Biomechanics and Modeling in Mechanobiology

BackgroundAortic microstructure-function relationships and the pathophysiology of how medial degeneration leads to aortic dissection remain poorly defined. We aimed to determine how degeneration of individual components of the extracellular matrix (ECM), namely elastin, collagen, and proteoglycans, influence biomechanical properties of aortic tissue through an improved, disease-motivated enzymatic digestion framework. MethodsPorcine aortic tissue was sectioned into 200 {micro}m thick samples in the media, and progressively digested with elastase or collagenase for selective degradation of these ECM components. Full thickness human aortic tissues were treated with chondroitinase, hyaluronidase, and heparinase to completely remove proteoglycans. Biomechanical characterization was performed using planar biaxial tensile testing, from which low- and high-strain modulus, transition-zone behaviour, strain-energy density, and energy loss were derived. Degree of elastin fiber degradation was analyzed using two photon excitation fluorescence imaging. Analysis of collagen degradation was performed using picrosirius red staining under brightfield and polarized light. Alcian blue staining was used to evaluate proteoglycan content. ResultsInduced fragmentation and disorganization of elastin fibers reduced low-strain load bearing capacity, evidenced by reduced low-strain modulus, strain-energy density, and transition zone stress, along with reduced energy loss. Targeted collagen disorganization similarly reduced strain-energy density and decreased strain at the onset of transition, consistent with premature collagen recruitment, and was accompanied by reductions in high strain modulus and energy loss with increasing collagen degradation. Proteoglycan removal decreased energy loss and was found to modulate low- and high-strain behaviour, including reduced strain-energy density and strain at onset of transition, and increased high strain modulus. ConclusionsThrough targeted modelling of ECM degenerative features on aortic tissue mechanics, we have identified distinct disease-associated biomechanical roles for major matrix constituents, with overlapping effects. These findings delineate mechanical consequences of component-specific matrix degeneration while underscoring the complex, multifactorial nature of structure-function relationships in aortic disease.

Current clinical risk stratification for thoracic aortic aneurysms (TAA) relies primarily on maximum diameter, which is a poor predictor of rupture. Recent fluid-structure interaction studies have identified a dimensionless "flutter instability parameter" (N{omega} ) that accurately classifies abnormal aortic growth. However, this parameter currently serves as a static diagnostic snapshot. In this work, we propose a proof-of-concept computational framework that links flutter instability to microstructural tissue damage via a coupled system of ordinary differential equations (ODEs). We model a feedback loop where flutter-induced energy dissipation drives elastin degradation and collagen remodeling, which in turn reduces wall stiffness and amplifies the instability. To address the challenge of unobservable tissue properties, we implement a Bayesian inference engine to infer model parameters. We demonstrate feasibility on a synthetic patient cohort calibrated to published clinical growth rates and diameters. Our results show that this approach can infer hidden damage parameters and capture the qualitative bifurcation between stabilizing remodeling and runaway aneurysm expansion. While validation on real patient data remains essential, this work establishes the mathematical foundation for transforming a static physiomarker into a personalized prognostic trajectory.

Cartilage deterioration is a hallmark of the most common joint disease, osteoarthritis, and there is substantial interest in developing strategies for cartilage synthesis and repair. Cyclical mechanical stimulation has been known for decades to drive synthesis of cartilage matrix proteins. Matrix synthesis requires activation of central metabolism for producing precursors to non-essential amino acids required for protein translation. However, there are gaps in knowledge regarding how mechanical stimuli affect chondrocyte central metabolism. Here, we find that cyclical shear and compression drive differences in chondrocyte central metabolism in a sex-dependent manner. Based on established biochemistry, we developed and validated a stoichiometric model containing 139 metabolites and 172 reactions from central metabolism that includes production of the key cartilage matrix proteins of types II and VI collagen and aggrecan. We then used experimental data from shear and compressive stimulation of osteoarthritis chondrocytes to constrain this model and ran multiple simulations examining flux through the production reactions of matrix proteins and ATP. Our results show that both shear and compression can stimulate osteoarthritic chondrocyte metabolism in a manner consistent with production of cartilage matrix proteins, with notable differences in simulated central metabolism between male and female chondrocytes. Additionally, and importantly, our simulation results suggest that nitrogen availability is a key limitation to chondrocyte synthesis of matrix proteins. These results are a starting point for using central metabolism of chondrocytes to optimize synthesis of matrix proteins for cartilage repair. For example, increasing glutamine levels in the presence of cyclical compression has potential to increase production of both types II and VI collagen. These strategies have potential for improving cartilage tissue engineering and repair.

