annulus fibrosus and provide a quantitative assessment of the differences between the constitutive laws considered. Moreover, successful comparisons with the literature, in terms of macroscopic deformation under several loading conditions, serve as partial validation for our computational model.

Dissection of the thoracic aorta includes delamination of medial lamellae and permeation of blood within the media. Quantifying how biaxial loading of a vulnerable wall and fluid mechanics interact to drive dissection remains a central challenge. Here we combine controlled distension-extension testing of intact porcine descending thoracic aortas with forced intramural fluid injection to investigate how axial stretch, injection rate, and needle gauge modulate the initiation and propagation of intramural delamination. Across experiments, injection pressure-volume curves exhibited nonlinear responses characterized by pressure peaks followed by stepwise pressure drops, suggesting progressive micro-delamination events within the medial lamellar networks. Increasing axial stretch significantly elevated peak injection pressure and promoted preferential axial propagation of the permeation / delamination front. Higher injection rates induced abrupt lamellar separation and larger dissected areas, whereas smaller needle gauges generated higher upstream pressures due to increased hydraulic resistance. Synchrotron imaging revealed the microstructural transition from intralamellar fluid permeation and wall swelling to the formation of a large fluid-filled delamination cavity. These results support a mechanistic framework in which the introduction of pressurized fluid within the aortic media behaves as a hydraulic fracture process in a layered poroelastic tissue, governed by balance across fluid pressurization, wall loading, and interlamellar strength. The findings provide quantitative insight into the biomechanical conditions that contribute to the initiation and propagation of aortic dissection.

Computational analysis of physiological and biomedical systems necessitate efficient geometry representations for high fidelity model predictions, including patient or device specificity. Particle-based Lagrangian computational approaches comprise a valuable approach to gain insights from quantitative velocity and pressure data from computational models. Examples include particle dynamics and transport in human vasculature for diseases such as stroke, thrombosis, and embolisms; and modern targeted drug delivery systems in the vascular network and respiratory airways. However, current particle simulation approaches can bear significant computational expense that scales with both number of particles and background fluid mesh resolution. A significant determinant of this computational expense is the contact resolution between particles and anatomically realistic vessel wall. Here, we develop an efficient particle dynamics model that leverages an implicit representation of real anatomical features using a signed distance field to efficiently resolve particle-wall contact. We outline the underlying algorithmic details, followed by a systematic illustration of performance and accuracy using simplified and analytically defined geometries and flow fields. Subsequently, we present a representative simulation of embolic particles along a human vascular segment where we compare our distance field-based approach against classical wall-contact checks based on assessing particle boundary intersection with triangulated surface mesh. Our approach transforms the underlying Lagrangian contact detection operation into an equivalent Eulerian operation, significantly speeding up bulk particle dynamics computations without significantly impacting accuracy or geometric fidelity.

The osteocyte network in bone is believed to play an important role for how bone tissues sense and respond to mechanical stimulation. Yet, bone adaptation to mechanical loads is often conceptualised as a simple response to mechanical stimuli, such as Wolffs law, which is based on mechanical variables only and takes no account of the cellular basis of mechanosensation. Wolffs law presumes the existence of a reference mechanical stimulus, the mechanical setpoint, above which bone is consolidated, and under which bone is removed. In this paper, we develop a theory of bone tissue sensing and adaptation based on osteocytes to provide new understanding of the role played by osteocyte signals in mechanical adaptation. In this theory, the mechanical setpoint of Frosts mechanostat is explicitly embodied as osteocyte properties involved in mechanotransduction. The mechanical setpoint is allowed to adapt due to the replacement of osteocytes during remodelling, making the setpoint space and time dependent. We propose a mathematical model to implement this new theory of bone adapation and present numerical simulations of this model to explore how mechanobiological response curves (effective Wolffs laws) are modulated by setpoint adaptation during remodelling. By accounting for varying osteocyte populations within bone tissue, we explore bone adaptation under osteocyte disruptions, which is particularly relevant to age-related bone loss. Our model suggests that biological disruptions of remodelling balance cannot always be compensated by mechanical feedback, and that setpoint adaptation during remodelling may have significant observable consequences, such as hysteresis in bone response signatures that resemble lazy zones.

