Journal of the Mechanical Behavior of Biomedical Materials

Despite lacking a dedicated structural function, brain tissue is exposed to severe mechanical loading during accidents and trauma, with potentially permanent or lethal consequences. Therefore, characterizing its mechanical behavior assumes particular importance for designing protective equipment, planning surgeries, and training neurosurgeons, tasks that often rely on physical brain replicas made from phantom materials. Although the mechanics of brain tissue has been extensively studied, the selection of suitable phantom materials still relies on simplified metrics that fail to capture the highly nonlinear and rate-dependent behavior of the brain. In this work, we characterize the mechanical response at low-to-medium strain rates of common synthetic brain phantom materials, including Sylgard 527, Ecoflex Gel, and a composite hydrogel, and establish rigorous comparisons with porcine brain tissue as a benchmark. To this end, we employ a multi-step stress relaxation protocol under both uniaxial extension and compression across two different strain rates, introduce thermodynamic metrics, and perform comparisons based on rigorous statistical analysis. In particular, we isolate the thermodynamic equilibrium stress-strain curves, representing the baseline material's response, from rate-dependent dissipative inelastic effects, relatable to viscoelastic and poroelastic mechanisms. Our results provide a thorough comparison, revealing critical limitations in existing simulant materials and highlighting significant space for developing improved brain surrogates. The comprehensive dataset collected can also support the calibration of constitutive models, enhancing the predictive accuracy of numerical simulations involving brain tissue and its surrogates. More broadly, this study highlights the need to move beyond simplistic, scalar-metric-based comparisons of mechanical responses across materials.

Purpose: Fibrotic remodeling of the uterus, associated with aging, disease, and environmental exposures, alters collagen organization and tissue stiffness, yet how these changes influence organ-level mechanical behavior remains poorly understood. Glutaraldehyde (GA)-induced collagen crosslinking was used as a controlled surrogate for fibrotic remodeling to determine whether image-informed inverse finite element analysis (iFEA), combined with inflation testing and micro-computed tomography (microCT), could detect and quantify the resulting changes in uterine constitutive behavior. Methods: Murine uteri (n = 6 untreated, n = 6 GA-crosslinked) underwent volume-controlled balloon inflation with simultaneous microCT imaging to quantify deformation of the inner and outer wall boundaries for iFEA. Specimen-specific Gasser-Ogden-Holzapfel (GOH) finite element models were optimized by adjusting model parameters to reproduce experimentally measured wall contours throughout inflation. Model performance was evaluated using contour root mean square error (RMSE), and parameter identifiability was assessed through sensitivity analyses. Results: GA treatment significantly increased inflation work, linear stiffness, and maximum inflation resistance (p < 0.001). The iFEA framework accurately reproduced experimental deformation (RMSE < 3%) and revealed significant increases in the estimated GOH parameters C10 (9.2-fold), k1 (2.0-fold), and k2 (2.7-fold), consistent with increased effective tissue stiffness and a shift toward earlier collagen fiber recruitment. Sensitivity analyses demonstrated unique, well-defined minima for all parameter combinations. Conclusion: Image-informed iFEA provides a quantitative framework for relating collagen remodeling to organ-level uterine mechanics through specimen-specific constitutive parameter estimation. This approach establishes a foundation for investigating the mechanical consequences of uterine fibrosis and other remodeling processes.

The kidney is a critical soft-tissue organ responsible for blood filtration. Accurate constitutive models of the kidney are essential to predict tissue deformation and stress, yet existing models prescribe the strain energy function a priori, largely treat tissue variability as deterministic, and do not distinguish between cortex and medulla. Here we use Gaussian constitutive artificial neural networks to discover probabilistic strain energy functions for kidney cortex and medulla from tension, compression, and shear experiments. The medulla is approximately twice as stiff as the cortex across all three modes: The effective Young's moduli are 3.41, 5.04, and 4.48 kPa for the medulla and 1.43, 2.32, and 2.87 kPa for the cortex in tension, compression, and shear. Both regions display pronounced tension--compression asymmetry. When trained on all three modes, the network discovers strain energy functions in the second invariant alone over the tested deformation range. The cortex model selects two exponential I2 terms, while the medulla model selects an additional linear I2 term. Gaussian external weights propagate specimen-to-specimen variability into closed-form probabilistic stress predictions. Our region-specific probabilistic models capture the mechanical heterogeneity and variability of the kidney and enable more realistic simulations of kidney deformation and stress for surgical planning, needle interventions, and renal trauma.