PurposeTo evaluate the hemodynamic impact of restoring a normal sino-tubular junction (STJ) following a novel Hegar dilator-based procedure in patients undergoing root-sparing ascending thoracic aortic aneurysm (ATAA) repair using computational modeling. MethodsWe retrospectively selected an ATAA patient who underwent pre- and postoperative gated computed tomography angiography (CTA). We developed a novel workflow to segment the lumen, thick-walled aorta, and aortic valve from CTA images for subsequent blood flow analysis using computational fluid dynamics (CFD) and fluid-structure interaction (FSI). Morphological and hemodynamic characteristics of the root were quantified and compared against those of a control subject, with no noted ascending aortic dilation. The models sensitivity to graft properties and leaflet material heterogeneity was analyzed. ResultsBoth CFD and FSI results showed that the postoperative geometry reconstructed with a normal STJ profile reintroduces sinus vortices during peak systole, similar to the control subject, but were absent pre-surgery. Accounting for aortic valve leaflets in FSI studies yielded qualitatively similar results to the CFD cases, albeit with locally elevated velocities, time-averaged wall shear stress (TAWSS), and energy dissipation, likely due to the dynamically changing orifice area and differing profiles of the left ventricular outflow tract (LVOT). ConclusionWe demonstrated that the novel Hegar dilator-based STJ reconstruction restores normal blood flow patterns, highlighting the importance of reprofiling the aortic sinuses and STJ. The study also highlights the models sensitivities, particularly the LVOT shape and leaflet morphology and mobility, and may assist planning STJ reconstruction to yield optimal hemodynamics before intervention.

Clavicle fractures often exhibit markedly different clinical outcomes: some patients recover acceptable function despite shortening or displacement, whereas others with apparently similar deformity develop persistent pain, functional loss, or poor healing. To explain this distinction, we propose a minimal nonlinear mechanical model for prognostic analysis of clavicle fractures. The model describes the interaction between fracture-related shortening and compensatory shoulder-girdle posture through a reduced equilibrium equation incorporating stiffness, geometric nonlinearity, and shortening-posture coupling. Within this framework, we analyze equilibrium branches, local stability, and the emergence of critical thresholds. We show that post-fracture destabilization can be interpreted as a fold bifurcation, while more complex parameter dependence gives rise to cusp-type structures and multistability. These bifurcation mechanisms provide a mathematical explanation for sudden deterioration after injury or treatment, as well as for strong inter-individual variability. We further introduce an optimization principle based on a utility functional to guide treatment planning. The analysis predicts that the optimal safe correction should lie strictly below the bifurcation threshold, thereby generating a natural safety margin. Although the model is simplified and has not yet been calibrated against patient data, it nevertheless provides a theoretical framework for understanding why fracture prognosis may deteriorate abruptly near critical mechanical conditions and offers a dynamical-systems interpretation of empirical treatment thresholds used in clinical practice.

Deep vein thrombosis (DVT) is a prevalent vascular condition in which venous anatomy and flow disturbances contribute to the risk of thrombosis, but the mechanistic links between vessel shape and haemodynamics remain poorly quantified. Although computational fluid dynamics (CFD) can estimate flow-related risk metrics such as low wall shear stress (WSS), the influence of anatomical fidelity on these predictions is not well understood. Statistical shape modelling (SSM) offers a principled framework for characterising geometric variability, but its integration with CFD in venous applications is still emerging. This study investigates how different levels of anatomical representation--2D projections, simplified 3D extrusions, and full 3D reconstructions of the common iliac veins--influence both the statistical structure of venous shape variability and the haemodynamic metrics derived from CFD. Using patient-specific MRI/CT data from twelve cases, we constructed SSMs in Deformetrica and performed steady-state CFD simulations in ANSYS Fluent under standardised inflow conditions. We compared the variance structure of the 2D and 3D latent spaces and quantified correlations between principal shape modes and low-WSS burden across three thresholds ([≤] 0.05, 0.10, 0.15 [Pa]). Idealised 3D geometries consistently produced larger low-WSS areas than patient-specific shapes, with average increases of 118-136% across thresholds. The 2D SSM exhibited a strongly hierarchical variance spectrum with one dominant mode that correlated significantly with WSS, whereas the 3D SSM showed a flatter spectrum with weaker univariate associations. These findings demonstrate that geometric fidelity and alignment strategy critically influence shape-flow relationships, highlighting the need for careful model selection when using CFD-based haemodynamic indicators in DVT research. Author summaryDeep vein thrombosis (DVT) is a common condition in which blood clots form in the deep veins of the leg and can lead to serious long-term complications. Although medical imaging captures important anatomical differences between patients, it remains unclear how these variations in vein shape influence local blood flow and the associated risk of clot formation. To address this challenge, we developed a computational framework that combines statistical shape modelling (SSM) with computational fluid dynamics (CFD) to analyse the relationship between venous geometry and haemodynamic risk factors. We examined the common iliac veins at three levels of anatomical detail: simplified two-dimensional projections, intermediate three-dimensional extrusions, and full three-dimensional reconstructions derived from MRI/CT data. By comparing these representations, we show that geometric fidelity strongly affects both the detected modes of anatomical variation and the resulting flow predictions. Simplified geometries consistently overestimated regions of low wall shear stress, a flow feature associated with thrombosis, compared to full 3D models. We also found that shape-flow associations depend heavily on how shapes are aligned and represented. Our findings highlight the importance of anatomical detail in computational venous modelling and provide a foundation for more personalised, simulation-based tools to support DVT treatment.

