Biomechanics and Modeling in Mechanobiology

The response of the brain to the explosion induced primary blast waves is actively sought. Over the past decade, reasonable progress has been made in the fundamental understanding of bTBI using head surrogates and animal models. Yet, the current understanding of how blast waves interact with the human is in nascent stages, primarily due to lack of data in humans. The biomechanical response in human is critically required so that connection to the aforementioned bTBI models can be faithfully established. Here, using a detailed, full-body human model, we elucidate the biomechanical cascade of the brain under a primary blast. The input to the model is incident overpressure as achieved by specifying charge mass and standoff distance through ConWep. The full-body model allows to holistically probe short- (<5 ms) and long-term (200 ms) brain biomechanical responses. The full-body model has been extensively validated against impact loading in the past. In this work, we validate the head model against blast loading. We also incorporate structural anisotropy of the brain white matter. Blast wave human interaction is modeled using a conventional weapon modeling approach. We demonstrate that the blast wave transmission, linear and rotational motion of the head are dominant pathways for the biomechanical loading of the brain, and these loading paradigms generate distinct biomechanical fields within the brain. Blast transmission and linear motion of the head govern the volumetric response, whereas the rotational motion of the head governs the deviatoric response. We also observe that blast induced head rotation alone produces a diffuse injury pattern in white matter fiber tracts. Lastly, we find that the biomechanical response under blast is comparable to the impact event. These insights will augment laboratory and clinical investigations of bTBI and help devise better blast mitigation strategies.

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.

PurposeBiomaterial and stem cell delivery are promising approaches to treating myocardial infarction. However, the mechanical and biochemical mechanisms underlying the therapeutic benefits require further clarification. This study aimed to assess the deformation of stem cells injected with the biomaterial into the infarcted heart. MethodsA microstructural finite element model of a mid-wall infarcted myocardial region was developed from ex vivo microcomputed tomography data of a rat heart with left ventricular infarct and intramyocardial biomaterial injectate. Nine cells were numerically seeded in the injectate of the microstructural model. The microstructural and a previously developed biventricular finite element model of the same rat heart were used to quantify the deformation of the cells during a cardiac cycle for a biomaterial elastic modulus (Einj) ranging between 4.1 and 405,900 kPa. ResultsThe transplanted cells deformation was largest for Einj = 7.4 kPa, matching that of the cells, and decreased for an increase and decrease in Einj. The cell deformation was more sensitive to Einj changes for softer (Einj [&le;] 738 kPa) than stiffer biomaterials. ConclusionsCombining the microstructural and biventricular finite element models enables quantifying micromechanics and signalling of transplanted cells in the heart. The approach offers a broader scope for in silico investigations of biomaterial and cell therapies for myocardial infarction and other cardiac pathologies.

Our understanding of the process of formation of gyri (ridges) and sulci (furrows) in the cerebrum during development is moving beyond the role of neurons. Glial cells such as astrocytes, which are the most common cell type in the brain, are especially prominent under the gyri and have been shown to be essential for gyrification in ferrets. Their dysfunction has been linked to a host of neurodevelopmental diseases and disorders in humans, leading to abnormal folding patterns, hence, it is crucial to understand their role in the mechanics of folding. In this work, we propose two hypotheses of how astrocytes affect cortical folding. Our previous study demonstrated that astrocytes proliferate in the white matter (subcortex), and that inhibiting this process impairs gyrification. This leads to the pushing hypothesis, where astrocytes push up the cortex outwards, leading to formation of folds. On the other hand, ex vivo studies demonstrate areas of the cortex and subcortex that experience tension, due to the differential growth between materials in the brain tissue. This leads to the pulling hypothesis, where astrocytes experience tension from the surrounding tissue, leading to their proliferation, distribution of tensile stresses, and initiation of growth in those regions. Using the theory of finite growth, we implement these hypotheses via morphogenetic growth (pushing) and stress-driven (pulling) growth criteria. We find that morphological trends during development between the pushing and pulling effects are not dissimilar, with a comparable gyrification index. The stress distributions from both models also show common features, but the pulling effect shows tension in the subcortex, which matches trends observed in experiments. Therefore, it is more likely that the astrocytes affect gyrification by proliferating as a response to tensile cues and decrease the tension they experience, leading to deeper folds, rather than astrocytes independently proliferating under a gyri and then pushing the cortex up.