Healing of the gastrointestinal (GI) tract following surgical interventions is governed by coupled mechanobiological processes and may lead to severe complications such as perforation or stricture formation. A first comprehensive mechanobiological modeling framework of GI healing is herein proposed. The framework addresses two clinically relevant applications of bioprinting: endoscopic treatment of GI tissue defects and bioprinting-assisted surgical anastomosis. Tissue remodeling is based upon a hyperelastic material model with fiber dispersion coupling the spatiotemporal reaction diffusion dynamics of fibroblasts and cytokines with collagen deposition, reorientation, and plastic deformation. An active stress formulation drives fibroblasts and myofibroblast contraction while a multiplicative decomposition approach is adopted for collagen remodeling as a permanent change. The governing equations are discretised in time with an implicit Euler scheme, finite element in space and solved by a staggered algorithm implemented in the open-source platform FEniCS. Numerical simulations show that the initial collagen fibers content and orientation strongly influence fibroblast infiltration and scar contracture formation. The study quantifies the advantage of having a collagen bioprinted architecture aligned with tissue fibers, to reduce structural remodeling distortion. The model also predicts a strong correlation between collagen deposition and luminal burst pressure during the recovery of mechanical integrity in GI anastomosis.

Extrusion-based bioprinting enables the development of tissue-like constructs; however, the impact of printing-associated shear stress on cell viability and function remains a critical consideration. To address this, we developed a comprehensive workflow combining rheological characterization, computational fluid dynamics (CFD) modelling, and experimental validation to predict and assess shear stress effects during bioprinting. The rheological properties of gelatin methacryloyl (GelMA) at 5 % (w/v, 20 {degrees}C) and 10 % (30 {degrees}C) concentrations were modelled, comparing various non-Newtonian regression models. CFD simulations were validated using micro-particle image velocimetry, showing agreement between predicted and measured velocities. The impact of bioprinting-associated shear stress on cell viability was assessed using a co-culture of human umbilical vein endothelial cells and breast epithelial cells. Immediate post-printing analysis revealed increased apoptosis in GelMA 5 % (w/v, 20 {degrees}C), although 10 % (w/v) GelMA demonstrated higher shear stress levels compared to 5 % GelMA. After 1 day of culture in crosslinked hydrogels, apoptosis increased in extrusion pressure, demonstrating the impact of low levels of acute shear stress. This workflow provides a robust methodology for predicting acute shear stress impacts during bioprinting, laying the foundation for future optimization studies.

Therapeutic footwear with rocker soles is widely used to reduce plantar pressure in individuals at risk of diabetic foot ulceration. Of the various rocker sole characteristics, apex position, rocker angle, and apex angle have been extensively studied; however, the effect of forefoot rocker curvature has been seldom investigated. The aim of this study was to assess the isolated effect of forefoot rocker curvature on plantar pressure distribution, lower limb gait kinematics, and comfort. Ten healthy adults walked in a modular footwear setup with three interchangeable midsoles featuring a straight-line rocker profile (CRi), a rocker radius of 60% (CR60), and 45% (CR45) of the shoe length. In-shoe plantar pressure was measured using 99-sensor capacitive insoles. Kinematics of lower limb joints were measured via inertial measurement units. Comfort-related parameters were assessed using a Visual Analog Scale. Data were analyzed using non- parametric tests. CRi demonstrated significantly lower pressure at the metatarsal heads than CR60 and CR45 (kPa; median[Q1-Q3]; mean pressure=80 [67-81]; peak pressure=151 [139-169]; p=0.002). CR45 showed significantly lower mean pressure and pressure-time integral in the midfoot than CRi (p=0.002). CR60 showed the largest maximum dorsiflexion angle (deg, median [Q1-Q3]: CR60=17.8[13.4-23.5]; p<0.001) and range of motion (deg: CR60=20.3[18.8-23.3]; p=0.007). CRi was perceived as the least comfortable rocker condition (score 0-100; median[Q1-Q3]; overall comfort=44 [35-57]; p=0.008). A straight rocker maximizes offloading at the expense of ankle kinematics and comfort; lower rocker radii enhance comfort, thus making the rocker curvature a key design parameter for therapeutic footwear.