Computational growth and remodeling (G&R) models have been extentively used to investigate abdominal aortic aneurysm (AAA) progression and to support clinical decision-making. However, the development of robust predictive models is often limited by the scarcity of large-scale longitudinal imaging datasets. In this study, we propose a physics-based G&R framework to simulate AAA shape evolution and generate a virtual cohort of aneurysms, thereby addressing data limitations and enabling integration with data-driven machine learning approaches for growth prediction. The proposed arterial G&R model incorporates key mechanisms influencing aneurysm progression, including elastin degradation and stress-mediated collagen production. A modified elastin degradation formulation was introduced to generate realistic aneurysm geometries exhibiting clinically relevant features such as asymmetry and tortuosity. By systematically varying parameters governing elastin damage and collagen production, 200 distinct G&R simulations were performed to produce a diverse set of AAA geometries. The dataset was further expanded using kriging-based spatial interpolation to construct a large in silico cohort. The synthetic dataset, combined with longitudinal imaging data from 25 patients, was used to train and validate four machine learning models: Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). A two-step training strategy was adopted to predict maximum aneurysm diameter and growth rate based on prior geometric characteristics. The LSTM model achieved the highest performance for maximum diameter prediction (R{superscript 2} = 0.92), while the RNN demonstrated strong overall performance (R{superscript 2} = 0.90 for maximum diameter and 0.89 for growth rate). The DBN and GRU models also showed competitive predictive capability. Overall, this study demonstrates that integrating physics-based G&R simulations with machine learning enables accurate prediction of AAA growth and maximum diameter. The proposed framework provides a scalable strategy for augmenting limited clinical datasets and offers a promising tool to support personalized risk assessment and treatment planning.

Uncontrolled hemorrhage remains a leading cause of traumatic death, driven by rapid physiological deterioration that is often difficult to detect during the compensated phase. While large-animal models provide critical insights into these dynamics, they are resource-intensive, motivating the need for efficient computational frameworks that can mechanistically interpret cardiovascular responses. We developed and calibrated a closed-loop zero-dimensional (0D) lumped-parameter model (LPM) using hemodynamic data from 43 anesthetized swine subjected to controlled hemorrhage (10%, 20%, or 30% of total blood volume). The computational framework, incorporates a dynamic heart model with a custom time-varying elastance function, a multi-compartment aorta, and distal Windkessel models representing vascular beds. The model was calibrated at discrete time snapshots throughout the 30-minute hemorrhage protocol to reproduce group-averaged experimental waveforms for aortic flow, regional organ flows, and systemic pressures. The calibrated model successfully reproduced experimental hemodynamic targets and waveform morphology across all hemorrhage severities. Analysis of the calibrated parameters revealed distinct physiological mechanisms driving hemodynamic adaptation during hemorrhage: a preferential increase in renal resistance compared to carotid resistance, indicating flow redistribution to vital organs, and a progressive mobilization of venous unstressed volume to sustain cardiac filling. Furthermore, the model captured the distinct shift toward preload limitation state for 30% hemorrhage group. This study establishes a physiologically interpretable in-silico framework capable of predicting both global and regional hemodynamic responses to acute blood loss, providing a validated foundation for future applications in trauma care and resuscitation modeling.

