Journal of Biomechanical Engineering

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.

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

Adolescent idiopathic scoliosis (AIS) alters spinal geometry and may influence the biomechanical response of the spine during functional postures. However, posture-dependent changes in spinal loading and paraspinal muscle forces in AIS remain poorly understood. This study investigated the effects of trunk posture on intervertebral loading and paraspinal muscle forces using a subject-specific musculoskeletal model of an adolescent with AIS. The spinal deformity was reconstructed from biplanar radiographs and incorporated into a full-body musculoskeletal model. Flexion, extension, lateral bending, and axial rotation were simulated at three incremental magnitudes, with motion distributed across the thoracolumbar spine. Intervertebral compressive and lateral forces around the curve apex and forces in the erector spinae (ES) and multifidus (MF) muscles were evaluated. Trunk flexion produced the greatest compressive loading, reaching 337 N at the curve apex and 372 N two levels below the apex at 30{degrees} flexion. Lateral bending produced pronounced direction-dependent loading: concave-side bending increased lateral forces, whereas convex-side bending increased compressive forces. Axial rotation produced similar but smaller direction-dependent changes. Paraspinal muscle forces were consistently asymmetric, with concave-side dominance of the ES and convex-side dominance of the MF. Flexion and convex-sided movements generally produced greater muscle imbalance, while increasing posture magnitude amplified spinal loading and muscle forces. These findings demonstrate that trunk posture, movement direction, and magnitude substantially influence the biomechanical environment of the scoliotic spine and should be considered when evaluating spinal mechanics in AIS.

Background/PurposeDiagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). MethodsPersonalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. ResultsAverage area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. ConclusionThis study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Background and objectiveMusculoskeletal joint disorders often show inconsistent relationships between structural degeneration and nociceptive pain. Temporomandibular joint (TMJ) disc displacement represents a clinically relevant model for investigating the structure-function-pain relationship. This study aimed to develop a multiscale computational framework integrating biomechanics, three-dimensional (3D) neural morphology, and electrophysiology to quantitatively link TMJ structural alterations, biomechanical loading, and peripheral nociceptive activation. MethodsStrain distributions in the TMJ disc and retrodiscal tissue during mouth opening and clenching were computed in ArtiSynth under varying degrees of displacement. Human TMJ 3D nerve architecture was reconstructed using porcine TMJ nerve mapping data as an anatomical reference, and ion channel dynamics were implemented in NEURON. Model coupling was achieved by mapping biomechanical strain fields onto nociceptor membranes to simulate mechanosensitive currents and action potential propagation to the trigeminal ganglion. ResultsAnterior DDwoR induced a severity dependent strain pattern in the TMJ disc and retrodiscal tissue, including posterior redistribution, increased strain magnitude, prolonged activation, and broader retrodiscal tissue involvement. Displacements of 4, 6, and 8 mm produced larger mechanosensitive currents, broader terminal depolarization, and higher trigeminal firing rates during mouth opening (6, 18, and 28 Hz) and clenching (8, 20, and 28 Hz), whereas 0- and 2-mm displacements produced negligible neural activation. ConclusionsThis study establishes a multiscale biomechanical-electrophysiological framework linking TMJ structural alterations to peripheral nociceptive activation. The framework quantitatively connects macroscale strain patterns with microscale neural activation, suggesting that anterior disc displacement may amplify peripheral nociceptive signaling by increasing the overlap between elevated strain and densely innervated retrodiscal tissue.