Successful instrumented fusion of the lumbar spine is a complex surgical challenge, with positive patient outcomes dependent on careful surgical planning. Material selection is of critical importance to a mechanical construct supporting successful spinal fusion. Therefore, the aims of this study were to (a) evaluate the potential clinical use of finite element analysis (FEA) and (b) conduct a retrospective mechanical analysis of different implant materials in patients having undergone spinal fusion using FEA. Our methodology involved segmenting the spine from post-operative computed tomography (CT) image data from patients with previous spinal fusion. FEA models representing post-surgery cases were developed and different biomechanical loading conditions such as compression, flexion, bending and extension whilst testing pedicle screws of different materials were simulated. Patient specific finite element models were created, and biomechanical analysis were completed for all three patients. Polyetheretherketone (PEEK) constructs typically demonstrated lower peak implant stress when compared to titanium constructs for all spinal fusion levels. Furthermore, increasing the spinal fusion level resulted in significant differences in the maximum von Mises stress within both the bone and the instrumentation, whereas the 2-level fusion exhibited comparable stress levels in the bone irrespective of the instrumentation material. This pilot explores the potential of FEA as a clinical tool for assessing device and bone stresses. In our cohort, different materials can influence the stresses in both the instrumentation and the instrumented vertebrae, suggesting FEA can be useful pre- operative tool with regards to instrument selection and post-operatively to assess instrumentation and bone stresses. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/742715v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@55959aorg.highwire.dtl.DTLVardef@d0b9d6org.highwire.dtl.DTLVardef@158c348org.highwire.dtl.DTLVardef@7ce828_HPS_FORMAT_FIGEXP M_FIG C_FIG

Digital volume correlation (DVC) provides full-field three-dimensional measurements of internal deformation, but its accuracy and effective spatial resolution depend on image-processing and analysis choices. Here, we use fibrous, cartilaginous and mineralised tissues within a rat intervertebral disc (IVD) as a controlled multi-material case study, combining virtual compression with experimentally loaded synchrotron computed tomography images. We systematically evaluate phase retrieval and image filtering, bit-depth conversion, point-cloud design, subvolume size and strain-field smoothing. Stronger phase retrieval reduced correlation residuals while increasing displacement and strain errors, showing that residual minimisation alone can select poorer parameters. Image filtering sensitivity was greatest in fibrous tissue, which had the smallest characteristic image feature size, whereas inappropriate intensity mapping during 16-bit to 8-bit conversion preferentially degraded low-contrast cartilage. Increasing subvolume size, point spacing or strain-window size improved measurement robustness but progressively smoothed local strain heterogeneity. We demonstrate that the spatial resolution of strain measurement must be matched across tissue types in order to compare strain magnitude; in the IVD, matching DVC spatial resolution changed the apparent ratio of compressive strain among fibrous, cartilaginous and mineralised tissues from 3.1:1.9:1 to 14:7.7:1. These findings establish a sequential, deformation-based optimisation framework in which image characteristics guide processing, known deformations validate accuracy and strain fields are compared at matched measurement scales. The framework supports more reproducible and mechanically interpretable DVC analyses in heterogeneous biological and engineered materials.

Tissue expansion is an established technique for the reconstruction of skin defects, but the cost of conventional silicone expanders may limit its application in contexts with lower resource availability. This study evaluated, in an experimental model, whether a container for storing saline solution could act as a low-cost alternative device for tissue expansion. An experimental, prospective, controlled and randomized study was carried out with 10 Wistar rats, distributed in two groups: conventional silicone expander and saline storage container. Both underwent the same surgical and expansion protocol. Abdominal circumference gain, final expanded skin length, histological and histomorphometric aspects of the skin, including collagen types I and III, as well as exploratory cost analysis, were analyzed. The conventional expander produced a greater gain in abdominal circumference, but the final length of the expanded skin was similar between the groups. Microscopic evaluation showed preservation of the cutaneous architecture, intact epidermis, cellularized dermis, and collagen deposition in both groups. There was no significant difference in the proportion of type I or type III collagen. The saline solution container had a substantially lower cost per centimeter of expanded skin than the conventional expander. These findings suggest that the saline container can produce experimental skin expansion with final length and collagen composition similar to the conventional method, at a lower cost, although additional studies are needed before clinical application.

