Journal of Biomechanical Engineering

Anterior cruciate ligament (ACL) injuries disproportionately affect female adolescent athletes, with hormonal influences implicated in this sex disparity. However, the relationship between pubertal hormonal changes and ACL gene and protein expression remains poorly understood. This study characterized hormone receptor expression and transcriptional profiles in the anteromedial (AM) and posterolateral (PL) bundles of female porcine ACLs before and after puberty. ACL bundles were collected from pre-pubescent (8 weeks) and post-pubescent (>8 months) female Yorkshire cross-breed pigs (n=6/group) and analyzed using gene expression profiling, western blotting, and immunofluorescence. Pre-pubescent ACLs exhibited greater expression of primary matrix genes (COL1A1, COL1A2, ELN, TNMD), suggesting active matrix synthesis, while post-pubescent ACLs showed elevated secondary matrix genes (COL3A1, LUM, COMP), indicating a homeostatic state. Notably, estrogen receptor alpha (ER) gene and protein expression were significantly greater in post-pubescent ACLs, particularly in AM bundles, whereas G-protein coupled estrogen receptor (GPR30) expression was elevated pre-puberty. Both receptors were distributed homogeneously throughout the tissue. Progesterone receptor protein expression was not detected in any samples. Histologically, post-pubescent ACLs demonstrated decreased cellularity and thicker fascicles compared to pre-pubescent tissues. These findings indicate that ACL sensitivity to estrogen varies across development, with increased ER expression post-puberty potentially rendering the ligament more responsive to circulating estrogen. This work provides foundational evidence for age-dependent hormonal responsiveness in the ACL and motivates further investigation into how sex hormones influence ACL injury risk in adolescent females.

IntroductionArteries, like other soft tissues, exhibit viscoelastic mechanical behavior, meaning their response to stress and strain is time dependent. This implies that the way arteries deform depends not only on the amount of force applied but also on the rate at which the force is applied. This study investigates the effects of different loading rates on the mechanical behavior of human femoropopliteal arteries (FPAs) to understand their rate-dependent characteristics. MethodsHuman FPA specimens were collected from 14 donors, including 7 males and 7 females, aged 45-55 years. A 10x10 mm segment was isolated, mounted onto a biaxial testing device, and subjected to varying loading rates (10 to 50 mN/s). Mechanical responses were recorded, and stress-stretch curves were analyzed. Statistical analyses, including mixed-design ANOVA, assessed the impact of sex and loading rates on tissue stiffness. ResultsResults indicated significant loading-rate dependency, particularly in the circumferential direction. Stretch values decreased with increasing loading rates, more prominently in the circumferential than in the longitudinal direction (p-value<0.01). Statistical analyses revealed no significant interaction between sex and loading rate, though male arteries exhibited slightly higher compliance than female arteries. DiscussionThe findings demonstrate that the mechanical response of FPAs is highly dependent on the loading rate, with more pronounced effects observed in the circumferential direction. At higher loading rates, the human FPAs demonstrated a stiffer response in the circumferential direction. DedicationWe dedicate this work to the memory of our late student, Ali Zolfaghari Sichani, who passed away tragically during his doctoral studies. Ali performed the majority of the experiments and the initial analysis reported in this paper. His passion, dedication, and hard work were the foundation of this research, and he is deeply missed.

Finite element (FE) head models are valuable tools for investigating brain injury mechanics, with their reliability critically dependent on accurate material modelling. White matter (WM) is often considered mechanically anisotropic due to its aligned axonal fiber architecture and is commonly represented using fiber-reinforced hyperelastic formulations such as the Gasser-Ogden-Holzapfel (GOH) model. A fundamental assumption of the GOH model is that fibers contribute only in tension and not in compression, requiring the use of tension-compression switches. However, inconsistencies were noted in the formulation of tension-compression switches with the influence on computational biomechanics unknown. To address this knowledge gap, three commonly used switching schemes - differing in both the switching parameter and the treatment of compressed fibers - were theoretically elaborated and numerical implementation within the GOH framework to simulate the mechanical anisotropy of WM in impact simulations. Results from the case-based and group-level analyses demonstrated that both the switching parameter and the treatment of compressed fibers affected WM deformation. Significant cross-scheme strain differences were noted in the first principal strain at the element level and fiber strain at the fiber level. These findings highlighted the mechanical role of tension-compression switch in the GOH-based brain modelling and advocated the adoption of fiber stretch itself as the switching parameter to discriminate the tensile and compressive fibers. The current study provides important guidance for the anisotropic constitutive models in brain tissue and calls for direct verification of the tension-compression switch hypothesis in axonal fibers.

