Finite element modeling in biomechanics has been widely used for several decades to investigate the injury mechanisms and tolerance limits of various biological structures. The development of biomechanical models includes choosing an anthropometry, which generally leads to a 50th percentile male model. In that context, derived numerical injury metrics are dedicated to males, although conclusions of these studies are generalized to the whole population including female subjects. This study proposes to address the existing paucity concerning female blunt thoracic injuries and add to the understanding of thoracic injuries at a numerical level. Thus, a female finite element model of the thorax has been developed, named HUBxx, in the same way as the existing male geometry model is named HUByx. A 3-D reconstruction of the geometry of the various components of the model was based on literature data. Constitutive laws were implemented in the model for soft tissues and skeletal structures, and a numerical replication of existing experimental tests was carried out in the context of blunt ballistic impacts. The results show that the numerical responses of the HUBxx model are in good agreement with the female experimental corridors for both sternal and breast impacts, demonstrating its biofidelity and its ability to reproduce the mechanical response of the female thorax. In contrast, a male finite element model (SurHUByx FEM) scaled-down to female external anthropometric dimensions fails to reproduce the characteristics of the female experimental corridors. This study provides tools and data for specific investigations of female thoracic injuries, given the differences observed between males and females.
Anterior cruciate ligament (ACL) injuries remain a critical burden in professional football, resulting in prolonged absence and high economic costs. While physical demands have increased, traditional epidemiological surveillance relies on manual data extraction, which is resource-intensive and subjective. Large Language Models (LLMs) offer a potential solution for automated screening, but their reliability in sports medicine remains largely unexplored. This study aimed to: (1) update the epidemiology of ACL injuries in the Spanish First Division (LaLiga) over six consecutive seasons (2019/2020-2024/2025) and (2) validate the accuracy of an AI-assisted screening approach using ChatGPT compared to manual data extraction. A retrospective observational study was conducted using publicly available sources. Manual screening by external investigators (gold standard) was compared against an AI-assisted workflow based on a structured prompt-engineering framework. Analyses focused on injury frequency, mechanism, return-to-play (RTP) time, recurrence rates, and AI case-detection sensitivity. A total of 56 ACL injuries were identified in 52 players. Findings revealed a predominance in defenders (41.1%), a high prevalence of non-contact mechanisms (67.9%), and a median RTP of 280 days. Notably, the match-to-training injury ratio was 1.8-1, a significantly narrower gap than historical records. The recurrence rate was 17.9%, with 70% of cases occurring in the contralateral knee. Methodologically, the AI model exhibited a learning curve: 44.4% sensitivity in the initial refinement season (2019/2020), which progressed to a sustained 100% sensitivity across the following five seasons (2020-2025). ACL injury incidence in LaLiga remains stable, but the narrowing match-to-training ratio suggests that modern training intensities are reaching competitive levels of biomechanical stress. LLMs, once optimized through prompt engineering, provide a highly reliable and scalable tool for near real-time epidemiological surveillance, representing a methodological milestone in sports medicine research.
Osteoporosis is a prevalent condition with substantial health and economic implications. Although physical activity is associated with bone health, the specific temporal activity patterns related to bone mineral status remain insufficiently understood. This proof-of-concept study evaluated whether convolutional neural networks (CNNs) applied to Gramian Angular Field (GAF) representations of MIMS-derived wrist accelerometry data could predict femoral neck T-score and classify normal bone status versus osteopenia/osteoporosis. Data from 2504 participants in NHANES 2013-2014 were analyzed, including DXA-derived femoral neck measures, age, sex, body mass, and 7 days of wrist-worn accelerometry. Minute-level MIMS data were averaged into 10-min intervals and transformed into seven-channel GAF images. VGG-like and ResNet-like CNNs were trained from scratch using an internal validation set for early stopping and evaluated on a held-out test set. In classification, the VGG model achieved the best CNN performance, with test accuracy of 71.3%, sensitivity of 61.5%, specificity of 77.5%, F1-score of 0.625, and ROC-AUC of 0.780. In regression, the VGG model also performed best among CNNs, with test R2 of 0.305, MSE of 1.095, RMSE of 1.047, and MAE of 0.789. Demographic-only baseline models showed comparable or slightly better performance, indicating that age, sex, and body mass carried substantial predictive information. These findings support the feasibility of GAF-CNN modeling of accelerometry-derived activity patterns for bone-health research, while emphasizing the exploratory nature of the approach and the need for further validation.