Repetitive low-intensity blast (LIB) exposure has been identified as a probable cause of mild blast-induced traumatic brain injury (mbTBI) and a chronic injury risk to U.S. military personnel. However, the human brains biomechanical response to this loading regime remains poorly characterized. Using blast-exposure-validated 3D models of human anatomy, we simulated the intracranial tissue response to blast pressure typically experienced by Warfighters during weapons training. Two scenarios were evaluated, a single-dose exposure and a repetitive-dose exposure, to study intracranial pressure (ICP), shear strains, and spectral content. Ansys LS-DYNA was used to generate planar blast waves with peak overpressures of 4-90 kPa and positive phase durations of 2.2-10 ms. Single exposures produced ICP ranging from 4.7-112.7 kPa, dependent on dose and positive phase duration. Under repetitive LIB exposure, peak ICP increased by 8-26% relative to single exposures, with an increase of high-frequency components (>2 kHz). These results demonstrate that LIB can produce measurable intracranial responses that are amplified through repetition, producing pronounced spectral content and elevated pressures despite low strain levels. This study underscores the need to further investigate cumulative dose effects and the value of computational approaches to clarify hypothesized mbTBI mechanisms in operationally relevant conditions.

Ultrasound is commonly used to assess structural changes in symptomatic Achilles tendons, but quantitative biomechanical metrics for progressive tendon deterioration remain limited. The goal of this study was to develop and validate an automated ultrasound tracking algorithm for regional tendon deformation and evaluate strain progression in survived and ruptured tendons during fatigue loading. We hypothesized that maximum strain, average strain, and strain heterogeneity would exhibit different trajectories between groups. Ten cadaveric Achilles tendons underwent cyclic loading with stress tests every 500 cycles until rupture or 150,000 cycles. Ultrasound images acquired during stress tests were analyzed using an automated tracking algorithm to generate spatially resolved regional strain fields. Ultrasound-derived bulk strain was highly correlated with actuator-derived strain in survived (R^2 = 0.968 +/- 0.017) and ruptured tendons (R^2 = 0.972 +/- 0.014). Maximum and average longitudinal strains progressively diverged between groups across fatigue life (Group x FatigueLife: p = 0.003 and p < 0.0001, respectively). During the first 10,000 cycles, average strain decreased in survived tendons ({beta} = -0.0268%, p = 0.0215) but not ruptured tendons ({beta} = 0.0147%, p = 0.1197), with a significant Group x Cycle interaction (p = 0.0061). This study demonstrates that the algorithm quantified Achilles tendon deformation with high fidelity and enabled spatially resolved strain assessment throughout fatigue loading. Maximum and average strain followed different trajectories between groups, whereas strain heterogeneity did not. Early differences in tendon biomechanics suggest that regional strain behavior may change before pronounced differences in absolute magnitude develop.

Purpose: Quantifying aortic morphology is central to surgical planning for thoracic endovascular aortic repair (TEVAR), yet no consensus exists on how best to represent three-dimensional aortic shape for outcome prediction. Two broad strategies have emerged: statistical shape analysis (SSA), which relies on statistical methods and dimensionality reduction to capture the most significant shape modes, and geometrically-informed approaches that extract descriptors grounded in differential geometry. Here, we directly compare these paradigms on a cohort of 290 CTA scans classified by surgical outcome (non-pathological, successful TEVAR, failed TEVAR). Methods: For the geometrically-informed approach, we use a two-dimensional feature space using normalized fluctuation in integrated Gaussian curvature $\widetilde{\delta K}$ and mean aortic radius $R$. For SSA, we construct a point-cloud shape model with dimensionality reduction using Principal Component Analysis (PCA) and evaluate classification performance as a function of the number of retained principal components. Results: SSA's leading principal components encode variations in global aortic size and are statistically redundant with ($R$, $\widetilde{\delta K}$), yet they lack a one-to-one correspondence with interpretable anatomical quantities. Testing on an unseen, independent dataset reveals that the geometrically-informed approach provided better generalizability than SSA. Using Gaussian process classification with 10-fold cross-validation, we find that the geometrically-informed approach achieves a higher weighted $F_1$ score than SSA achieves with up to 20 principal components. While SSA's full-dataset accuracy rises above 90\% with increasing dimensionality, this gain is driven by overfitting rather than genuine discriminative power. Conclusion: These results demonstrate that geometrically-informed descriptors offer a more interpretable, robust, and clinically translatable framework for aortic disease classification than data-driven statistical shape representations.