The placenta is a fundamental organ for human reproduction, facilitating fetal growth via the exchange of oxygen, nutrients and waste products between mother and fetus, by means of a dense network of fetal villi bathed in maternal blood that flows through the intervillous space (IVS). However, despite its role in adverse pregnancy outcomes associated with impaired maternal-fetal transfer, the influence of placental structure on maternal haemodynamics and the delivery of oxygen and nutrients to the developing fetus is not well understood. This study employs computational fluid dynamics within physiologically informed 2D and 3D whole-organ-scale placental geometries, whose features are informed by recent ex vivo experimental and {micro}CT data, to examine comprehensively the influence of placental anatomy on maternal flow and transport in the IVS. In particular, we consider in detail the impact of the number and placement of maternal decidual arteries and veins that supply and drain the IVS, the location and height of so-called septal walls that loosely separate the placenta into functional units (cotyledons) and sub-units (lobules), and the density of the fetal villous trees as reflected in the rate of uptake of dissolved solutes from the maternal blood and the resistance to flow. We first exploit the computational efficiency of simulation in a representative 2D geometry to study in detail the sensitivity of haemodynamic markers to these parameters. These results guide our 3D study which reveals that the flow, transport and oxygen uptake are strongly influenced by placental structure, and exposes the vein-to-artery ratio as a key indicator of placental efficiency, regulating a trade-off between a preferential maternal flow environment and fetal oxygen uptake. Conversely, the location and height of the septal walls, a feature that is not well studied, have minimal systematic impact on the macroscopic haemodynamic and transport measures considered here. We also introduce a reduced model, for which analytical progress can be made, and demonstrate its utility in exposing key drivers of maternal-fetal transport. Author summaryIn our study, we explored how placental structure affects maternal blood flow and the delivery of oxygen to the fetus. The placenta is a complex and vital organ, but we do not fully understand how its morphology influences its functional efficiency. This is a critical gap in our knowledge as placental blood flow is implicated in serious pregnancy complications such as fetal growth restriction and pre-eclampsia. To investigate the effect of structure, we used detailed 2D and 3D models of the placenta. These models allowed us to simulate maternal blood flow and oxygen transport therein while changing various structural features, such as the number and location of placental veins and the density of the intervillous space. We found that the ratio of veins to arteries is a key factor in determining the maternal blood flow speed and the amount of oxygen the fetus receives. A higher number of veins relative to arteries helps create the slow-flow environment necessary for a healthy pregnancy, but an excessive ratio can reduce the efficiency of oxygen uptake. Our findings suggest that the vein-to-artery ratio could be an important indicator of placental health, offering a new perspective for understanding and enabling effective early intervention treatments of pregnancy complications.

Thoracic Endovascular Aortic Repair (TEVAR) is a minimally invasive procedure for the treatment of thoracic aortic pathologies, such as Thoracic Aortic Aneurysm (TAA). Computational simulations can provide valuable insights into TEVAR outcomes and complications prior to surgery, making them a useful tool in the procedural planning. In this work, Fluid-Structure Interaction (FSI) computational simulations are carried out in ten pre-TEVAR patient-specific TAA cases, for which post-TEVAR outcomes are known, to quantify the hemodynamic drag forces acting on the aortic wall. Based on these results, this study proposes a new risk factor R to predict the occurrence of type I and III endoleaks. The patient cohort is divided in a calibration set, used to associate specific R values with three different risk levels, and a validation set, to test the risk factor efficacy. Based on the risk factor values obtained for the calibration set, R[&le;] 0.33 is associated with low risk of endoleak formation, 0.33 < R[&le;] 0.67 with moderate risk, and R > 0.67 with high risk. Once it is applied to the validation set,the risk factor is able to predict the formation of a type Ia endoleak. The risk factor proposed in this work is capable of identifying all the endoleak cases analysed, as well as conditions known to increase the risk of TEVAR complications. This study represents a preliminary attempt to determine whether pre-TEVAR hemodynamics can effectively predict post-TEVAR complications and thereby aid clinicians in the pre-operative planning.

