Annals of Biomedical Engineering最新文献

Purpose: Osgood-Schlatter disease (OSD) is linked to quadriceps traction, yet quantitative force data for adolescent females in high-risk sports is scarce. This study aimed to biomechanically compare quadriceps muscle forces during key motions in female adolescent soccer and basketball players. The objective was to determine which sport's characteristic movements impose greater mechanical loads on the tibial tuberosity, thereby representing a higher potential risk for OSD development.

Methods: Sixteen adolescent females were divided into basketball (n = 8) and soccer (n = 8) groups, each performing three sport-specific motions. Kinematic, kinetic, and electromyography (EMG) data were captured using a 10-camera motion capture system, force plates, and wireless sensors. A musculoskeletal model in OpenSim was employed to estimate and compare peak and accumulated quadriceps muscle forces between the groups and their respective motions.

Results: In basketball, the single-leg jump yielded the highest peak and impulses. For soccer, the side-step cut produced the greatest peak force, and turning yielded the highest accumulated force. Crucially, overall peak quadriceps muscle forces were significantly higher in the soccer group compared to the basketball group. The rectus femoris generated higher peak forces in basketball, while the vasti muscles demonstrated higher peak forces in soccer.

Conclusion: Single-leg jumping in basketball and cutting/turning in soccer impose the most significant traction on the tibial tuberosity. Due to lower overall peak forces, basketball may pose a reduced OSD risk for adolescent females compared to soccer. Differential recruitment of the rectus femoris versus vastus muscles between sports is a key consideration for injury prevention and athlete guidance.

Clinical ultrasound systems typically assume a speed of sound of 1540 m/s for reconstructing images. However, the speed of sound varies between tissues, and this assumption creates distortions and errors in representing the thickness of tissue structures. Quasi-simultaneous (i.e., taken subsequently without movement of the specimen) computed tomography (CT) and ultrasound scans at various speeds of sound (1480, 1540, 1600, and 1660 m/s) were taken at the calcaneus and the second metatarsal head for seven cadaveric feet. A speed of sound of 1600 m/s demonstrated the lowest bias with a mean signed error of - 0.06 mm for the calcaneus and - 0.19 mm for the second metatarsal head, while for 1540 m/s, the calcaneus had a bias of - 0.44 mm, and the second metatarsal head had a bias of - 0.67 mm. The CT-derived distance was used to estimate the ideal speed of sound by calculating the slope of the change in distance with the change in speed of sound and was found to be 1616.6 ± 76.5 m/s for the calcaneus and 1623.9 ± 63.6 m/s for the second metatarsal head. However, limitations in the use of cadavers, CT as the reference standard, and methodological assumptions in analysis limit the extent to which these findings can be generalized to in vivo conditions. This work suggests that using an assumed speed of sound greater than 1540 m/s may be more appropriate for imaging plantar soft tissue; however, additional in vivo studies are needed to corroborate this finding.

Purpose: In the context of a multi-speaker "cocktail party" scenario where listeners selectively focus on specific speakers, human auditory attention networks have shown a strong correlation with Electroencephalography (EEG) measurements. However, current EEG-based auditory attention detection (AAD) methods, mostly using artificial neural networks (ANN), face limitations on edge computing platforms due to extended decision windows, high power consumption, and substantial memory requirements linked to multiple EEG channels.

Methods: This paper introduces a novel hybrid convolutional-spiking neural network (CNN-SNN) architecture, inspired by the auditory cortex, combining EEG data with multi-speaker speech envelopes, enabling effective auditory attention decoding within 0.5-s timeframes. Our approach reduces EEG channels, minimizes computational operations, and quantizes weight parameters while maintaining high accuracy.

Results: We validate this approach on our dataset and compare it to state-of-the-art methods on a publicly available dataset. CNN-SNN demonstrates superior performance, achieving up to 10% increase in decoding accuracy, while using 87.5% fewer EEG channels and 75% smaller bit precision for weight quantization compared to existing methods.

Conclusion: These results offer promise for edge computing applications, such as hearing aids, emphasizing short decision windows, minimal EEG channels, and strict power and memory constraints.

Purpose: Plantar skin is a highly specialised tissue which protects the foot from injuries and adapts to external stresses. However, it can be subjected to diabetic plantar ulcers, which are among the most difficult and costly wounds to treat. Although this is a crucial topic, few studies have focused on the mechanical properties of foot skin and how disease alters them. In this context, this work aims to fully describe the mechanical behavior of plantar skin through experiments and constitutive analysis.

Methods: Different experimental tests (failure tensile tests, unconfined compression at different strain rates, stress relaxation tests) were conducted on human plantar skin samples cut along the posterior-anterior (PA), lateral-medial (LM), and cranial-caudal (CC) directions. Then, experimental results were used to identify, through an inverse analysis, the parameters of the anisotropic visco-hyperelastic constitutive model adopted to describe the skin's mechanical response.