Employing DLA methods, this paper explores the self-assembly of collagen fibers and resulting fibrosis at three scales up to the scale of regular lobule models. This allows a mechanistic exploration of the effects of collagen on drug transport (flow and diffusion) and metabolism. In addition, this method permits an analysis of fiber growth characteristics. First, variations of the DLA method of Parkinson et al (1994) will be used to generate multiple explicit collagen microfibril self-assembly using DLA particles in one dimension using cubic grid blocks of (4 mm)3 in a 240 x 20 x 20 grid model. The second stage will be to assess the consequences of various densities of these fibers in three dimensions on flow reductions at a higher scale. Here we utilize DLA methods in cubic grid blocks of (80 nm)3 to mimic 3D collagen self-assembly of fibrils. We then apply a pressure gradient or specified flow rates across a spatially gridded version of these models to quantify flow effects. This region represents a local zone of liver tissue affected by fibrosis. Analytic models of fibrotic effects on flow are employed for comparison. A third stage explores the implications of fibrosis in a liver lobule model using multiple grid blocks of size 3200 mm to represent the lobule tissue. Here, a continuum model of fiber density is employed, based on the previous two scales. The model also includes the effects of additional grid blocks representing sinusoidal flow paths found in the lobule. We contrast and quantify drug propagation and metabolism of molecular dissolved versus nanoparticle delivery vehicles in fibrotic media, achieved by upscaling explicit collagen distributions to appropriate average values.

Introduction: Metastatic spinal lesions substantially alter vertebral mechanical properties and increase fracture risk. Computed tomography (CT) based finite element (FE) models can estimate vertebral strength, but their accuracy depends on how CT derived material properties are represented. This study evaluated the effect of two material grouping strategies on simulated strength and stiffness in metastatic vertebrae. Methods: We compared Adaptive Clustering (AC) with Uniform fixed width grouping in 44 vertebrae from 11 donors (8 osteolytic, 12 osteoblastic, 12 mixed, 12 no observed lesion (NOL)). FE models were generated based on CT scans with 2 to 500 material groups and compared for material mapping error and simulated strength and stiffness. Overall and lesion stratified agreement with experimental measurements was assessed in an exploratory analysis. Results: AC showed significantly lower Young's modulus root mean square error than Uniform (p < 0.05). Simulated strength and stiffness stabilised by 50 material groups. At 50 groups, simulated strength showed moderate correlation with experimental strength overall (R2 = 0.57), strongest in NOL vertebrae (R2 = 0.82) and lower in lesion-bearing vertebrae (R2 = 0.4-0.59). Stiffness showed weaker correlation overall (R2 = 0.27), highest in NOL vertebrae (R2 = 0.48) and negligible in mixed lesions (R2 = 0.007). Bland Altman analyses indicated systematic underestimation of experimental fracture load. Discussion: AC improved material-mapping fidelity, whereas increasing material groups beyond 50 had little influence on simulated strength or stiffness. Numerical stabilisation therefore did not imply experimental accuracy. Lesion stratified findings were exploratory and should be interpreted cautiously because of limited subgroup sizes.

Musculoskeletal symptoms are frequently reported following silicone breast implantation. However, the biological mechanisms linking implant-derived silicone exposure to skeletal muscle alterations remain poorly understood, partly because the biological effects of silicone have long been debated in the context of its biocompatibility. Here, we chemically characterized the low-molecular-weight fraction of the breast implant silicone exposome, readily released from implant gel through gel bleed, and investigated its potential biological consequences using an integrated approach combining analytical chemistry, clinical transcriptomics and histology, and controlled in vitro muscle experiments. Transcriptomic analyses of periprosthetic tissues associated with silicone implant rupture revealed unexpected myogenic and neuromuscular signatures in tissue conventionally regarded as predominantly fibrous, together with alterations in lipid metabolism and transport. These findings were supported by histological evidence of close interactions between periprosthetic tissue and skeletal muscle. Chemical analysis of the implant-gel extract detected linear siloxane L2 and cyclic siloxanes D3-D8, with tentative assignment of D9. In vitro, C2C12 cells exposed to the implant-gel extract showed up to 30% reduced viability and decreased expression of key neuromyogenic genes. Together, these findings provide convergent chemical, clinical, and experimental evidence that low-molecular-weight constituents of the breast implant silicone exposome may constitute a biologically active exposure capable of affecting skeletal muscle. The associated alterations in lipid metabolism and transport further provide a mechanistic framework for investigating the cellular handling and potential tissue distribution of hydrophobic silicone-derived species. These findings position silicone gel bleed as a biologically relevant source of chemical exposure rather than solely a material-integrity phenomenon.