PurposeComputational models of the uterus during pregnancy enable analysis of electro-chemo-mechanical pathways to predict labor timing and guide treatment planning. We aim to develop a robust image-based modeling pipeline to investigate uterine passive mechanics during late pregnancy. MethodsA parametric model of the uterus and cervix was created using a patients MRI measurements at 38 weeks of gestation. Inspired by advances in cardiac mechanics models, we created Laplace-Dirichlet solutions to inform tissue domains, fiber structure within the uterus and cervix, and spatially varying Robin boundary conditions. Prior imaging and mechanical testing data were used to fit material parameters. Boundary condition parameters were tuned to match the displacements of a previously established approach that employed contact with surrounding tissue. The tissue mechanical response to a physiologic load was assessed across varying material properties and fiber architectures. ResultsDiscrepancies in nodal displacements between the current approach and the contact-based model were limited to 3.4 {+/-} 1.8 mm, yielding nearly 90 % computational savings. Uterine tensile strains were more sensitive to ground substance elastic modulus (E) compared to fiber properties. Reduced E and fiber stiffness increased cervical strains and compression. Fiber dispersion and architecture modulated the opening of the cervical internal ostium but had a reduced impact on compression. ConclusionWe developed a novel workflow for modeling passive uterine mechanics, informed by patient-specific measurements and in vitro mechanical tests. The robust workflow may prove useful for studying labor progression and conducting longitudinal studies to enhance our understanding of normal and pathological pregnancies.

Thin structures such as heart valves and aortic dissection flaps interact dynamically with blood flow in human vessels. Their flexibility and capacity for large deformations generate complex, highly transient hemodynamic patterns over the cardiac cycle. Accurately resolving these interactions remains challenging for conventional boundary-fitted fluid-structure interaction approaches. We present an immersed boundary method for simulating thin structures in incompressible flow on unstructured grids. The method couples a stabilized finite element fluid solver with a nonlinear, rotation-free shell formulation through a direct forcing immersed boundary approach. The framework supports both weak (explicit) and strong (implicit) time-coupling strategies, enabling stable simulations over a wide range of solid-to-fluid density ratios. Hydrodynamic forces acting on thin structures are computed from fluid solutions sampled on both sides of the structure, allowing accurate force reconstruction for zero-thickness shells. To our knowledge, this is the first immersed boundary formulation that couples an unstructured finite element fluid solver with a two-dimensional, rotation-free shell model to simulate interactions between thin structures and incompressible flow. Fluid-structure coupling is achieved using predefined finite element shape functions, which provide consistent projection between Eulerian and Lagrangian fields without additional interpolation procedures. The framework is validated using three-dimensional benchmark problems involving thin structures. Then, valve-like model is used to compare strong and weak coupling strategies. Finally, the method is applied to an idealized type-B aortic dissection model. The proposed approach is implemented within the open-source software CRIMSON, a finite element platform for cardiovascular simulation.

Aortic stiffness is both heterogenous and anisotropic. Current non-invasive methods to estimate aortic stiffness are limited to characterizing the aortic tissue as isotropic due to the lack the techniques required to extract multi-axial strain from 3D dynamic images. Vascular deformation mapping (VDM) is a nonrigid image registration technique which has thus far been applied to map aortic growth using longitudinal imaging. In this study, we propose to use VDM to assess 3D aortic deformation by mapping diastolic and systolic images. During image registration process, penalty parameters are employed to fine-tune image alignment and penalize non-physiological deformations. These penalty parameters must be calibrated to ensure that VDM successfully reproduces multi-axial aortic motion patterns in health and disease. In this paper, we developed a calibration pipeline for these parameters using synthetic data. A rotation-free shell model was used to generate physics-based synthetic data on aortic motion incorporating patient-specific geometries, root motion, and blood pressure from a cohort of 14 subjects (healthy, Marfans syndrome and thoracic aortic aneurysm). An error metric was defined to quantify the quality of the VDM results. Furthermore, a k-means clustering technique was used to categorize the subjects into three clusters based on ascending aortic motion. Optimal penalty parameters were identified for each of the three clusters. The results indicated that patient clusters with smaller aortic root motion required larger rigidity penalty values. The calibrated parameters successively reduced errors in 3D displacement and multi-axial stretch compared to un-optimized VDM predictions, enhancing the accuracy of capturing aortic deformation from dynamic images. Among the different aortic regions, the ascending thoracic aorta exhibits the largest error reduction.