The interplay between cell-cell and cell-substrate interactions is complex yet necessary for the formation and well-functioning of tissues. The same mechanosensing mechanisms used by the cell to sense its extracellular matrix, also play a role in intercellular interactions. We used the discrete element method to develop a computational model of a deformable cell that includes subcellular components responsible for mechanosensing. We modeled a cell pair in 3D on a patterned substrate, a simple laboratory setup to study intercellular interactions. We explicitly modeled focal adhesions between the cells and the substrate, and adherens junctions between cells. These mechanosensing adhesions matured; their disassembly rate was dictated by the force they carry. We also modeled stress fibers which bind the discrete adhesions and contract. The mechanosensing fibers strengthened upon stalling and exerted higher forces. Traction exerted on the substrate was used to generate maps displaying the magnitude of the tractions along the cell-substrate interface. Simulated traction maps are compared to experimental maps obtained via traction force microscopy. The model recreates the dependence on substrate stiffness of the tractions spatial distribution across the cell-substrate interface, the contractile moment of the cell pair, the intercellular force, and the number of focal adhesions. It also recreates the phenomenon of cell decoupling, in which cells exert forces separately when substrate stiffness increases. More importantly, the model provides viable molecular explanations for decoupling. It shows that the implemented mechanosensing mechanisms are responsible for competition between different fiber-adhesion configurations present in the cell pair. The point at which an increasing substrate stiffness becomes as high as that of the cell-cell interface is the tipping point at which configurations that favor cell-substrate adhesion dominate over those favoring cell-cell adhesion. This competition is responsible for decoupling. Additionally, we learn that extent of decoupling is modulated by adherens junction maturation.\n\nStatement of SignificanceCells are sensitive to mechanical factors of their extracellular matrix while simultaneously in contact with other cells. This creates complex intercellular interactions that depend on substrate stiffness and play a role in processes such as development and diseases like cardiac arrhythmia, asthma, and cancer. The simplest cell collective system in vitro is a cell pair on a patterned substrate. We developed a computational model of this system which explains the role of molecular adhesions and contractile fibers in the dynamics of cell-cell interactions on substrates with different stiffness. It is one of the first models of a deformable cell collective based on mechanical principles. It recreates cellular decoupling, a phenomenon in which cells exert forces separately, when substrate stiffness increases.

Moderate to extreme preterm birth (<32 weeks gestation) affects cardiopulmonary structure and function and is associated with increased risk of heart failure through adulthood. The rat hyperoxia (Hx) model (term born; postnatal Hx exposure) captures biventricular changes, including at the cell- and organ-scale, and pulmonary vascular remodeling seen in preterm humans. However, synthesizing these measures across scales and organ systems is challenging. We hypothesized that in-silico modeling of biventricular mitochondrial, myofiber, and organ-scale function plus circulatory function could capture key features of cardiopulmonary abnormalities due to preterm birth. Therefore, we calibrated a multiscale model to subject-specific biventricular pressure-volume data previously obtained from Hx rats alongside normoxic (Nx) controls to investigate the abnormalities in cardiopulmonary function at multiple scales in this animal model of human preterm birth. The calibrated model demonstrates excellent agreement with the data and captures the expected pulmonary vascular changes and right ventricular dilation seen in preterm born children. Our multiscale modeling approach captures cardiopulmonary abnormalities across spatial scales and provides an innovative approach to explore the consequences of preterm birth beyond preclinical experimental data alone. This is a foundational step in understanding the impact of preterm birth on cardiopulmonary disease in childhood as well as adulthood.

A previously developed computational model for cardiovascular mechanics is here calibrated using typical values of 22 clinically observed hemodynamic properties in healthy human subjects. The model includes spatially resolved representations of the four heart chambers. The left ventricle is represented as a truncated thick-walled prolate spheroid, with three modes of deformation (short and long axis contraction and torsion). The other chambers are represented as full or partial thick-walled spherical shells. Wave propagation and reflection in the aorta are represented using a one-dimensional model. The closed-loop system is completed using lumped elements representing vascular resistances, compliances and inertances. The resulting system of coupled ordinary differential equations can be solved computationally in less than 0.1 s per cardiac cycle. In the present study, the values of 19 key input parameters to the model are estimated by minimizing the deviation of model predictions from the 22 observed hemodynamic properties. The resulting calibrated reference model provides a baseline for theoretical studies exploring the relationship between fundamental properties of the cardiovascular system, such as ventricular contractility and stiffness or vascular resistance and compliance, and clinically available measurements such as blood pressures, chamber volumes or valve flow waveforms.

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.

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.