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 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.

Intervertebral disc degeneration (IVDD) compromises disc structures and mechanics, yet systematic evaluations of the mechanical responses and their relationship to morphological changes in preclinical models remain limited. This systematic review and meta-analysis synthesized mechanical and morphological alterations following experimental disc injury in in vivo animal models. Searches of MEDLINE, EMBASE and Web of Science databases were conducted in accordance with PRISMA guidelines. Study quality and risk of bias were assessed using modified CAMARADES and SYRCLE tools. Twenty-eight studies were included. Pooled analyses showed significant reductions in stiffness, Youngs modulus, and disc height, and significant increases in range of motion and degeneration grade, indicating both mechanical and structural deterioration. Youngs modulus appeared to be the most sensitive marker of functional degeneration. By contrast, creep and other viscoelastic responses showed non-significant changes. High heterogeneity was evident across studies, reflecting variability in injury models, species, timepoints, and testing methods. Evidence of publication bias was detected in several domains, and moderate methodological quality was noted with overall insufficient blinding and lack of sample size calculations. In vivo animal models of IVDD demonstrate robust and consistent mechanical and morphological degeneration after injury. Youngs modulus is a sensitive mechanical indicator, supporting its use in future preclinical research. Standardization of outcome definitions, methodology, and reporting is essential to improve comparability and enhance translation of preclinical findings to clinical research.

Functional spinal cord repair in zebrafish is governed by regeneration-favorable biochemical and mechanical cues within the lesion microenvironment. Alterations in extracellular matrix composition and stiffness are closely associated with axon regeneration. However, experimentally dissecting the interplay between mechanical signals and axonal regrowth in vivo remains technically challenging. Here, we present an agent-based modeling framework to simulate stiffness-mediated axonal growth trajectories across the lesion. We use this model to explore potential mechanisms underlying the characteristic growth patterns observed during zebrafish spinal cord regeneration. Computational predictions were qualitatively compared with confocal imaging data obtained from larval zebrafish. These phenomenological comparisons revealed a close agreement between simulated and experimentally observed axon growth, indicating that experimentally observed patterns could be governed by transient changes in the stiffness profile of the spinal cord and lesion microenvironment. Hence, our computational framework provides an in silico platform for investigating the role of mechanical cues in axon regeneration in the injured spinal cord.

The fibrous microstructure of tendons and ligaments is an important determinant of their mechanical behavior and integrity. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the inference of microstructural features within fibrous tissues and has recently been used to characterize the microstructure of dense connective tissues such as tendon and ligament. However, the effect of microstructural variations in tendon and ligament on DTI metrics remains unclear. To address this gap, we simulated diffusion MRI of second harmonic generation (SHG) image-informed square lattice fiber networks to determine which microstructural features have the strongest influence on DTI metrics. Then, we performed a second set of diffusion MRI simulations for randomly dispersed fibers within synthetic tendon volumes to relate DTI metrics to the influential microstructural features, including fiber dispersion. All DTI metrics were insensitive to collagen fiber crimp. Fiber dispersion did not affect mean diffusivity, decreased axial diffusivity, increased radial diffusivity, and decreased fractional anisotropy. These results provide valuable insight into the relationships between DTI metrics and microstructural properties of tendon and ligament, which is particularly relevant for inferring microstructural changes in impaired tissue using DTI. Furthermore, our findings are an important step in the translation of DTI for clinical and computational studies of dense connective tissues such as tendon and ligament.