To evaluate the accuracy and reliability of the Magene® heart rate (HR) monitor compared to the Polar® H7 (validated device) during rest, maximal graded exercise test, and active recovery. A within-group study was conducted with 68 healthy adults (21 women). Participants completed two laboratory sessions using both monitors in random order. HR values were collected at rest, during the Ellestad maximal treadmill stress testing protocol, and in active recovery. Relative reliability was assessed using the two-way random effects intraclass correlation coefficient for agreement (ICC(2,1)). Absolute reliability was assessed using typical error (TE, in bpm and %), coefficient of variation (CV, %), and minimal detectable change (MDC, in bpm and %). Agreement was examined using Wilcoxon signed-rank tests, Bland-Altman plots, and Pearson's r or Spearman's rho, as appropriate. No significant HR differences were found between devices (p > 0.05). Mean biases, calculated as Polar - Magene, were 1.421 bpm at rest, 0.659 bpm during exercise, 0.103 bpm at maximal effort, and 0.569 bpm during active recovery. ICC(2,1) values ranged from 0.656 (rest) to 0.881 (maximal effort). TE ranged from 3.784 bpm (2.0%) to 6.470 bpm (8.7%), and MDC from 10.5 bpm (5.6%) to 17.9 bpm (24.2%), depending on the condition. Correlations ranged from 0.682 to 0.881. The Magene® shows acceptable agreement with Polar® H7 during exercise but may lack precision for resting HR monitoring in clinical settings.
Accurate registration of cone-beam computed tomography (CBCT) and intraoral scanner (IOS) dental models is essential for diagnosis and preoperative planning. However, reliable alignment remains challenging because IOS data primarily represent the crown surface whereas CBCT provides complete tooth morphology with lower surface fidelity, resulting in substantial cross-modal discrepancy and limited geometric overlap. This study proposes a training-free spectral-topological feature-driven registration framework (STFR) for robust CBCT-IOS alignment under low-overlap conditions. STFR resamples the CBCT point cloud using Divergence Index and Improved Euclidean Clustering Rules (DI-IECR), then establishes reliable coarse correspondences via curvature-topology features and geometric-topological confidence domains. During fine registration, Laplacian spectral features are integrated with geometric-topological descriptors in an adapted iterative closest point framework to improve global structural consistency and reduce convergence to anatomically incorrect local optima. A neighborhood-curvature-based strategy then improves crown-root continuity. The framework was evaluated on 14 paired CBCT-IOS tooth models covering different morphologies and overlap conditions, using surface-distance metrics, spatial overlap ratio, and landmark-based target registration error (TRE) at the case level. STFR achieved a mean RMSE of 0.475 mm and a mean landmark TRE of 0.621 mm, outperforming five representative baselines (ICP, NDT, CPD, FPFH-RANSAC, and a fine-tuned DCP), with RMSE reductions of 29%-59% and significantly lower error in every paired comparison (all p < 0.01). STFR thus provides an interpretable, robust approach for low-overlap CBCT-IOS registration, although prospective clinical validation remains necessary before routine use.
Subject-specific finite element analysis (FEA) can estimate tissue-level stress, strain, contact pressure, and load transfer in the knee, but building and solving a new model for each anatomy, activity, or treatment scenario remains time-consuming. This narrative review examines how knee biomechanical simulation has progressed from conventional FEA and efficient physics-based approximations to contact emulators, hybrid FE-machine learning workflows, temporal models, geometric deep learning, graph neural networks, operator-learning methods, and physics-guided surrogates. These methods can accelerate selected predictions, although their accuracy is usually established only for the anatomies, loads, and FE formulations represented during development. Pre-training across related FE model families may allow a model to be adapted to new geometries, outputs, or tasks with fewer additional simulations. However, this has not yet been demonstrated for nonlinear, multi-tissue knee mechanics. Any such model would still inherit the constitutive laws, contact definitions, boundary conditions, and calibration choices used to generate its training data. A credible system would therefore need traceable simulation data, explicit information about the underlying FE formulation, reliable uncertainty estimates, mechanical checks, and confirmation with high-fidelity FEA when a case falls outside the supported range. The immediate goal is a model that can be reused across related FE problems without being retrained from scratch, while unsupported or clinically consequential cases remain subject to full FE analysis.