Experimental studies involving mechanical loading of the human head and brain in vivo are necessarily limited, making computational modeling essential for advancing our understanding of brain biomechanics. Demographic factors such as age and gender are known to influence brain anatomical structures, material properties, and potentially vulnerability to injurious loading. To address this, we construct six group-average brain models stratified by age and gender from a total of 135 subjects, and investigate the mechanical responses of these "group-average brains" using computational simulations. We use Pearson correlations to assess to what degree the group-average models represent individuals within each demographic category, showing strong correlations. Further, our p-value hypothesis test of the first principal strain across the six groups shows significant differences. This study demonstrates that age- and gender-stratified group-average models can effectively represent biomechanical responses of the individuals within the groups, and can reveal meaningful demographic differences that may influence susceptibility to traumatic brain injury. We show that the age-dependent change in material properties plays a greater role than anatomical changes in driving differences in the deformations. We hope to see increased utilization of these group-average models in both research and clinical applications.

Cell shape and mechanics are intricately connected and tightly regulated by mechanochemical events including biochemical signaling, cytoskeletal remodeling, and plasma membrane mechanics. While experimental advances in microscopy have shed light on the intricate coordination involved in cell shape change in response to different cues, the ability to conduct three-dimensional simulations in realistic geometries remains an open computational challenge. In this work, we develop a finite-element framework that incorporates advection-diffusion-reaction equations coupled with equations governing the kinematics of a deformable interface representing the cell membrane. We applied this framework to three distinct coupled mechanochemical systems, each governed by geometric partial differential equations, resulting in large deformations of the interface. In all three examples, our simulations revealed the emergence of feedback between cellular signaling, cytoskeletal organization, and cell shape. In our first two sets of simulations, we observed that cell migration and neutrophil protrusion were regulated by membrane tension-mediated feedback. In our final application, we predicted shape changes of a dendritic spine starting from a realistic geometry, and found that the complex shape of the spine gives rise to localized regimes of actin cytoskeleton remodeling not previously observed with idealized geometries. Thus, our finite-element framework allows us to generate new mechanistic insights for biophysical problems.

Hearts change their wall thickness (concentric growth) and chamber size (eccentric growth) as they adapt to circulatory demands and the intrinsic function of their contractile cells. Factors associated with wall thickening include variants of sarcomeric proteins that enhance contractility, mitochondrial dysfunction, and hypertension. Chambers can dilate due to many factors including sarcomeric variants that depress contractility and aortic and / or mitral valve insufficiency. Despite intensive study, the mechanisms that regulate cardiac growth remain unclear. It is also uncertain whether inherited variants induce growth via the same mechanisms as more common clinical pathologies, such as hypertension. Here we show that computer simulations of a beating left ventricle reproduce both variant and non-variant-related growth patterns when myocytes grow concentrically to regulate intracellular ATP concentration and eccentrically to maintain titin-based intracellular stress. The simulations support the hypothesis that cardiac growth reflects homeostatic feedback through three interacting systems whereby myocytes add or remove mitochondria and sarcomeres (1) in parallel to match ATP generation to myocardial energy demand, and (2) in series to regulate passive tension, while (3) the autonomic nervous system regulates cardiac power, and thus myocardial ATPase, via baroreflex control. The new framework provides a mechanistic basis for the patterns of eccentric and concentric growth induced by a wide range of clinically-relevant conditions and could facilitate in silico testing of potential therapies for cardiac disease. Significance statementHearts grow in response to both physiological and pathological stimuli. The patterns of concentric (wall thickening / thinning) and eccentric (chamber dilation / constriction) induced by different challenges are well recognized but the underlying mechanisms remain unclear. This work presents simulations of a beating left ventricle where (1) concentric growth is regulated by myocytes attempting to stabilize the intracellular ATP concentration and (2) eccentric growth is regulated by titin-mediated stress. The calculations reproduce the growth associated with inherited variants of sarcomeric proteins, mitochondrial dysfunction, hypertension, and both mitral and aortic valve insufficiency. The new ability to predict cardiac growth and its potential modification by treatments, including myotropes, brings the field closer to in silico optimization of therapy for cardiovascular disease.

Identification and treatment of pregnancies at risk for preterm birth is a central challenge in obstetric research. Many of the known causes of preterm birth originate from mechanical failure in reproductive tissues. To better understand the biomechanical environment of the gravid uterus and its potential contribution to preterm birth, this computational study presents a parametric method for modeling maternal reproductive anatomy during the early second trimester. A finite element modeling approach was built using existing sonographic measurements from early second-trimester maternal anatomy and material properties from published mechanical tests. We applied the same physiologically relevant intrauterine pressure to all models and quantified the resulting tissue stretch. The sensitivity of the stretch in the proximal cervix was explored by varying material properties and sonographic maternal anatomy dimensions. Cervical material properties, particularly the fiber stiffness modulus and ground substance Youngs modulus, were found to have the greatest effect on proximal cervix stretch compared to other material properties and sonographic dimensions. Among the sonographic dimension measurements, those defining the region surrounding the proximal cervix had the greatest effect on proximal cervix stretch, including the curvature of the posterior uterine wall and the thickness of the lower uterine segment. The computational modeling approach presented here enables future patient-specific studies of gravid reproductive tissues to elucidate differences between individuals who do and do not deliver preterm. Additionally, this study is foundational for building digital twins to support future virtual clinical studies on diagnostic and therapeutic device design to prevent preterm birth.