The present study presents a systematic approach for generating data-driven synthetic cerebral aneurysm geometries and evaluating their hemodynamics through computational fluid dynamics. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component analysis (PCA) was then applied to the aligned point-cloud data to quantify morphological variability and parameterize shape deformation. The first four principal components captured over 90% of the total variance; however, higher-order components were required to capture the detailed geometrical features of the original geometries. Computational fluid dynamic simulations were performed on the PCA-based synthetic geometries under pulsatile flow conditions to investigate the influence of shape variations on intra-aneurysmal flow patterns, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). The first principal component score (PCS1), which was associated with changes in aneurysm height and dome width, had the strongest effects on TAWSS and OSI levels. Lower PCS1 values, which corresponded to taller and more oblique domes, produced slower adjacent flow and elevated OSI, whereas higher PCS1 values increased TAWSS. The second principal component score primarily modulated lateral geometric asymmetry and further influenced OSI distribution for the lower PCS1 values. Collectively, these findings indicate that PCA-based shape parameterization provides a practical approach for generating synthetic aneurysm datasets and systematically assessing how specific morphological features govern hemodynamic behavior. The proposed approach is expected to contribute to the future development of surrogate modeling and data-driven hemodynamic prediction.

Cerebral blood flow (CBF) control is essential for normal brain function and is disrupted in pathological conditions. Arterial diameters are tightly regulated to provide on demand increases in blood flow in regions of neuronal activity. Pericytes (PCs) exhibit robust myogenic tone and may also respond to neuronal activity to fine-tune local resistance and blood flow. Thus, mural control of microcirculatory resistance may extend beyond arteries and arterioles. Yet, PCs electrophysiology and contractility have not been thoroughly characterized, and this prohibits an integrated view of brain blood flow control. In this study, we develop a detailed mathematical model of mural cell electrophysiology, Ca2+ dynamics and biomechanics. The model is informed by electrophysiological data in smooth muscle cells (SMCs) or PCs and predictions are compared against pressure-induced responses in isolated arterioles and capillaries, respectively. Simulations recapitulate myogenic constrictions and examine differences in contractile dynamics as we move from arterioles to proximal and distal capillaries. In arteriole-to-capillary transitional (ACT) zone PCs, increased mechanosensitivity, more Ca2+ influx through non-selective cation (NSC) channels and/or a higher sensitivity of the contractile apparatus to Ca2+ can compensate for reduced L-type voltage-operated (VOCC) Ca2+ influx and allow for robust constrictions at the lower operating pressures of capillaries relative to the arterioles. A significant Ca2+ influx through NSC relative to VOCC, however, can decouple the PCs contractile apparatus from electrical signaling. Vasoactivity to chemomechanical stimuli along the arteriole to capillary axis is progressively driven by VOCC-independent Ca2+ influx and Ca2+ sensitization with slow kinetics. The proposed cell model can form the basis for detailed multiscale and multicellular models that will examine physiological function at a single vessel or vascular network levels and investigate CBF control in health and in disease. Key pointsO_LIA mural cell model of electrophysiology, calcium (Ca2+) dynamics and biomechanics is informed by data and adapted for modeling cerebral arteriole smooth muscle cells and capillary pericytes. C_LIO_LIIon channel activities are characterized by patch-clamp electrophysiology in isolated cerebral smooth muscle cell and pericytes, and capillary and arteriole electromechanical responses to transmural pressure changes are assessed using novel ex vivo preparations. C_LIO_LIMyogenic constrictions in arterioles can be reproduced by pressure-induced non-selective cation channel (NSC) activation that depolarizes the cell, opens L-type Ca2+ channels (VOCCs) and increases Ca2+ influx. C_LIO_LIRobust myogenic constrictions in arteriole-to-capillary transition (ACT) zone pericytes may reflect significant Ca2+ influx through NSC, increased mechanosensitivity, or higher sensitivity of the contractile apparatus to Ca2+, potentially compensating for reduced VOCC density relative to arteriolar smooth muscle. C_LIO_LIA significant contribution of NSC relative to VOCC in Ca2+ influx, can decouple the contractile apparatus from electrical signaling. C_LIO_LIThe model shows how gradients in ionic activities, mechanosensitivity and/or Ca2+ sensitivity can alter contractile phenotype and electromechanical coupling along the arteriole to capillary continuum. C_LIO_LIThe proposed model can form the basis for detailed multiscale and multicellular models that will investigate cerebral blood flow control in health and in disease. C_LI