This study evaluates the biomechanical influence of surface texturing and hybrid coatings on stress distribution and marginal bone loss (MBL) in dental implants under varying bone loss conditions and axial loading. A parametric three-dimensional finite element model (FEM) of the human mandible was developed, consisting of cortical and cancellous bone layers. Five implant surface textures-Dome, Straight, U-Shape, X-Shape, and V-Shape-were considered, along with four hybrid coatings: hydroxyapatite (HA), HA with 3% tantalum pentoxide (HA3TO), HA with 3% strontium (HA3Sr), and HA with a combination of 1.5% tantalum pentoxide and 1.5% strontium (HA1.5TO1.5Sr). The implants were subjected to static axial loads (100, 150, 200, and 250 N). The V-Shape implant with HA1.5TO1.5SR exhibited the highest implant stress (97.13 MPa at 250 N), exceeding the 35 MPa cortical bone yield threshold, indicating an increased risk of mechanical overload and resorption. Dome-Shape and U-Shape textures demonstrated improved stress distribution, reducing peak stresses and enhancing stability. Hybrid coatings lowered implant stress by 19.65%, mitigating bone remodeling risks. Bone loss amplified stress concentrations, with higher micromotion risks observed in V-Shape and Straight-Shape textures. Surface texturing and hybrid coatings significantly influence peri-implant stress and stability. Based on our findings, Dome-Shape and U-Shape textures, combined with hybrid coatings, offer biomechanical advantages for implant longevity. These findings support the clinical preference for coated, curved-surface implants, particularly in patients with compromised bone quality.
Pedicle screw fixation is widely utilized for lumbar stabilization, but deviation from the planned pedicle corridor may result in cortical breach and neurovascular injury. Individualized 3D-printed guides may help transfer preoperative planning to screw insertion. However, their guiding performance and measurement reliability still require quantitative validation in controlled L4 models. This in vitro comparative study evaluated the accuracy, safety, and measurement reliability of guide-assisted L4 pedicle screw placement. Five L4 vertebrae were selected from a public spine CT dataset to design and print individualized guides and corresponding bone models. Bilateral screw placement was performed in both the guide-assisted and free-hand groups. Postoperative CT scans were registered to preoperative planning models to assess entry point deviation, 3D angular error, axial and sagittal plane angular errors, and Gertzbein-Robbins (GR) grading. Registration quality and the reliability of repeated measurements were also evaluated. The guide-assisted group showed lower positional and angular errors than the free-hand group across all quantitative metrics, and this pattern was consistent across the five paired specimens. A higher strict Grade A rate was observed in the guide-assisted group, although both groups achieved a 100% clinically acceptable rate (Grade A + B). These findings suggest that individualized 3D-printed guides may improve the reproduction of planned L4 pedicle screw trajectories in printed bone models. Because only five independent L4 specimens were included and PLA models cannot reproduce bone density or tactile feedback needed for free-hand placement, the between-group comparison should be interpreted as exploratory in vitro geometric evidence. The workflow provides a quantitative framework for the in vitro evaluation of guide-assisted screw placement.