Nucleus deformation has been shown to play a key role in cell mechanotransduction and migration. Therefore, it is of wide interest to accurately characterize nucleus mechanical behavior. In this study we present the first computational investigation of the in-situ deformation of a heterogeneous cell nucleus. A novel methodology is developed to accurately reconstruct a three-dimensional finite element spatially heterogeneous model of a cell nucleus from confocal microscopy z-stack images of nuclei stained for nucleus DNA. The relationship between spatially heterogeneous distributions microscopic imaging-derived greyscale values, shear stiffness and resultant shear strain is explored through the incorporation of the reconstructed heterogeneous nucleus into a model of a chondrocyte embedded in a PCM and cartilage ECM. Externally applied shear deformation of the ECM is simulated and computed intra-nuclear strain distributions are directly compared to corresponding experimentally measured distributions. Simulations suggest that the nucleus is highly heterogeneous in terms of its mechanical behaviour, with a sigmoidal relationship between experimentally measure greyscale values and corresponding local shear moduli (n). Three distinct phases are identified within the nucleus: a low stiffness phase (0.17 kPa [&le;] n [&le;] 0.63 kPa) corresponding to mRNA rich interchromatin regions; an intermediate stiffness phase (1.48 kPa [&le;] n [&le;] 2.7 kPa) corresponding to euchromatin; a high stiffness phase (3.58 kPa [&le;] n [&le;] 4.0 kPa) corresponding to heterochromatin. Our simulations indicate that disruption of the nucleus envelope associated with lamin-A/C depletion significantly increases nucleus strain in regions of low DNA concentration. A phenotypic shift of chondrocytes to fibroblast-like cells, a signature for osteoarthritic cartilage, results in a 35% increase in peak nucleus strain compared to control. The findings of this study may have broad implications for the current understanding of the role of nucleus deformation in cell mechanotransduction.

The evolution of palliative surgical procedures for children born with congenital heart defects has proven remarkably successful in extending life, but the resulting non-physiological circulation predisposes to myriad sequelae that compromise quality of life and overall life span. Among these procedures, standard-of-care Fontan completion surgery bypasses the nonfunctional ventricle and provides steady flow of deoxygenated blood to the lungs via a synthetic conduit that typically connects the inferior vena cava to a pulmonary artery. This altered circulation reduces cardiac output, elevates central venous pressures, and possibly contributes to adverse remodeling of the pulmonary vessels. There is, therefore, strong motivation to develop a next generation Fontan conduit capable of serving as a sub-pulmonic pulsatile pump, and there are now several reports of initial attempts. None of these studies have been driven by biomechanical considerations, however, and none have achieved the desired functionality. We thus present a novel analytical framework to improve design and guide fabrication by focusing on the microstructure and material properties of the contractile myofibers and associated passive matrix. Our optimized designs simultaneously ensure desired levels of stroke volume, ejection fraction, and pressure generation given constraints on Frank-Starling myofiber contraction and the limited space within the thoracic cavity of a three-to four-year-old child. This analysis also highlights the need to minimize any associated axial force or torque generation that a pulsatile conduit could transmit to the host vessels at the requisite anastomoses.

Mechanical loading regulates chondrocyte health in articular cartilage. While physiological stimuli maintain homeostasis, supra-physiological stimuli from joint injuries disrupt it, leading to osteoarthritis (OA). OA is a prevalent degenerative joint disease affecting millions worldwide. OA progression involves complex mechanical and biochemical interactions across multiple length scales, which are challenging to investigate experimentally. In silico models provide an effective framework to explore these mechanisms. This study developed an integrated multiscale modeling framework for articular cartilage. It combined finite element (FE) models at tissue and cellular scales with an intracellular gene/protein regulatory network. The network incorporated key chondrocyte mechanotransduction and inflammatory pathways. A Hills function was used to link cellular forces from the FE model to a mechanical loading input to the regulatory network. Hills function constants were calibrated using a genetic algorithm approach. Calibration was performed by matching experimental and simulated expressions of COL-II and ADAMTS5 of cartilage explants under 20% dynamic compression. As a validation step, model simulations were performed at 10% dynamic compression of cartilage explants. COL-II and ACAN were overestimated, and ADAMTS5 was underestimated compared with experimental data. Furthermore, predicted sGAG loss matched the trend of experimental data. Simulated chondrocyte responses for varying spatial locations revealed spatial heterogeneity of chondrocyte activity. Over-all, the multiscale modeling workflow developed in this study provides a first step towards a powerful tool to increase the understanding of the complex interplay of mechanics and inflammation in articular cartilage. By integrating tissue, cellular, and intracellular scales, it offers a comprehensive framework for studying cartilage mechanobiology and guiding future therapeutic strategies. HighlightsO_LIDeveloped an integrated multiscale model linking tissue, cellular mechanics, and gene regulation in articular cartilage. C_LIO_LICoupled cellular mechanical forces to gene regulatory networks using Hills function approach. C_LIO_LICalibrated Hills function parameters via genetic algorithm using experimental cartilage explant compression data. C_LIO_LIPredicted spatial heterogeneity of chondrocyte activity and cartilage biomarker expression under dynamic compression C_LIO_LIEstablished computational framework can be used for studying cartilage mechanobiology and osteoarthritis therapeutic strategies C_LI