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

The human masseter muscle is one of the primary muscles responsible for mastication and mandibular movement; however, its intrinsic mechanical properties remain insufficiently characterized. In this experimental study, the nonlinear, viscoelastic, and history-dependent behaviour of the human masseter muscle was investigated using ex vivo uniaxial cyclic tensile tests. The masseter muscle samples prepared from fresh and formalin-preserved cadavers were tested under two loading protocols: a continuous stretch protocol with increasing stretch levels and a constant stretch protocol with repeated loading to a fixed maximum stretch. Tests were conducted at two strain rates, and their influences on the mechanical behaviour of the tissue were examined. The effect of formalin preservation was also investigated. The results showed that the stiffness of the tissue increases for formalin-preserved samples. Under cyclic loading, the features including energy dissipation, stress-softening, residual deformation, and cyclic conditioning progressively changed during the initial loading cycles and reached stabilization during the final cycle. These findings provide experimental evidence that the human masseter muscle exhibits nonlinear, viscoelastic, and history-dependent mechanical behaviour under cyclic tensile loading. The experimental data obtained in this study may be used for biomechanical modelling of the human masticatory system and the development of constitutive models for cranio-maxillofacial surgical simulation, prosthetic design, and facial soft-tissue biomechanics. Statement of significanceThe masseter muscle is one of the primary muscles of mastication. To address the current gap in craniofacial biomechanics that has largely focused on the mechanical characterization of the masseter muscle based on imaging techniques or monotonic loading, this study quantifies the nonlinear and viscoelastic mechanical response of masseter tissue under cyclic continuous and constant stretch loading, including strain-rate and preservation effects. The results show that the mechanical behaviour of the masseter muscle, including stiffness, hysteresis, stress-softening, and residual strain behaviour, is strongly influenced by strain-rate and formalin preservation. The experimental results provide mechanical data for constitutive modelling of the masticatory system with applications in cranio-maxillofacial surgical simulation, prosthetic design, and facial soft tissue modelling.

Acute subdural hematoma (ASDH) is a prevalent injury with high mortality and morbidity, often resulting from bridging vein (BV) disruption secondary to cortical relative motion. As a thin membrane enveloping the brain surface and anchoring BVs, the pia mater is hypothesized to play a critical mechanical role in cortical response and hence ASDH pathogenesis. Finite element (FE) head models are valuable tools to predict ASDH occurrence during impacts. However, the pia mater is often represented as an elastic material in existing FE head models, despite experimental evidence reporting its nonlinear mechanical behavior. In this study, both linear (Young's modulus of 11.5 MPa) and nonlinear (the stress-strain curve derived from pial tension tests) material models of the pia mater were implemented in one FE head model. The models were subjected to three experimental impact loadings, one of which was known to cause ASDH and two of which were not. Results demonstrated that, across all simulated impacts, the model with nonlinear pia mater properties predicted larger cortical displacements and BV responses than the linear model. For the impact with known ASDH occurrence, the predicted BV strain was 0.17 for the nonlinear model and 0.094 for the linear model, with only the former approaching the reported rupture strain range of the BV-superior sagittal sinus complex (0.29 {+/-} 0.13). These findings verified the mechanical importance of the pia mater in cortical responses and hence the prediction of ASDH, suggesting that conventional linear pia modeling might over-constrain cortical motion, leading to underestimation of BV strain and ASDH risk. The current study supported the adoption of experimentally derived nonlinear pia mater properties in FE head models to improve the reliability of ASDH prediction.

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 podocyte foot process network forms the final barrier of the kidneys glomerular filtration system. Under mechanical stress this network is prone to injury in which podocytes lose connectivity to their neighbors and begin the progression toward effacement, but what governs its mechanical resilience is unknown. We show that the network is built like a lap joint: two major processes coupled through interdigitating foot processes, a configuration that behaves as a classical shear lag system, with force concentrating at the joint ends and decaying over a characteristic transfer length set by geometry and stiffness. A discrete network model reproduces the continuum shear lag solution and identifies a hierarchy among governing parameters, with cytoskeletal stiffening of the major process amplifying foot process force more potently than basement membrane stiffness. Applying the model to morphometric data from puromycin aminonucleoside nephrosis, a model of human minimal change disease and early focal segmental glomerulosclerosis, reveals a mechanical positive feedback loop: force concentration drives foot process loss, which raises force on surviving segments and accelerates further loss. This nonlinear amplification implies a threshold beyond which failure becomes self-sustaining, analogous to the critical crack length in fracture mechanics.