This study quantified the biomechanical consequences of incremental far-cortex over-penetration at terminal locking screw positions in a locking compression plate construct using a validated three-dimensional finite element model of a femoral diaphyseal fracture (AO/OTA 32-A3). Four terminal screw engagement configurations were evaluated: unicortical fixation, fully contained bicortical fixation, 1-thread over-penetration (0.6 mm), and 3-thread over-penetration (1.8 mm). Axial compression (1000 N), torsional loading (±10 Nm), and cyclic dynamic loading (100-1000 N, 1 × 106 cycles) simulating the 8-week early postoperative period were applied. Model validation against published experimental data demonstrated agreement within 4% for all key biomechanical parameters. Fully contained bicortical fixation was the only configuration to remain below the predicted fatigue threshold, demonstrating the lowest peak von Mises stress (74.8 MPa) and a Miner's cumulative damage index of D = 0.291. Unicortical fixation (D = 1.103), 1-thread over-penetration (D = 1.362), and 3-thread over-penetration (D = 2.968) all exceeded the failure threshold (D ≥ 1.0). Under the loading conditions and material assumptions of the present finite element model, unicortical terminal fixation alone did not sustain simulated cyclic physiological loading during the pre-callus phase without exceeding the predicted fatigue damage threshold. These findings provide preliminary, model-dependent computational evidence suggesting that fully contained bicortical fixation may offer greater fatigue resistance, and that the biomechanical basis of current terminal screw placement guidelines warrants further experimental investigation.
While substantial evidence supports the associations between physical activity and bone health, the present study aims to advance the field by applying a novel, data-driven approach through machine learning techniques. The primary objective was to identify significant predictors of femoral neck bone mineral density (BMD) and to develop an interpretable modeling framework for estimating individualized physical activity and strength targets associated with BMD in women. This study analyzed data from 1205 female participants in the National Health and Nutrition Examination Survey (NHANES) 2013-2014 cohort to examine the relationship between femoral neck BMD and key variables, including age, body mass, grip strength, and physical activity. Machine learning regression models, such as Multiple Linear Regression (MLR) and Random Forest Regression (RFR), were employed to develop an interpretable modeling framework. Shapley Additive exPlanations (SHAP) were used to support interpretation of the nonlinear RFR model and to visualize each predictor's contribution to BMD estimates. The MLR model estimated femoral neck BMD with a test R2 of 0.375, RMSE of 0.105 g/cm2, and MAE of 0.082 g/cm2. The RFR model resulted in a test R2 of 0.309, RMSE of 0.111 g/cm2, and MAE of 0.086 g/cm2. This study illustrates the potential of interpretable machine learning approaches to support individualized, data-driven assessment of physical activity, strength, and BMD. The numerical model developed in this work provides association-based estimates of physical activity and strength targets according to individual characteristics, highlighting its potential for hypothesis generation and future studies on bone health promotion and osteoporosis prevention. Prospective and interventional validation is required before clinical recommendations can be made.
Accurate characterization of impingement-free range of motion (ROM) following reverse total shoulder arthroplasty (RTSA) is essential for implant design and surgical planning. Existing computational methods often rely on mesh-overlap or clearance-based estimations, which can limit geometric accuracy. This study introduces and validates a CAD-to-Simulink computational pipeline that predicts impingement-free ROM using a high-resolution, point-cloud-based collision detection approach. Computer models of shoulder bony anatomy and generic RTSA implant components were assembled in SolidWorks and exported to MATLAB. Automated scripts executed humeral motion sweeps across planes of elevation. Point clouds were seeded on the acromion, coracoid, and scapular neck and used to detect impingement when penetration depth ≥1 mm. Three neck-shaft angles (155°, 145°, 135°) were evaluated. The 155° configuration was validated experimentally using additive manufactured monoblocs mounted on a custom-made frame under a 15 N compressive load. Sensitivity analyses examined the influence of point cloud density and spacing on impingement detection accuracy. Computational results demonstrated that decreasing the neck-shaft angle from 155° to 135° increased impingement-free ROM across evaluated planes, with improvements of up to 23°. This gain was attributed to delayed inferior impingement during adduction. Experimental validation closely aligned with predictions, showing impingement at 61.4° (superior) and 20.9° (inferior), compared to predicted values of 61.4° and 21.0°. Sensitivity analyses highlighted the importance of point cloud placement along key scapular regions. The CAD-to-Simulink framework provides a validated, reproducible, Finite Element Method independent prediction of impingement-free ROM, providing a kinematically accurate tool to optimize implant geometry and surgical positioning in RTSA.