Many congenital conditions and surgical interventions perturb the hemodynamics experienced by proximal pulmonary arteries during early postnatal development, thus leading to differential gene expression and associated changes in vascular structure and function. Among these, pathologic conditions include patent ductus arteriosus, pulmonary atresia and stenosis, and hypoxemia-induced pulmonary hypertension while surgical interventions include the placement of a Blalock-Taussig shunt as well as Glenn, Fontan, and Norwood procedures. Despite the significant morbidity associated with these diverse conditions, there has been little attention directed towards understanding natural postnatal development of pulmonary arteries from both biological and mechanical perspectives. With-out such information, we cannot truly understand the phenotype of the affected pulmonary artery, which is fundamental to improving diagnosis, treatment, and prognosis. In this paper, we present novel data from wild-type mice that document normal postnatal changes in select gene expression, vascular wall composition, and biomechanical properties of proximal pulmonary arteries. These findings enabled the establishment of a novel, data-informed computational model of pulmonary artery development capable of simulating outcomes in response to perturbations in the pulmonary artery hemodynamic environment.

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.

The structural integrity of spinal cord tissue and the transmission of mechanical stimuli across the different levels of tissue microarchitecture and varying spatial scales of mechanical loading challenge experimental and computational efforts to accurately model, simulate and interpret tissue mechanics, leading to conflicting findings in existing literature. Here, we demonstrate that the bead size used in spherical indentation tests significantly affects the stiffness ratio of spinal cord gray to white matter, a dependence which we only observe on the transverse plane and not the coronal plane of the tissue. Our study reveals a shift in stiffness ratio such that for smaller spherical indenters gray matter is stiffer than white matter, while for larger indenters, white matter is stiffer than gray matter. The mean relative change from the 100 {micro}m bead to the 500 {micro}m bead differed between anatomical planes, with transverse sections showing a decrease in gray matter (-13.3%) and an increase in white matter stiffness (+26.9%), accompanied by a reduction in the gray-to-white matter stiffness ratio from 1.07 to 0.76, whereas coronal sections exhibited increases in both gray (+21.0%) and white matter (+33.8%), along with a change in the ratio from 0.99 to 1.14. These findings contribute to explaining previously contradictory results in the literature and underscore the relevance of spatial scales in mechanical characterization studies.

Mechanical forces applied to cells are known to regulate a wide variety of biological processes. Recent studies have supported that mechanical forces can cause nuclear deformation, leading to significant alterations in the gene expression and chromatin landscape of the cell. While the stresses and strains applied to cells is it is often known or controlled experimentally on a macroscopic length scale, it is often unclear what the actual forces and displacements are at the microscopic level of the cell. In this work, we created a model of cell deformation during application of mechanical stretch to cultured cells growth on a flexible membrane. This configuration is commonly used is in experimental studies as a means to apply controlled mechanical strains to adherent cultured cells. The parameters used in the study were used for application of strain to a mesenchymal stem cell stretched on a membrane. computational model was created to simulate the stresses and strains within the cell under a variety of stain amplitudes, waveforms and frequencies of mechanical loading with the range of commonly used experimental systems. The results demonstrate the connection between mechanical loading parameters applied through the flexible membrane and the resulting stresses and strains within the cell and nucleus. Using a viscoelastic model of chromatin, we connected the results provide to a rough model of resulting deformation within chromatin from the forces applied to the nucleus. Overall, the model is useful in providing insight between experimentally applied mechanical forces and the actual forces within the cell to better interpret the results of experimental studies. Statement of SignificanceIn this work, we created a computational model of the mechanical stretching of cell on a flexible membrane under cyclic mechanical loading. This model provides insight into the forces and displacements inside of cell that result from that application of stretch. As many experiments use this set up, our work is relevant to interpreting many studies that use mechanical stretch to stimulate mechanotransduction.