Finite element (FE) head models are virtual tools to study brain biomechanics and their predictions must be numerically convergent. Previous convergence studies focused on the influence of mesh size, but the potential effect of element formulation on model convergence was often ignored. To address this, one original model with brain mesh size as 6.4 {+/-} 1.9 mm was modified to generate three derivatives with the same mesh topology but different element sizes, i.e., a coarse model (mesh size: 12.2 {+/-} 3.9 mm), a medium model (mesh size: 3.2 {+/-} 1.0 mm), and a fine model (mesh size: 1.6 {+/-} 0.5 mm). Three commonly used element formulations, i.e., reduced integration, selectively reduced (S/R) integration, and full integration, were implemented to the brain elements. These models were subjected to rotational loadings along the axial, coronal, and sagittal axes, respectively. The maximum relative displacement at representative sites and 95th percentile maximum principal strain at the whole brain level were used to evaluate mesh convergency. The results showed that the S/R integration yielded a 5% difference between the original and medium meshes, while the reduced and full integration revealed a difference over 5% even between the medium and fine meshes. This study verified that the mesh convergence of FE brain models is affected by the choice of element formulation and the S/R integration contributes to the fastest convergence behavior than the reduced and full integrations. It provided practical information on how to develop numerically convergent and computationally efficient FE brain models. HighlightsO_LIThis study verifies that the choice of element formulation affects the mesh convergence behavior of finite element brain models C_LIO_LIThis study finds the selectively reduced integration yields the fastest convergence behavior than the reduced and full integration C_LIO_LIThis study provides practical guidance on the choice of mesh density and element formulation on how to develop numerically convergent and computationally efficient finite element brain models. C_LI

BackgroundIn mouse classic amputation model, second phalanx (P2) is incapable of regeneration. Extracellular matrix (ECM) solution has shown limited ability to induce digit regeneration in classical amputation model of the murine digit. However, the effect of solid ECM particles on bone regeneration is not well understood due to difficulties in treating solid particles in classical amputation model. MethodsWe examined the regenerative effects of ECM particles in mice digit by establishing a soft tissue preserved amputation (SPA) model on P2, through removing the amputated bone whilst preserving soft tissue. ECM particles implanted into the amputation site and wrapped in the preserved soft tissues. Bone regeneration was assessed by morphological examination and micro-CT scans. ResultsWe observed bone regeneration in the SPA model; specifically, new bone formed at the P2 distal end. Implantation of ECM particles exerted a pro-regenerative effect, characterized by increased bone volume and decreased bone density. Moreover, the ECM induced the formation of free-floating bone, further supporting its role in bone regeneration. Combined treatment with ECM particles and bone morphogenetic protein 2 (BMP2) resulted in a significant increase in bone volume. ConclusionsWe demonstrate that soft tissue preservation at the amputation site can overcome the intrinsic regenerative limitationsl. Using SPA model, we found that ECM particles have a proven ability to promote bone regeneration, and that the combination of ECM particles with BMP2 further enhances bone regeneration. These findings underscore the therapeutic promise of ECM-based strategies, for clinical translation in non-regenerative finger injuries. Summary statementSolid-state Extracellular matrix can induce mice digit regeneration and has the potential for clinical application. HighlightsThe SPA model we developed enables ECM particles to adhere to wounds, and our research has found that: O_LIDigit bone regeneration was shown in SPA model . C_LIO_LIECM particles treatment promoted bone regeneration and can generate free-floating bone in SPA model. C_LIO_LICombination of ECM + BMP2 treatment induced strong digit regeneration in SPA model. C_LI