Lumbar disc degeneration is a major contributor to low back pain and disability. Lumbar total disc replacement (LTDR) has been introduced as a motion-preserving alternative to spinal fusion, and its biomechanical performance is strongly influenced by prosthesis geometry. This study investigated the effects of two key geometric parameters, curvature radius and radial clearance, on the biomechanical behavior of a ball-and-socket LTDR. Three-dimensional finite element models of the L3-L5 lumbar spine were developed, incorporating prostheses with curvature radii ranging from 7 to 10 mm and radial clearances from 0.1 to 0.3 mm. Segmental range of motion (ROM) and adjacent intervertebral disc biomechanics were evaluated under physiological loading conditions. Increasing curvature radius substantially reduced flexion and lateral bending ROM while producing radius-dependent effects on axial rotation. It also led to elevated annulus fibrosus (AF) von Mises stress across all motion modes. Nucleus pulposus (NP) von Mises stress and strain increased modestly during flexion and lateral bending but decreased during axial rotation. Total deformation of both NP and AF decreased progressively. In contrast, increasing radial clearance moderately enhanced flexion and lateral bending ROM while producing negligible changes in stress and slightly modulating strain and total deformation. These results indicate that prosthesis geometry plays a key role in modulating spinal kinematics and adjacent segment loading following lumbar total disc replacement. Curvature radius exerts a stronger influence than radial clearance, which mainly serves as a secondary parameter for fine-tuning segmental motion.
Yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) implants have been proposed as an alternative to titanium (Ti) implants because of their aesthetic and mechanical properties. This in silico study compared the elastic strain distribution of Y-TZP and Ti dental implants under axial and oblique loads using a previously validated finite element model. Two finite element models, with identical geometry, boundary conditions, loading configuration, prosthetic crown, abutment, and screws, and differing only in implant body material, were constructed for biomechanical evaluation. Each model was subjected to a 300 N load applied axially and at 30°, and equivalent, maximum principal, and minimum principal elastic strains were evaluated in the peri-implant bone, implant body, and abutment screw. Under axial loading, peri-implant bone strain patterns were similar between Ti and Y-TZP, with peak equivalent elastic strain values of approximately 5 millistrain. Under oblique loading, the Y-TZP implant showed lower equivalent elastic strain in the implant body than Ti, with values of approximately 1.3 and 3.0 millistrain, respectively. The abutment screw also showed smaller regions of elevated strain in the Y-TZP model. Within the assumptions of this static linear elastic model, implant material stiffness influenced component-level elastic strain distribution, particularly under oblique loading. These numerical findings should not be interpreted as evidence of improved clinical performance, reduced bone resorption, or superior long-term stability. Further studies should include cyclic loading, fracture and fatigue analyses, interface stability, and patient-specific bone conditions.
Biodegradable implants are crucial for orthopaedic applications, as they eliminate the necessity for a secondary surgical procedure. This investigation adopts composite methodology by reinforcing ZK61 with hydroxyapatite (HA) and eggshell (ES) to fabricate Mg-based metal matrix composite using friction stir processing (FSP). The impact of introducing 10 wt% HA as a bioactive ceramic and 2.5 wt% ES as a green, low-cost reinforcement via FSP on mechanical performance, in vitro corrosion behaviour, and biocompatibility is reported. The microhardness and ultimate compressive strength of the ZK61-10HA-2.5ES composite (74.6 HV, 356 MPa) showed a ∼24% increase in microhardness and a ∼28% increase in UCS compared to ZK61 alloy (60.1 HV, 278 MPa). The corrosion rate of ZK61-10HA-2.5ES composite was assessed using electrochemical measurements and found a lower corrosion rate (1.05 mm/year) compared to ZK61 alloy (3.63 mm/year), which aligned with the results obtained from immersion. After 1 and 3 days of incubation, the MTT assay revealed that the ZK61-10HA-2.5ES composite exhibited grade 0 non-cytotoxic response to MG-63 cells, indicating its promising potential for orthopaedic applications.