We present an automated framework to generate 1 demographically stratified virtual populations of abdominal aortic aneurysms (AAAs) and to quantify anatomy-flow relationships via an in silico observational study. Using 258 CTA-derived cases, we generated 182 validated AAA geometries and ran 364 simulations, extracting 11 geometric descriptors and six haemodynamic biomarkers. The automated constraint-aware framework blends statistically grounded sampling, anatomical plausibility and regional morphing to provide a scalable route for reproducible CFD to uncover geometry-biomarker relations at cohort scale. The proximal neck diameter was the strongest determinant of shear, increasing mean WSS (r {approx} 0.77) and peak WSS0.95(r {approx} 0.58) while reducing low-TAWSS area (r {approx} -0.36). Maximum diameter minimally affected peak shear (r {approx} -0.03) but led to moderate increase of low-TAWSS regions (r {approx} +0.20). Compactness indices suppressed oscillatory shear; sphericity and convexity, largely under-explored AAA shape descriptors, showed strong inverse correlation with OSI (r {approx} -0.68, -0.65) and mean WSS (r {approx} -0.47, -0.59). The framework reveals neck calibre and shape compactness, not maximum diameter alone, as dominant modulators of AAA haemodynamics. Subject Areasfluid mechanics, biomechanics, biomedical engineering

1Nuclear envelope stretch and rupture are common to cell spreading and migration in a variety of microenvironments, leading to marked changes in nucleocytoplasmic transport. Predicting cell response to different mechanochemical cues that are transmitted to the nucleus remains an open problem in the field of mechanomedicine. We developed a predictive modeling framework to examine how nuclear deformation on substrates with different nanotopographies influences nucleocytoplasmic transport and rearrangement of the nuclear lamina. Using the finite element method, we simulated nuclear compression by the perinuclear actin cap on substrates with arrays of nanopillars, modeling the nuclear envelope as a nonlinear elastic structure and coupling deformations to a biochemical model of lamin remodeling and nucleocytoplasmic transport. These simulations predicted regions of high nuclear envelope stretch adjacent to cell-nanopillar contacts, leading to maximized nuclear envelope tension on small nanopillars spaced by 4-5 microns. We then considered the effects on nuclear transport of YAP and TAZ and found that increased nuclear compression led to YAP/TAZ nuclear localization in agreement with previous experiments. Furthermore, the simulated force load per lamin was maximized on nanopillar substrates with high nuclear stretch. The magnitude of this load was modulated by the rate of actin cap assembly and the overall expression level of lamin A/C - decreasing lamin content in the nuclear envelope led to a higher likelihood of rupture. We validated this prediction in subsequent experiments with lamin-depleted U2OS cells, establishing the central importance of lamin transport and microenvironment nanotopography to nuclear mechanotransduction. 2 SignificanceCell nuclei commonly experience large strains, but existing computational models do not explain the coupling between such deformations and molecular transport. Here, we present a modeling framework that includes the mechanics of nuclear deformations and the reaction-transport of molecules within the cytoplasm, nuclear envelope, and nuclear interior. As a well-controlled setup for comparing experiments and simulations, we consider nuclear indentations exhibited by cells on nanopillar substrates. Our simulations recapitulate measurements of nuclear YAP/TAZ localization from the literature and predict that low-lamin cells experience higher force loads at the nuclear envelope. We validate this prediction experimentally, showing that lamin-depleted cells are more likely to exhibit nuclear rupture. Overall, our framework presents opportunities to predict nuclear mechanoadaptation to different microenvironments.

Pathologic vertebral fractures are a major complication in metastatic spine disease. However, current clinical scores, such as Spinal Instability Neoplastic Score (SINS), show limited predictive capability, particularly within the indeterminate range where most clinical uncertainty lies. This study aimed to develop and evaluate quantitative computed tomography (qCT)-based subject-specific finite element (SSFE) models to predict vertebral strength in presence of different metastatic lesion types. Twelve ex vivo human spine segments, each containing one metastatic (n=12) and one adjacent control vertebra (n=12), were scanned using qCT and calibrated using a calibration phantom. Homogenised nonlinear finite element models were developed with spatially heterogeneous, isotropic, density-dependent material properties and loaded under uniaxial compression corresponding to 1.9% apparent strain. Ultimate failure load, stiffness, and strain distributions were compared between metastatic and control vertebrae. Predicted failure load ranged from 0.2 kN to 6.2 kN (mean. {+/-} standard deviation: 1.8 {+/-} 1.6 kN metastatic; 1.7 {+/-} 1.5 kN control), with no statistically significant difference between groups (p > 0.05). Normalised failure load varied widely, reflecting lesion-specific mechanical heterogeneity. Lytic lesions generally weakened vertebrae, whereas mixed and blastic lesions occasionally enhanced strength, likely due to localised sclerosis or reactive bone formation. High compressive axial strains (greater than 0.019) were frequently concentrated near the endplates, particularly in lytic vertebrae. qCT-derived bone mineral density strongly correlated with failure load (R{superscript 2} = 0.74-0.77). These findings highlight the complexity of metastatic vertebral mechanics and demonstrate that qCT-based SSFE modelling provides a quantitative framework for assessing fracture risk, complementing conventional imaging-based tools.