ObjectiveTo investigate the effects of different gait patterns on knee joint biomechanics and dynamic stability during stair ascent. MethodsFourteen healthy males were recruited to ascend stairs using two distinct gait patterns: the "single-step" (leading with the same leg) and "cross-step" (alternating legs) strategies. Kinematic and kinetic data were collected synchronously using a Qualisys infrared motion capture system and a Kistler 3D force plate. Dynamic stability was quantified using the Margin of Stability (MOS), and knee joint biomechanics were evaluated using Patellofemoral Joint Stress (PFJS) and other relevant metrics. ResultsThroughout the gait cycle, there was no significant difference in the Medio-Lateral (ML) MOS between the single-step and cross-step patterns (P=0.318). However, in the Anterior-Posterior (AP) direction, the MOS for both patterns remained negative and decreased over time, with the cross-step pattern exhibiting significantly lower AP MOS values than the single-step pattern (P=0.002). At the moment of left foot-off, significant differences were observed in the right knee joint angle, right knee joint moment, net joint moment, effective quadriceps muscle lever arm, Quadriceps Force (QF), the angle between the quadriceps tendon and patellar ligament, Patellofemoral Joint Force (PFJF), patellofemoral joint stress, and patellofemoral contact area (all P<0.001). ConclusionsDuring stair ascent, the cross-step pattern reduces body stability, thereby increasing the risk of backward falls. Furthermore, this pattern increases patellofemoral joint stress, subjecting the knee to greater loading. Therefore, it is recommended to enhance lower limb muscle strength through targeted training to reduce fall risk. Additionally, adopting a more cautious gait strategy (such as the single-step pattern) can help minimize patellofemoral joint loading and mitigate the risk of patellofemoral pain.

Rotator cuff tendinopathy is highly prevalent in aging populations, yet the mechanisms leading to age-dependent tendon degeneration are not well understood. In addition to tensile loading, tendons are subjected to compressive forces at certain anatomical sites (e.g., Achilles, rotator cuff), where altered adaptive responses may contribute to degenerative remodeling. The objective of this study was to investigate age-related differences in tendon responses to dynamic compressive loading using an ex vivo model. Murine flexor tendon explants from young and aged animals were cultured in a biaxial bioreactor and subjected to different levels of dynamic compressive loading. We then observed changes in metabolic activity, matrix composition, matrix biosynthesis, matrix structure, and gene expression. Young tendons exposed to moderate levels of compression maintained homeostasis, whereas high compression induced a robust adaptive response characterized by increased glycosaminoglycan accumulation, elevated collagen content, and upregulation of remodeling-associated genes including collagen I, decorin, and MMP-9, as well as inflammatory and apoptotic markers. In contrast, aged tendons demonstrated a qualitatively different response, with transcriptional downregulation of key remodeling markers alongside elevated secretion of matrix-degrading enzymes and pro-inflammatory cytokines, indicative of a maladaptive mechanobiological response even at low compressive levels. These findings reveal that impaired mechanosensitivity and a lower threshold for injury may predispose chronically loaded tissues to degenerative pathology associated with excessive compressive loading.

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

AO_SCPLOWBSTRACTC_SCPLOWMuscle fibre type distribution influences both the metabolic and contractile properties of individual muscles. However, as humans tend to self-optimize their gait pattern to minimize cost of transport, these changes in muscle properties may influence gait biomechanics in manners that are difficult to isolate in in vivo experiments. The purpose of this study was to predict the influence of muscle fibre type distribution on the metabolic cost and biomechanics of simulated walking and running. We implemented a muscle model that could predict recruitment of slow and fast twitch muscle fibres in a framework for predictive musculoskeletal simulation. Subsequently, we employed the framework to investigate how metabolic cost of transport, stride length, stride frequency, and mechanical work performed by slow and fast twich muscle fibres were influenced by fibre type distribution across locomotion speeds from 1.0 to 4.5 m {middle dot} s-1. Our results predict that cost of transport increases as slow twitch area fraction decreases, while stride length and frequency was minimally affected by fibre type distribution at speeds resulting in walking. In contrast, fibre type distribution interacts with locomotion speed at speeds resulting in running. Specifically, we predict the existence of a threshold speed below which cost of transport decreases with an increasing proportion of slow twitch fibres, while cost of transport increases with increasing proportions of slow twitch fibres above it. The shift in fibre type distribution was accompanied by an increase in stride frequency and decrease in stride length. These shifts in spatiotemporal characteristics appear to allow the muscles to operate at speeds close to those that achieve peak mechanical efficiency. Taken together, the results of this study predict that muscle fibre type distribution may influence both the energetics and biomechanics of gait, and that this influence is dependent upon the locomotion speed.