Despite advances in robot-assisted rehabilitation, existing control strategies often lack real-time personalization to account for subject-specific physiological capabilities, limiting the effectiveness of assist-as-needed interventions. Moreover, there is a further need to account for the patient's physiological functional capacity (PFC) in the design of such assistive technology. Here, an attempt has been made to create an Assist-as-Needed (AAN) Controller that works in collaboration with a Smart Avatar. The Smart Avatar mimics the patient's capabilities by learning in real-time and informing the controller. To efficiently predict the subject's level of involvement, a Reinforcement Learning (RL) based Inverse Dynamics model has been designed. Additionally, the controller for the Avatar has been appended with an Energy Map for modifying the reference trajectories and making the system energy efficient. The human torque estimated by the Smart Avatar assists the Assist-as-Needed (AAN) controller in providing the optimum robot torque to guide the subject's wrist along the modified trajectories. The developed algorithm was validated on five healthy participants. The system achieved trajectory tracking errors in the range of 0.01-0.04 rad across wrist motions. Subject- and axis-dependent differences in interaction torque were examined using descriptive statistics and torque profiles, supporting the feasibility of adaptive assistance under the tested conditions.
The drilling force, temperature and surface roughness during bone drilling critically determine bone tissue trauma and surgical performance. To address the difficulty of coordinating multiple conflicting indicators through single-objective optimization, this study conducts multi-objective parameters optimization of low-frequency vibration-assisted bone drilling. Central Composite Design (CCD) is employed to establish quadratic regression models for drilling force, drilling temperature and surface roughness with spindle speed, feed rate, voltage (amplitude), and vibration frequency as input variables. Model validity is confirmed via analysis of variance and residual analysis. Taking the simultaneous minimization of the three machining responses as the objective, the genetic algorithm (NSGA-II) is applied to acquire Pareto optimal solutions. The optimal parameter combination is determined as: spindle speed 2000 rpm, feed rate 0.18 mm/s, voltage 200 V, frequency 170 Hz. Corresponding predicted values are 35 N (drilling force), 61°C (temperature) and 1.05 μm (surface roughness). Validation experiments show that the prediction error of each indicator is within 15%, verifying the reliability of the proposed optimization method. Furthermore, when combined with drill bit pre-cooling, the drilling temperature decreased to 50.3°C, while drilling force and surface roughness remain at low levels. This study provides a low-damage, high-quality process parameter optimization method for bone drilling surgery, offering significant clinical application value.
Adaptive remodeling of trabecular bone surrounding orthopedic implants plays a critical role in maintaining prosthesis stability and preventing aseptic loosening caused by stress shielding and osteolysis. This study presents a numerical model approach combining Finite Element Analysis and Weinans' bone remodeling model to evaluate the initial density adaptation response of peri-prosthetic femoral trabecular bone under the critical loading condition occurring during the toe-off phase of normal gait. A high-fidelity model incorporating cortical and trabecular bone regions was developed to calculate stress distribution and strain energy density within the peri-prosthetic region. The mechanical stimulus field obtained from the Finite Element Model was subsequently used to evaluate localized remodeling activation and density-related adaptation tendencies. The results showed that localized regions near the stem tip exceeded the ultimate compressive strength of trabecular bone (7.18 MPa), suggesting potential osteolytic activity confined to a limited area. In contrast, stress in the cortical bone remained below reported failure thresholds. Regions subjected to elevated mechanical stimulus exhibited increased trabecular bone density and corresponding improvements in predicted mechanical strength, reaching values close to 10 MPa. The simulations also demonstrated that density adaptation reduced local deformation, thereby promoting mechanical stabilization at the prosthesis-bone interface. These findings support the trabecular bone's capacity to adapt mechanically to implant-induced loading conditions and demonstrate the usefulness of the proposed framework for evaluating peri-prosthetic remodeling behavior and implant load-transfer mechanisms.