The left ventricle (LV) exhibits torsional deformation during systole, and mechanical relaxation begins during the isovolumic phase. Recent advances in imaging techniques, such as MRI, have revealed that myocardial tissue deformation and sarcomere length changes occur during the isovolumic relaxation phase, even when the chamber volume remains constant. Although such ventricular deformation during the isovolumic phase is considered important for blood ejection and filling efficiency, its mechanistic contribution to contraction and relaxation remains unresolved. In this study, we hypothesized that sarcomere length dynamics during the isovolumic phase affect the isovolumic contraction and relaxation time (IVCT and IVRT) by regulating the contraction force via the force-velocity relationship of ventricular myocytes. To investigate this hypothesis, we focused on experimentally reported differences in the relationship between sarcomere length and LV volume across the endocardial and epicardial layers, as described by Rodriguez et al. We constructed and compared two types of hemodynamic models within the same integrated framework consisting of a circulation model, a LV model, and a myocardial cell contraction model by Negroni-Lascano et al., which differ only in how sarcomere length is determined: a volume-based length model (VL model), in which sarcomere length is uniquely determined by LV volume, and a volume-force-coupled length model (VFL model), in which sarcomere length is determined by the balance between LV volume and contraction force. Simulation results showed that in the VFL model, compared to the VL model, sarcomere length changed during the isovolumic phase, leading to a decrease in contractile force and shortening of IVRT, which may contribute to improved hemodynamic efficiency. These results indicate that sarcomere length dynamics can mechanically regulate force decay during isovolumic relaxation, even under constant left ventricular volume. This study provides a theoretical framework for understanding the contributions of different layers within the LV wall to diastolic function during the isovolumic relaxation phase.

PurposeThis work investigates the role of the cancer cell morphology and elasticity on the deformation patterns under shear-flow in a micro-channel. MethodsA novel hybrid continuum-particle framework is developed to simulate cancer-cell dynamics. Cell membrane and nucleus geometries are reconstructed from microscopic images and modeled using Dissipative Particle Dynamics, while the surrounding blood plasma is treated as an incompressible Newtonian fluid. Cell-flow interactions are captured via an immersed boundary method. ResultsAll cancer-cell models exhibited a rapid deformation response within the first 1-2 ms, followed by morphology- and stiffness-dependent shape evolution. The compact morphologies showed strong recovery, whereas the other models evolved toward folded/lobed states with only intermittent partial recovery during shape transitions. Membrane stiffening dominated elongation and compactness loss, while nuclear stiffening modulated deformation excursions and partial recovery. These shape transitions were accompanied by near-field vortex reorganization and traction localization. Similar to deformation response the net membrane force exhibited a common start-up rise within 0-0.5 ms followed by relaxation. Compact morphologies produce lower and steadier forces. They show minimal stiffness dependence. Deformation-prone morphologies show stronger unsteadiness and clearer stiffness modulation. Cross-sectional velocity and vorticity fields showed a dominant x-directed hydrodynamic imbalance and lateral migration. ConclusionOur results demonstrate that morphology sets the stiffness modulated deformation patterns which effects the extracellular flow dynamics and traction. In turn, the resulting flow field and traction distribution feed back to influence subsequent deformation and migration. This mechanistic link provides a framework for interpreting circulating tumor cell transport in shear-dominated metastatic environments.