The primary source of strength in ligaments and tendons are hierarchically organized collagen fibers. These fibers largely do not regenerate after injury, with repair, nor in engineered replacements, limiting treatment options. Previously, we developed a culture system which guides ACL fibroblasts in high-density collagen gels to form native-size hierarchical fibers over 6 weeks, and demonstrated that intermittent cyclic stretch further improves maturation. However, additional maturation is needed for clinical relevance. Interestingly, we found cyclic load affected cells differentially depending on the degree of organization, with 10% cyclic strain driving early improvements in unorganized gels and 5% strain being more beneficial later in culture once cells were on aligned fibers. Here, we explored whether a stepped cyclic load, that increased or decreased in strain magnitude as collagen fibers developed, further improved maturation. We hypothesized that progressively decreasing cyclic strain as organization increases would drive cells to produce more mature hierarchical fibers, resulting in stronger replacements. Controls had intermittent cyclic stretch at 0, 5, 7, or 10% strain throughout culture, while stepped load constructs were cyclically loaded with a strain that increased or decreased by 2-3% every 2 weeks as constructs matured. Contrary to our hypothesis, neither decreasing nor increasing load led to further tissue maturation. We hypothesize stepped cyclic load may disrupt cellular tensional homeostasis, leading to repeated remodeling of collagen and shifted proteoglycan accumulation. This study provides insight into how stepped cyclic loading affects hierarchical fiber formation and maturation, which will help to engineer stronger replacements and better rehabilitation protocols.

Predictive biomechanical models of the tricuspid valve require accurate representation of the chordae tendineae, yet subject-specific chordal geometry is difficult to reconstruct from non-invasive imaging. Here, we adopt a framework for generating functionally equivalent synthetic chordae without prior knowledge of in vivo chordal attachments. To this end, we first adapt an anatomy-informed hyperelastic shape-matching method that establishes correspondence between end-diastolic and end-systolic leaflet configurations using chordal-mimicking forces, a rigid contact template, and a Gaussian-smoothed locally corrective pressure field. We then generate synthetic chordal insertion sites using zone-based rejection sampling and calibrate the unloaded length of each chord by combining reaction forces with the chordal stress-stretch relationship. The framework was evaluated using Texas TriValve 1.1, a high-fidelity finite element model of a human tricuspid valve validated against beating-heart echocardiography. We found that shape matching reproduced the target end-systolic geometry with a mean inter-surface distance of 0.29 {+/-} 0.35 mm. Moreover, synthetic chordal insertions faithfully reproduce end-systolic leaflet deformations. We subsequently examined synthetic chordal configurations containing 202, 225, and 450 insertions, informed by measurements from eight explanted human tricuspid valves. Here, increasing insertion number reduced mean inter-surface distance from 0.63 {+/-} 0.52 mm to 0.49 {+/-} 0.44 mm and reduced contact area errors from 3.52% to 0.59%. Across all configurations, mean maximum principal stretch errors in leaflet belly regions remained below 2.4%, while areal strain errors ranged from 0.34% to 10.01%. These results demonstrate that anatomically informed shape matching coupled with stress-based chordal calibration can reproduce tricuspid valve closure without explicit subject-specific chordal geometry. This framework provides a foundation for generating synthetic subvalvular anatomy for future imaging-derived, predictive tricuspid valve models.

Predicting wear in artificial joints requires integrating joint dynamics, contact mechanics and progressive surface evolution, yet these processes are often treated separately. In total hip arthroplasty (THA), finite-element approaches remain the reference standard, but they are computationally demanding and usually rely on boundary conditions from independent musculoskeletal (MSK) models, hindering consistent coupling and feedback between wear progression and movement dynamics. As a single-subject proof of concept, we present a computational framework that embeds wear estimation within forward MSK simulations through OpenSim-MATLAB integration. Contact variables are computed using an elastic-foundation formulation, and wear is updated through the Archard law, enabling cyclic prediction of contact mechanics and surface evolution within a single workflow at practical computational cost. The framework was evaluated in one subject with right THA during five activities of daily living and numerically benchmarked against finite-element simulations. A long-term walking analysis of 4 million cycles was also performed to assess geometry updating. Across tasks, peak contact pressures remained within 7% of finite-element predictions. Linear wear depth and volumetric loss showed maximum deviations of 16% and 13%, respectively. Accounting for progressive geometry changes yielded a maximum wear depth about 31% lower than linear extrapolation. These preliminary results support the framework's computational feasibility and numerical consistency for the tested case; nevertheless, multi-subject evaluation is required before broader predictive or clinical use.