Although fully cortical-threaded screws are introduced as a design adaptation tailored to the cortical-dominant load path of modified cortical bone trajectory (MCBT) fixation, the biomechanical consequences of this design across the screw-bone interface and fusion construct remain insufficiently defined. Therefore, this study compared fully cortical-threaded MCBT screws with clinically used dual-threaded screws through an integrated experimental and finite element (FE) framework, spanning in vitro ovine vertebral biomechanical test and specimen-specific L4 vertebra and L1-S1 fusion models. At the screw level, compared with the control group, fully cortical-threaded MCBT screws increased maximum pull-out strength by 111.2% and multidirectional stiffness by 75%-89% in L4 vertebral FE analysis, with biomechanical testing showing corresponding increases of 39.1% in insertion torque and 41.9% in maximum pull-out strength. At the fusion-construct level, fully cortical-threaded MCBT fixation limited fused-segment motion and decreased stress across the cage, instrumentation, and vertebral bone, indicating a more coordinated load-transfer pattern rather than a simple increase in interface-level strength. These effects were consistent across fusion strategies, but procedure-specific mechanics remained evident, with PLIF producing more symmetric load sharing and TLIF retaining intrinsic asymmetry owing to unilateral facet joint resection. Overall, the fully cortical-threaded screw design for MCBT promoted continuous and stable bone-screw load transfer, translating interface-level gains into coordinated load distribution, greater fusion-construct stability, and lower deformation-driven stress concentration. These findings indicate that aligning screw architecture with the cortical-dominant load path is a mechanically rational design strategy within MCBT fixation, particularly in biomechanically demanding settings.
Individuals with transfemoral amputation (TFA) often experience gait adaptations when walking under environmental and cognitive challenges. This study examined the influence of passive prosthetic knee mechanisms on self-reported mobility and gait biomechanics during single-task and dual-task walking over an uneven compliant surface. Participants were grouped as able-bodied individuals (AB; n = 15), individuals with TFA using polycentric knees (TFAP; n = 6), and individuals with TFA using fluid-controlled knees (TFAFC; pneumatic/hydraulic; n = 5). The dual-task condition involved walking while performing an auditory Stroop task. Outcome measures included self-report questionnaires, joint kinematics, joint kinetics, and ground reaction forces. Biomechanical variables were analysed using linear mixed-effects models to examine group, task, and group × task interaction effects. Self-reported outcomes indicated reduced functional mobility in individuals with TFA compared with AB. Biomechanical findings showed altered gait patterns in both TFA groups compared with AB, particularly reduced knee flexion and ankle plantarflexion. For kinetic outcomes, significant group effects were observed for selected joint moment and power variables, although Bonferroni-adjusted pairwise differences were identified only for maximum stance hip extension moment (HM1), with lower values in TFAFC compared with AB. A significant task effect was observed for maximum stance ankle dorsiflexion (AA2) during dual-task walking across all groups. A significant group × task interaction was found for maximum hip eccentric power during late stance (HP2), with increased hip power during dual-task walking in TFAP. Overall, passive prosthetic knee mechanisms showed limited differences, although task-specific adaptations were evident under combined uneven surface and cognitive demands.
Femoral neck fractures necessitate effective fracture fixation techniques to ensure optimal outcomes. The study evaluates the bone remodelling around Dynamic Hip Screw with anti-rotational screw (DHS + AR) and Femoral Neck System (FNS) used for treating femoral neck fractures of Pauwels types I, II and III. Femur models were developed using CT imaging data. Fractures were modelled assuming smooth fracture surfaces. Isotropic heterogeneous CT-grey value-based bone material properties were employed in the bone model, and Ti-alloy was incorporated as implant material. Bone remodelling algorithm based on strain energy density was employed to predict the implants' effects under two loading conditions: normal walking and stair climbing. The density change (Δρ) was iteratively updated until convergence occurred (Δρ < 0.005 g/cm3 between two consecutive iterations). The investigation compared post-operative (PO) conditions with equilibrium (AE) states. Results revealed that strain shielding was approximately 27% lower in FNS-implanted models than DHS + AR models, indicating better biomechanical performance under adopted modelling assumptions. From PO to AE, axial deformation and rotational stability of the femoral head were reduced more in FNS models, with reductions of up to 13.57% and 83%, respectively. Micromotion was (below 100 μm) in all models except for FNS in Pauwels III fractures under PO conditions. However, it was reduced below 100 μm at AE. Lower bone loss was observed in FNS-implanted models while DHS + AR implanted models exhibited higher resorption. These findings suggest that FNS implants are predicted to provide better stability and favourable bone remodelling outcomes compared to DHS + AR screws.