Cell division within mechanically confining extracellular matrices (ECMs) is a key regulator of tissue morphogenesis and cancer progression. Although the intracellular force-generation mechanisms that drive volumetric growth and mitotic elongation are well characterized, how ECMs resist these forces remains poorly understood. Unlike linearly elastic materials, fibrillar ECMs exhibit nonlinear and viscoelastic behaviors that fundamentally alter how they oppose cell-generated stresses. Using a fiber-level computational model, we dissected the origins of ECM-mediated mechanical confinement during mitosis. We identified three distinct modes of resistance: compressive resistance at the cell poles, shear resistance from a pericellular shell, and tensile resistance at the cell equator. The relative contributions of these modes depended on fiber architecture and connectivity; however, shear resistance from the pericellular shell--pre-tensed by volumetric growth during G1--consistently dominated as the primary mechanical barrier to mitotic elongation. These findings suggest that the pericellular shell functions as a natural mechanical checkpoint on cell division within collagen-rich microenvironments. Notably, a finite element continuum model, despite being the most widely used framework for tissue mechanics, failed to reproduce these behaviors, underscoring the necessity of fiber-resolution approaches. We propose that overcoming this mechanical checkpoint is a critical step in cancer progression, enabling cells to divide within the dense stromal matrices characteristic of metastatic tumors.

PurposeFunctional tricuspid regurgitation (FTR) is driven by right ventricular (RV) remodeling, annular dilation, and papillary muscle dislocation. Free wall approximation (FWA) has been proposed to treat FTR by addressing RV dilation, but its effects on tricuspid valve (TV) biomechanics remain unclear. We present a real-time 3D echocardiographic (rt3DE)-based finite element framework to quantify TV biomechanics under FTR, and preliminarily apply it to assess FWA effects. MethodsSubject-specific models were developed from rt3DE data of three dilated porcine hearts in an ex-vivo mock-loop. TV geometries at end-diastole and peak systole (PS) were complemented by parametric chordae tendineae and hyperelastic tissue properties. TV closure was simulated under a standard pressure load and image-based annular motion. After tuning chordae length to replicate the PS ground truth in FTR, FWA was simulated as 30% and 60% approximations along three anatomical directions (anterior-posterior, A-P; anterior-septal, A-S; anterior-septal wall, A-SW). ResultsIn FTR simulations, median geometric errors ranged from 1.16 to 1.26 mm; median stress ranged from 56.4 to 74.7 kPa. FWA simulations predicted regurgitant orifice area (ROA) reductions by 53-99%, albeit overestimating the residual ROA vs. in vitro ground truth when starting from particularly extreme FTR conditions; concomitantly, a median stress reduction by 8-43% vs. FTR conditions was predicted. ConclusionPreliminary data suggest that our rt3DE-based framework can reliably quantify FTR-related TV biomechanics and that post-FWA biomechanics depends on initial FTR conditions. A larger cohort is required to verify the method and obtain statistically significant results.