Results: Plantar skin's non-linear, anisotropic, and time-dependent behavior, with differences between the anterior and posterior foot's regions. In addition, the constitutive model adopted is able to capture the mechanical behavior of the plantar skin Failure tensile tests showed that PA directions exhibited higher elastic modulus than LM directions in both posterior (22.05 vs 12.91 MPa) and anterior (17.39 vs 12.82 MPa) regions, while the unconfined compression tests revealed that compressive elastic moduli in the posterior region increased with increasing strain rates.

Conclusion: The proposed model provides new insights into the mechanics of plantar skin, being a valuable tool for applications such as diagnosing skin diseases and developing skin substitutes.

Purpose: Despite substantial advances in computational cardiovascular modeling, simplifying assumptions often overlook key compensatory mechanisms. To better capture physiological responses under pathological conditions, we incorporate two essential compensatory mechanisms-collateral circulation and arteriolar vasodilation-into a multiscale blood perfusion simulation framework.

Methods: Collateral vessels are stochastically generated to supply ischemic regions, with model parameters systematically varied to control vessel density, caliber, and spatial distribution. Arteriolar vasodilation is modeled to represent the combined response of vascular smooth muscle cells to local metabolic and hemodynamic stimuli. To assess their impact on perfusion, artificial stenoses are introduced into both an idealized geometry and a subject-specific cerebrovascular model.

Results: Simulation results across varying collateral configurations and vasodilation capacities demonstrate that perfusion enhancement from sub-resolution collateral vessels alone is limited and highly dependent on vessel caliber and density. In contrast, combining collateral flow with vasodilation produces a more pronounced improvement in tissue perfusion. These findings suggest that arteriolar vasodilation serves as the primary mechanism for ischemic compensation, with collateral circulation providing secondary support which is consistent with clinical observations.

Conclusion: The proposed methods enable multiscale blood flow simulations while accounting for the main autoregulatory mechanisms. The models have been verified using literature data and are shown to provide accurate results.

Purpose: To study the relationship between soccer heading and the risk of mild traumatic brain injury (mTBI), we previously developed an instrumented headband and data-processing scheme to measure the angular head kinematics of soccer headers. Laboratory evaluation of the headband on an anthropomorphic test device showed good agreement with a reference sensor for soccer ball impacts to the front of the head. In this study, we evaluate the headband in measuring the full head kinematics of soccer headers in the field.

Methods: The headband was evaluated under typical soccer heading scenarios (throw-ins, goal-kicks, and corner-kicks) on a human subject. The measured time history and peak kinematics from the headband were compared with those from an instrumented mouthpiece, which is a widely accepted method for measuring head kinematics in the field.

Results: The time-history agreement (CORA scores) between the headband and the mouthpiece ranged from 'fair' to 'excellent', with the highest agreement for angular velocities (0.79 ± 0.08) and translational accelerations (0.73 ± 0.05) and lowest for angular accelerations (0.67 ± 0.06). A Bland-Altman analysis of the peak kinematics from the headband and mouthpiece found the mean bias to be 40.9 $\%$ (of the maximum mouthpiece reading) for the angular velocity, 16.6 $\%$ for the translational acceleration, and -14.1 $\%$ for the angular acceleration.

Conclusions: The field evaluation of the instrumented headband showed reasonable agreement with the mouthpiece for some kinematic measures and impact conditions. Future work should focus on improving the headband performance across all kinematic measures.

Purpose: Aseptic loosening and intraoperative periprosthetic fractures (IOPPFs) are major complications in uncemented total hip arthroplasty (THA). Computational models for assessing primary stability and IOPPF risk preoperatively are limited. This study developed a patient-specific finite element analysis (FEA) framework that replicates stepwise broaching and implantation in uncemented THA, enabling primary stability assessment and providing a foundation for future IOPPF research.

Methods: A FEA framework was developed using patient-specific femoral geometries and heterogeneous material properties to simulate stepwise broaching and implantation along a defined insertion path. Primary stability was assessed via micromotion under physiological loading. Four case studies were performed to (a) demonstrate the framework using three cadaveric femurs, (b) validate the framework against experimental strain measurements obtained via digital volume correlation (DVC), (c) compare predicted outcomes with a literature-based volumetric expansion model, and (d) assess sensitivity to variations in bone material property models.