Vertical ground reaction force (vGRF) profiles during walking typically exhibit a double-peaked structure with a mid-stance trough, yet the mechanical conditions governing this morphology remain incompletely defined. In this study, we examined how the balance between push-off and collision impulses during the step-to-step transition influences the temporal and structural characteristics of the vGRF trajectory. Empirical relationships describing push-off and collision work were used to compute transition impulses across walking speeds ranging from 0.8 to 1.4 m{middle dot}s{square}1. A normalized Impulse Balance Index (IBI) was defined to quantify the relative dominance of push-off and collision impulses. The temporal position of the mid-stance trough was quantified using a Trough Deficit Index (TDI) derived from quadratic fits of the vGRF trajectory. Across walking speeds, push-off and collision variations produced step-to-step active work performance imbalance. Push-off and collision became approximately balanced near 1.2 m{middle dot}s{square}1, corresponding to the mechanically preferred walking speed. Deviations from this balanced condition were associated with systematic shifts in trough timing: the trough occurred 1.83% and 1.56% earlier in stance at 0.8 and 1.0 m{middle dot}s{square}1, respectively, and 1.31% later at 1.4 m{middle dot}s{square}1 relative to the reference speed. TDI exhibited a strong inverse relationship with impulse balance (IBI), indicating that vGRF morphology is tightly coupled to the mechanical balance of the step transition. A simplified pendular model further demonstrated that active torque, representing work, during single support shifts the quadratic vertex of the force trajectory by approximately 48.6-51.1% of stance, consistent with the observed trough timing variations. These results show that vertical GRF morphology reflects the imbalance between push-off and collision provides a simple signal of step-to-step transition mechanics, that may be used for rehabilitation and exoskeleton modulation.

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.

AA amyloidosis is a severe complication of chronic inflammatory diseases characterized by fibrillar protein deposition in the kidneys, leading to progressive organ failure. This study presents a mathematical model coupling SAA-HDL binding dynamics with renal amyloid aggregation kinetics to elucidate disease pathogenesis. Under normal conditions, Serum Amyloid A (SAA) circulates bound to high-density lipoprotein (HDL), which acts as a molecular chaperone preventing misfolding. However, during chronic inflammation, SAA production exceeds HDL binding capacity, resulting in free SAA that undergoes renal filtration. The model calculates free SAA concentration from reversible binding equilibrium and incorporates renal filtration, mesangial accumulation, and conversion to amyloid fibrils through primary nucleation and autocatalytic growth mechanisms. A central contribution of this work is quantifying accumulated nephrotoxicity arising from AA oligomers, which inflict cumulative cytotoxic damage to mesangial and tubular cells over time. Because oligomers are continuously generated during ongoing aggregation, their toxic burden integrates across the entire duration of the disease. Combined nephrotoxicity, encompassing both oligomer-mediated cellular injury and fibril-driven mechanical disruption of renal architecture, therefore reflects not merely the current disease state but the full inflammatory trajectory of the patient. This cumulative damage defines renal biological age, a measure of functional deterioration whose portion attributable to accumulated nephrotoxicity is irreversible. Renal biological age is also path-dependent: two patients with identical present-day SAA levels may carry different renal damage burdens depending on the duration, timing, and severity of their prior inflammatory episodes. Sensitivity analysis reveals that HDL concentration and SAA cleavage rate are critical determinants of amyloid burden.