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

The hindlimb ischaemia (HLI) mouse model is a widely used preclinical model of chronic limb-threatening ischaemia (CLTI). While CLTI involves complex interactions between impaired perfusion, inflammation and muscle wasting, the standard imaging approach, laser Doppler imaging (LDI), only assesses perfusion. MRI is used clinically to assess neural tracts, inflammation, and perfusion in the brain. We therefore evaluated whether a multimodal MRI approach could longitudinally monitor recovery in the HLI mouse model. Mice underwent MRI three days pre-HLI surgery, and on Days +3 and +7 post-surgery, with histology on Day +7. The MRI detected significant increases in muscle volume and inflammation after HLI surgery, with significant decreases in perfusion, vascular length, and muscle fibre integrity. Overall, MRI can monitor inflammation, muscle fibre integrity, and vascular recovery post-HLI and should be applied in future studies to identify mechanisms of therapeutic recovery in a sequential in vivo analysis without requiring animal sacrifice. O_FIG O_LINKSMALLFIG WIDTH=186 HEIGHT=200 SRC="FIGDIR/small/739107v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@79843dorg.highwire.dtl.DTLVardef@1d8e90borg.highwire.dtl.DTLVardef@1abd740org.highwire.dtl.DTLVardef@c0d296_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

Motion artifacts present a major challenge for intravital imaging of tissues undergoing physiological movement or mechanical loading. Blurring or artificial changes in image intensity due to shifting on the z-axis reduce data reproducibility and reliability. Existing post-acquisition approaches can partially compensate for motion, but they increase experimental complexity and are not well suited for use in mechanically loaded bone. Therefore, we developed a novel method for real-time correction of axial motion during mechanical loading of bone by synchronizing the movement of the objective to the actuator. Synchronization was achieved by linking the position of the actuator piezo motor to the objective piezo motor with a user refined reduction via potentiometer. Applying axial motion correction effectively removed artificial changes in fluorescent intensity in a static fluorescent marker up to 3000{varepsilon} in bone as measured by similarity and average intensity before and during loading. This improvement was reflected in the improved accuracy in capture of a dynamic fluorescent calcium indicator (GCaMP6f) in osteocytes. Our system provides a user-friendly, robust framework that can be easily adapted to other mechanically loaded tissues, improving data collection and expanding the utility of two-photon imaging across a variety of biological applications.

Many people which are affected by drop foot syndrome, have to face difficulty while walking which leads to pathological gait. This type of syndrome is treated by means of an external artificial stimulation known as functional electric stimulator (FES). In this paper we are designing an online feedback control system which optimize the strength of a FES given to paretic muscle which results in correction of pathological gait of the patient in a tolerable domain. Different phases of gait are identified using inertial measurement unit (IMU) as a feedback sensor mounted on the foot. Data is collected form 8 different healthy subjects and average of collected data is used as a reference template. Different trajectories of drop foot patients are simulated (due to unavailability of patients) and corrected according to the reference template.

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

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.

Physiological cross-sectional area (PCSA) is defined as the summed cross-sectional area of all muscle fibers contracting in parallel and at optimal length. PCSA is widely used, yet the conventional equations used to compute PCSA were developed from two-dimensional (2D) interpretations of muscle architecture and may not accurately represent muscle fiber cross-sections in the case of real three-dimensional (3D) geometries of muscles. Related measures of functional cross-sectional area (FCSA) and geometric cross-sectional area (GCSA) were also developed and interpreted with simplified 2D representations. Using realistic 3D muscle architectures derived from medical imaging, we sought to investigate whether conventional definitions of PCSA, FCSA, and GCSA represent the summed cross-sectional areas of all parallel muscle fibers, the fundamental definition of PCSA. We found that none of these measures consistently represented this definition. Thus, we introduce the physiological cross-sectional surface (PCSS), a curved surface within a muscle volume that is everywhere perpendicular to the local fiber direction. We estimated PCSS in 3D muscle surface meshes reconstructed from MRI data, using fiber orientations derived from Laplacian fiber reconstruction. PCSS-derived estimates were compared with PCSA, FCSA, and GCSA across six muscles representing five architectural classes. PCSS differed from all conventional measures, with the magnitude and direction of disagreement depending on muscle architecture. PCSS-to-PCSA ratios ranged from 0.771 to 1.399, while GCSA underestimated PCSS by up to a factor of 2.256 in bi- and multipennate muscles and overestimated it in muscles with more uniform fiber arrangements. PCSS demonstrated high geometric fidelity (perpendicularity>0.987) and robustness to fiber density across a tenfold range (coefficients of variation 0.39-3.95%). These findings indicate that conventional cross-sectional area measures do not consistently account for all fiber cross-sections in parallel within realistic 3D muscle geometries. PCSS provides a geometrically rigorous alternative that may improve estimation of functional muscle capacity from subject-specific imaging data.