Computational Hill-type muscle models are widely used to simulate muscle force production because of their efficiency and physiological interpretability. However, their formulation relies on limiting assumptions, including debated multiscale simplifications, a simplified excitation-activation dynamics and an inability to capture slow and fast fibres. Moreover, existing Hill-type models remain insufficiently validated across physiological scales, fibre types, and contraction modes. We addressed these limitations by developing a multiscale fibre-type specific Hill-type neuromuscular actuator with mechanistic excitation-activation dynamics and systematically validated it against comprehensive experimental benchmarks. The model built upon a previously proposed motoneuron-driven actuator incorporating calcium-kinetics-based activation dynamics. The excitation-activation formulation was further refined to strengthen its physiological basis, while the contraction dynamics was extended by including an activation- and length-dependent force-velocity relationship, elastic tendon, passive elastic element, and the fibre-type-specific effects of yielding and sag. Validation was performed against four benchmark datasets spanning motor-unit and whole-muscle scales, including slow and fast fibres under both isometric and dynamic conditions. Experimental force traces were obtained from six muscles of rats and cats using a broad range of stimulation frequencies, muscle lengths, and imposed length changes, combining previous literature datasets with experiments performed ad hoc for this study. Overall, the model reproduced forces across all benchmark conditions, with mean absolute errors typically below 15% of the maximum isometric force, although larger errors were observed in specific submaximal and dynamic trials. The inclusion of physiologically based excitation-activation dynamics, together with yielding and sag, improved model performance under submaximal activation conditions. This study presents the first systematic validation of a single multiscale Hill-type neuromuscular actuator against comprehensive experimental motor unit and muscle force data, providing a benchmark framework for the development and assessment of future models. Author summarySkeletal muscles generate force through a complex sequence of events that links neural signals to muscle contraction. Because direct measurements are difficult to obtain, researchers often rely on computer models to investigate neuromuscular function and estimate muscle forces. However, most modelling approaches rely on simplifying assumptions about how force is generated across different biological scales, how muscles are activated, and how slow and fast muscle fibres behave. Moreover, they have not been validated against comprehensive experimental data. As a result, it remains unclear how accurately these models can reproduce muscle force across different physiological conditions. In this study, we established the first comprehensive set of experimental benchmarks spanning both motor-unit and whole-muscle scales, including slow and fast muscles under isometric and dynamic conditions. We used these benchmarks to validate a newly developed multiscale muscle model that explicitly represents the physiological pathway from neural stimulation to force production. The model incorporates experimentally based descriptions of calcium dynamics, activation, tendon elasticity, and fibre-type-specific contractile properties. We then compared simulated and experimental force responses across a wide range of stimulation frequencies, muscle lengths, and length-change conditions.

The aorta, normally resilient to hemodynamic stresses, becomes vulnerable to structural failure due to diverse conditions that weaken the wall. We injected fluid into excised specimens of human ascending aorta with pressure monitoring to quantify the impact of clinical and histological factors on mural damage. Two modes of medial injury emerged with distinct pressure tracings. Extravasation was characterized by diffuse infiltration of fluid with widespread damage of smooth muscle cells and collagen fibers but limited separation of elastic lamellae. By contrast, delamination was characterized by marked separation of elastic lamellae along a single plane with damage to cells and fibrillar matrix restricted to adjacent laminae. Aging, aortic dilatation, and family history associated with lower pressures causing delamination, whereas a diagnosis of hypertension associated with higher pressures suggesting resilience to dissection. Collagen fraction adjacent to delamination correlated with higher pressures as did decreased smooth muscle cell density and increased glycosaminoglycan fraction, although several clinical and histological variables were interrelated. Protein cross-linking strengthened and enzymatic digestion of collagen weakened the aortic wall, while acute cell lysis with detergent had no effect. We conclude that increased functional medial collagen has an adaptive protective role in aortic remodeling rather than signifying medial degeneration.

The corpus callosum is the largest white matter structure connecting the two cerebral hemispheres and is anatomically divided into three major subregions along the anteroposterior axis: the genu, midbody, and splenium. The splenium is frequently affected in traumatic head impacts, yet the biomechanical basis for this selective vulnerability remains poorly understood. Clinical studies have long hypothesized that the falx cerebri contributes to the splenial susceptibility because of its close anatomical relationship with the posterior corpus callosum, although direct verification is lacking. To address this, a high-resolution finite element head model with explicit representations of the genu, midbody, and splenium was employed. Two model variants, differing only in the presence or absence of an anatomically and mechanically detailed falx, were used to simulate ten head impacts covering a range of loading directions and severities. Peak strain, strain rate, and shear stress were quantified in each corpus callosum subregion and compared using linear mixed-effects models. The results showed that inclusion of the falx altered the regional distribution of mechanical responses within the corpus callosum. Across the simulated impacts, the splenium consistently exhibited greater strain, strain rate, and shear stress than the genu and midbody when the falx was present. In contrast, these preferentially larger splenial deformation were not consistently observed when the falx was absent. Statistical analyses demonstrated significant region-dependent effects of the falx, with falx-induced increases in strain, strain rate, and shear stress being significantly greater in the splenium than in the genu and midbody (p < 0.05). These findings verified the hypothesis that the falx selectively amplified mechanical loading within the splenium, thereby contributing to its heightened vulnerability to injury. This work provides a plausible biomechanical explanation for the frequent involvement of the splenium in brain trauma patients and highlights the heterogeneous influence of the falx on mechanical responses across corpus callosum subregions.