Alzheimers disease (AD) is characterized by the accumulation of amyloid-{beta} (A{beta}), yet the specific link between plaque burden and cognitive decline remains a subject of intense investigation. This paper presents a mathematical model that simulates the coupled dynamics of A{beta} monomers, soluble oligomers, and fibrillar species in the brain tissue. By modifying existing moment equations to include a dedicated conservation equation for A{beta} monomers, the model explores how various microscopic processes, such as primary nucleation, surface-catalyzed secondary nucleation, fibril elongation, and fragmentation, contribute to macroscopic disease progression. Central to this study is the concept of "accumulated neurotoxicity" as a surrogate marker of biological age, defined as the time-integrated concentration of soluble A{beta} oligomers. Unlike plaque burden, accumulated neurotoxicity cannot be reversed, and the harm it causes depends critically on the sequence of events that produced it. Numerical results demonstrate that while plaque burden and neurotoxicity both increase over time, their relationship is non-linear and highly sensitive to the efficiency of protein degradation machinery. Specifically, impaired degradation leads to a rapid advancement of biological age relative to calendar age. The model further identifies oligomer dissociation and fibril fragmentation as potential protective mechanisms that can counterintuitively reduce neurotoxic burden by diverting monomers away from the soluble oligomer pool. These findings provide a quantitative framework for understanding why individuals with similar plaque burdens may experience vastly different cognitive outcomes, underscoring the importance of targeting soluble oligomers early in therapeutic interventions.

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.

Patellar osteochondral allograft (OCA) transplantation is widely used to treat large full-thickness cartilage defects, yet long-term failure and reoperation rates remain high. Although surface congruity and osseous integration are emphasized clinically, cartilage thickness and mechanical compatibility between donor and recipient are not considered. Our previous work suggests that cartilage thickness mismatch can amplify local deformation at the graft boundary, potentially compromising graft longevity. This study investigates how combined mismatches in cartilage thickness and mechanical properties influence the local strain environment at the patellar OCA interface. Simplified two-dimensional axisymmetric finite element models of patellar OCA repair were developed in ABAQUS. Donor-to-recipient cartilage thickness ratios ranging from 0.33 to 3.25 were evaluated together with donor-recipient Youngs modulus mismatches (2.5-7.0 MPa). Cartilage was modeled using homogeneous linear elastic and functionally graded material formulations to account for depth-dependent stiffness. A compressive pressure of 1.0 MPa was applied to represent patellofemoral joint loading, and peak compressive and shear strains were quantified at the graft boundary. Cartilage thickness mismatch produced localized high-strain regions (HSR) of compressive and shear strain at the donor-recipient interface that were absent in thickness-matched constructs. Strain amplification increased with both thickness and mechanical property mismatch. Compressive strain exhibited directional asymmetry, with donor-side-thicker configurations producing greater amplification than recipient-side-thicker configurations. Incorporating depth-dependent cartilage stiffness reduced peak strain magnitudes but did not eliminate mismatch-driven strain amplification. These findings demonstrate that cartilage thickness and mechanical disparity can create HSR at the patellar OCA graft boundary that may predispose grafts to impaired integration and long-term failure.

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

This study investigates closed kinematic chain biomechanics in cycling with a focus on knee joint loading. Data from 16 cyclists collected on a standardized ergometer were analyzed in OpenSim using inverse dynamics, static optimization, and joint reaction analysis. To keep the pipeline consistent across all subjects, the report summarizes right-knee outputs over a steady-state interval between 120 and 124 s. Peak knee joint moments ranged from 15.79 to 44.85 Nm (mean 30.49 {+/-} 7.66 Nm), while peak resultant knee reaction forces ranged from 1187.61 to 3309.04 N (mean 2317.19 {+/-} 728.19 N). Static optimization showed strong contributions from the rectus femoris and vastus lateralis during power production, with additional stabilization from the biceps femoris long head and gastrocnemius medialis. Mean peak muscle activation was highest for the rectus femoris (0.72 {+/-} 0.19), followed by the biceps femoris long head (0.66 {+/-} 0.20). Mean peak muscle force was highest for the vastus lateralis (1078.1 {+/-} 305.8 N) and rectus femoris (994.1 {+/-} 379.2 N). The results confirm substantial inter-subject variability in knee loading and support the use of personalized training or rehabilitation strategies when cycling is used for performance development or joint recovery.