ObjectiveThis study investigates knee joint biomechanics during lunges under varying tibial angles and external loads using musculoskeletal modeling and finite element analysis. The goal is to provide a biomechanical basis for understanding knee loading patterns and optimizing sports training and rehabilitation. MethodsTwenty-six healthy young men performed lunges under tibial inclination angles relative to the ground (60{degrees} and 90{degrees}) and two external load conditions (bodyweight and an additional 98 N external load). Kinematic and kinetic data were captured using motion capture and force plates. Musculoskeletal models were used to estimate joint moments, range of motion, and stiffness, with data analyzed using two-way repeated-measures ANOVA. Finite element analysis was performed at 90{degrees} tibial angle to evaluate tissue stress and displacement. ResultsThe joint moment at a 60{degrees} tibial angle was much higher than at a 90{degrees}. External load showed significant effects on knee stiffness, with lower rotational stiffness in the horizontal plane (P < 0.001) and lower coronal plane stiffness at 90{degrees} (P = 0.012) under the 98 N external-load condition, indicating reduced resistance to angular displacement in these planes. Under the 90{degrees} tibial-angle condition with external load, peak stress and displacement were concentrated in the posterior horn of the meniscus, with a maximum displacement of 3.12 mm. ConclusionThe anterior tilt of the tibia increased sagittal-plane knee loading, while external load mainly reduced joint stiffness in the coronal and horizontal planes. Under the 90{degrees} loaded condition, the concentration of stress and displacement in the posterior horn of the meniscus suggests a mechanically unfavorable loading pattern rather than direct evidence of injury risk. These findings may provide useful biomechanical information for load management during lunge-based training and rehabilitation.

Total knee replacement restores mobility in patients with advanced osteoarthritis, yet many individuals still experience limited ability to perform high-flexion tasks such as squatting. Current preoperative planning relies on static imaging and cannot predict how different implant alignment choices will affect postoperative dynamic function. This study developed a predictive simulation framework that uses bi-level inverse optimal control to link preoperative implant alignment directly to expected postoperative squat kinematics. Subject-specific musculoskeletal models were constructed for six total knee replacement patients using experimental squat data. Bi-level inverse optimal control was applied to identify both individualised and group-level cost functions. The individualised setting provided subject-specific accuracy, while the group-level setting derived a single group-level cost function as an initial step toward preoperative use without requiring postoperative motion data. The individualised setting reproduced experimental trajectories with low errors across all joints (mean apex difference 1.53{degrees}, root-mean-square error 5.15{degrees}, normalised root-mean-square error 11.15%, Pearson correlation 0.96). The group-level setting yielded higher but acceptable errors (mean apex difference 5.70{degrees}, root-mean-square error 6.75{degrees}, normalised root-mean-square error 17.53%, Pearson correlation 0.95) while preserving the general pattern and phasing of the motion. Squat depth emerged naturally from the optimisation rather than being prescribed. This framework may provide a basis for future quantitative tools to evaluate how implant alignment choices influence postoperative squat performance, potentially improving functional outcomes in total knee replacement. These results suggest that the proposed IOC framework can reproduce key features of post-TKR squat kinematics, but further out-of-sample validation is required before it can be used for preoperative prediction or translated into tools aimed at improving functional outcomes in total knee replacement.

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