Finite element (FE) models of the human head are important injury assessment tools but developing a high-quality, hexahedral-meshed FE head model without compromising geometric accuracy is a challenging task. Important brain features, such as the cortical folds and ventricles, were captured only in a handful of FE head models that were primarily developed from two meshing techniques, i.e., surface-based meshing with conforming elements to capture the interfacial boundaries and voxel-based meshing by converting the segmented voxels into elements with and without mesh smoothing. Despite these advancements, little knowledge existed of how similar the strain responses were between surface- and voxel-based FE head models. This study uniquely addressed this gap by presenting three anatomically detailed models - a surface-based model with conforming meshes to capture the cortical folds-subarachnoid cerebrospinal fluid and brain-ventricle interfaces, and two voxel-based models (with and without mesh smoothing) - derived from the same imaging dataset. All numerical settings in the three models were exactly the same, except for the meshes. These three models were employed to simulate head impacts. The results showed that, when calculating commonly used injury metrics, including the percentile strains below the maximum (e.g., 99 percentile strain) and the volume of brain element with the strain over certain thresholds, the responses of the three models were virtually identical. Different strain patterns existed between the surface- and the voxel-based models at the interfacial boundary (e.g., sulci and gyri in the cortex, regions adjacent to the falx and tentorium) with strain differences exceeding 0.1, but remarkable similarities were noted at the non-interfacial region. The mesh smoothing procedure marginally reduced the strain discrepancies between the voxel- and surface-based model. This study yielded new quantitative insights into the general similarity in the strain responses between the surface- and voxel-based FE head models and underscored that caution should be exercised when using the strain at the interface to predict injury.

State-of-the-art cardiac electromechanical modelling and simulation form the basis for recent developments in cardiac Digital Twin technologies. However, a comprehensive evaluation of electromechanical models at cellular, tissue, and organ level has yet to be performed that addresses both ECG and pressure-volume biomarkers. Such an evaluation would build credibility for applications of cardiac Digital Twins in clinical research and therapy development. We aimed to follow ASME V&V40 standards to develop a strategy for calibration, validation, and uncertainty quantification of ventricular electromechanical Digital Twins under healthy conditions. We performed a multi-scaled review of ventricular electromechanics to compile a dataset for calibration and validation incorporating ECG, pressure-volume, displacement, and strain biomarkers. When applied to a biventricular multiscale model, we achieved healthy calibrated values for the QRS duration (89 ms), QT interval (360 ms), left ventricular ejection fraction (LVEF) (51 %), peak systolic pressure (14 kPa), end diastolic (110 mL) and end systolic volumes (50 mL), peak ejection flow rate (180 mL/ms). Model validation was performed by comparison to displacement and strain biomarkers including systolic atrioventricular plane displacement (1.5 cm), systolic fibre strain (-0.18) and longitudinal strain (-0.15). Sensitivity analysis of model parameters at cellular and ventricular scales was also performed. We quantified the effects of variability in ionic conductance, mechanical stiffness, cross-bridge cycling dynamics, and systemic circulation on action potential and active tension dynamics at the cellular scale, and on ECG, pressure-volume, displacement, and strain biomarkers at the ventricular scale. Simulations showed that the relationship between healthy LVEF and T wave biomarkers was primarily underpinned by variability in L-type calcium channel conductance and SERCA activity through multi-scale effects. In this study, we pave the way towards credible cardiac electromechanical Digital Twins by setting the basis for a strategy for calibration and validation based on both ECG and mechanical biomarkers.

The intracranial pressure is implicated in many homeostatic processes in the brain and is a fundamental parameter in several diseases such as e.g. idiopathic normal pressure hydrocephalus (iNPH). The presence of a small but persistent pulsatile intracranial pulsatile transmantle pressure gradient (on the order of a few mmHg/m at peak) has recently been demonstrated in iNPH subjects. A key question is whether pulsatile ICP and displacements can be induced by a small pressure gradient originating from the brain surface e.g. pial arteries alone. In this study, we model the brain parenchyma as either a linearly elastic or a poroelastic medium and impose a pulsatile pressure gradient acting between the ventricular and the pial surfaces. Using this high-resolution physics-based model, we compute the effect of the pulsatile pressure gradient on parenchyma displacement, volume change, fluid pressure, and fluid flux. The resulting displacement field is pulsatile and in qualitatively and quantitatively good agreement with the literature, both with elastic and poroelastic models. However, the pulsatile forces on the boundaries are not sufficient for pressure pulse propagation through the brain parenchyma. Our results suggest that pressure differences originating over the brain surface via e.g. pial artery pulsatility are not sufficient to drive interstitial fluid (ISF) flow within the brain parenchyma and that potential pressure gradients found within the parenchyma rather arise from local pressure pulsations of blood vessels within the brain parenchyma itself.