Spaceflight-Associated Neuro-ocular Syndrome (SANS) involves complex interactions between intracranial pressure (ICP), intraocular pressure (IOP), and cerebrospinal fluid (CSF) dynamics within the optic nerve subarachnoid space (ONSAS). While existing computational models address specific aspects of these interactions, they lack a comprehensive, system-level representation. To bridge this gap, we present the HEAD (Hemodynamic Eye-brain Associated Dynamics) model. By consistently integrating several previously proposed physiological sub-models, HEAD provides a unified lumped-parameter framework that fully couples cerebrovascular autoregulation, multi-territory ocular hemodynamics, and compartmentalized craniospinal-ONSAS CSF circulation under gravitational loading. This formulation enables the simultaneous analysis of eye-brain-CSF dynamics within a single computational tool. Model predictions were validated against experimental data from supine (0{degrees}) to head-down tilt (HDT, -30{degrees}) postures, accurately reproducing posture-dependent IOP increases and achieving an excellent ICP match against clinical benchmarks at the -6{degrees} HDT standard bed-rest angle. The coupled system predicts bed-specific ocular hemodynamic responses, with retinal blood flow exhibiting the largest relative increase under HDT compared to the ciliary and choroidal circulations. Crucially, explicitly modeling the ONSAS as a distinct compartment reveals a posture-dependent pressure drop of 1.89-3.69 mmHg between the intracranial and perioptic spaces. This compartmentalization yields a translaminar pressure profile that remains positive (8.05-11.83 mmHg) across all simulated conditions but is chronically reduced under sustained HDT. Ultimately, the HEAD model elucidates the physiological mechanisms linking gravitational stress to translaminar mechanics, providing a robust computational foundation to investigate SANS and supply boundary conditions for structural models of the optic nerve head.

Quantitative, reproducible characterization of aortic microstructure is essential for advancing vascular biomechanics and mechanobiology. To address this need, we present a comprehensive image-analysis workflow that extracts quantitative descriptors of tissue microstructure from multiphoton microscopy stacks of the murine thoracic aorta. Channel-specific signals are acquired for fibrillar collagen (second harmonic generation), elastin (two-photon autofluorescence), and cell nuclei (two-photon excited fluorescence). Following reorientation into the XZ plane, individual elastic lamellae are traced to quantify lamellar thickness and interlamellar spacing using circle-based geometry (Taubin fitting). After correction for vessel wall curvature via a cylindrical transformation, segmented nuclei are assigned to medial or adventitial compartments based on visual estimates of adventitial volume fraction, and nuclear morphology is characterized via ellipsoidal fitting in terms of nuclear aspect ratio and major-axis orientation. Collagen organization is resolved in XY sections by extracting fiber centerlines to quantify straightness and amplitude; traces from serial sections are then combined to reconstruct the three-dimensional collagen network and estimate porosity and linear fiber density, while fiber orientation distributions are derived from principal component analysis-based angles and fit using a von Mises mixture model. Finally, collagen and elastin volume fractions are computed via a two-stage fixed-threshold approach calibrated on a balanced training subset. Overall, this modular and robust workflow provides an integrated framework for studying aortic wall remodeling across physiological and pathological processes. Non-Technical SummaryAs the main blood vessel in our body, the aorta needs to be both strong and flexible. This balance comes from three main parts: elastic layers that allow the aorta to stretch, strong fibers that prevent tearing, and cells that sense and respond to changes in blood pressure and other signals. When any of these components are altered, the aorta may stiffen or weaken, which can interfere with normal blood flow. In this study, we developed a clear and consistent way to measure the structure of the aortic wall using microscope images. The approach examines how thick the elastic layers are and how far apart they lie, the size and orientation of cell centers, and how straight or wavy structural fibers appear. It also estimates how much of each component is present in the aortic wall. Because the same steps are applied each time, results can be fairly compared across different conditions. Overall, this tool transforms detailed images into simple measurements, helping scientists understand how the aorta changes in health and disease.

Digital volume correlation (DVC) has become the benchmark experimental technique for full-field strain measurement in bone mechanics. In our previous work we developed a novel data-driven image mechanics (D2IM) approach that learns from DVC data and predicts displacement fields directly from undeformed X-ray computed tomography (XCT) images, deriving strain fields from such predictions. However, strain fields derived through numerical differentiation of displacement fields amplify high-frequency noise, and regularization techniques compromise spatial resolution while incurring substantial computational costs. Here we propose the upgrade D2IM-Strain to predict strain fields directly from XCT images of bone. Two prediction strategies were compared: displacement-derived strain and direct strain prediction. The direct strain prediction model significantly improved accuracy particularly for strain magnitudes below 10000{micro}{varepsilon}, taken as a representative threshold value for bone tissue yielding in compression. In addition, the direct approach reduced false-positive high-strain classifications by 75%. By eliminating numerical differentiation, the approach reduces noise amplification while maintaining computational efficiency. These findings represent a critical step toward developing robust data-driven volume correlation methods for hierarchical materials.