Results: Average post-implantation von-Mises stress ranged from 9.87 to 14.77 MPa, with each broach increasing stress and indicating progressive bone compaction. Primary stability, assessed via bone-implant micromotion (29.10-78.04 µm), remained well below the 150 µm threshold, considered favourable for osseointegration. The proposed framework showed closer agreement with experimental DVC strains and compared to the volumetric-expansion model, halved the prediction error. The analysis also demonstrated limited sensitivity to variations in E-ρ models.

Conclusion: The proposed FEA method replicates stepwise broaching and implantation in uncemented THA, enabling patient-specific assessment of bone-implant interactions and primary stability, and providing a foundation for preoperative tools to evaluate IOPPF and aseptic loosening risk and guide tailored femoral implant selection.

Purpose: The goal of our study is to evaluate whether impact attenuation, as measured by peak linear acceleration (PLA), degrades over the expected usage life of field-used hockey helmets at both the certification impact speed of 4.5 m/s and at a lower-severity impact of 3 m/s.

Methods: Field-used helmets were collected from the public and other agencies. The impact testing protocol was adapted from CSA Z262.1-15 and ASTM F1045-16. Helmets were impacted at the rear, side, and front. General linear mixed models were used to assess the effect of age and other covariates (liner material, headform size, helmet wear) on peak linear acceleration.

Results: Over 8000 impact tests were conducted on 762 new and field-used helmets. No significant increases or decreases in PLA with helmet age were observed at any of the impact sites or impact speeds. A few helmet shells cracked in our impact tests.

Conclusion: We evaluated the effect of age on the PLA response of hockey helmets in 4.5 m/s and 3.0 m/s impacts from a large sample of field-used hockey helmets. We found no support for our hypothesis that PLA increases with age. Users should continue to remove helmets from service when they are in poor condition.

Purpose: Orthodontic tooth movement fundamentally relies on force-driven remodeling of periodontal tissues. However, the therapeutic predictability of clear aligner therapy (CAT) remains limited, particularly in periodontal-compromised patients. This limitation stems from a critical gap between biomechanical loading and biological remodeling. This study innovatively adopts strain energy density (SED) as a biomechanical parameter to investigate the integrated biomechanical and biological responses of the periodontium to CAT.

Methods: We employed a cross-scale biomimetic framework that combined three-dimensional finite element modeling (FEM) with an in vivo rabbit model. Biological assays, including micro-CT, TRAP staining, immunohistochemistry, 4D proteomics, the Oroboros Oxygraph-2000 (O2K), and Western blot (WB), were conducted in SED-concentrated areas to elucidate SED-triggered biological cascades.

Results: FEM quantified that periodontal SED escalated with attachment loss, concentrating at the labial alveolar crest. In vivo experiments indicated that elevated SED exacerbated periodontal damage, evidenced by reduced bone volume fraction and density, increased TRAP-positive osteoclasts, upregulated IL-1β, and downregulated BMP2. Proteomics identified 908 differential proteins in high-SED regions, which are enriched in NF-κB activation and suppression of oxidative phosphorylation. O2K and WB validated compromised mitochondrial complex I/II function, ATP production, and phosphate/oxygen ratio in these areas.

Conclusion: Using SED as a quantitative metric, this study bridges the biomechanical-biological gap by linking CAT-induced biomechanical responses to mitochondrial bioenergetic dysfunction. These findings establish a basis for a biologically informed predictive system to facilitate personalized CAT.

Purpose: Vaginal tissue tearing during childbirth is a prevalent injury with potential long-term consequences, yet the mechanical factors influencing tear behavior remain poorly understood due to the difficulty of conducting in vivo studies. This study aims to investigate how tear geometry and fiber orientation influence local stress and strain patterns, and whether machine learning (ML) can accurately predict tissue responses from finite element (FE) simulation data.

Methods: Previously characterized swine vaginal tissue, tested via planar biaxial loading, was used to calibrate the Holzapfel-Gasser-Ogden (HGO) hyperelastic constitutive model. FE simulations were conducted for a range of tear orientations, tear sizes, and fiber alignments to evaluate stress and strain distributions near the tear boundary. Four ML models, linear regression with Stochastic Gradient Descent, Random Forest, Support Vector Regression, and Extreme Gradient Boosting (XGBoost), were trained and evaluated on the FE-generated dataset.

Results: The HGO-based FE simulations reproduced experimental results with high fidelity, validating the modeling approach. XGBoost achieved the highest predictive accuracy across all mechanical and geometric outputs. Sensitivity analysis via permutation tests revealed that the initial orientation of the elliptical tear was the most influential predictor of high local stress and strain at the tear during extension.

Conclusion: This integrated FE-ML approach enables rapid, accurate prediction of vaginal tissue behavior under varied tear conditions without additional simulations or experimental tests. By leveraging synthetic data, it provides a powerful tool for injury risk evaluation and supports clinical decision-making in maternal health, particularly where in vivo studies are limited.