Computer Methods in Biomechanics and Biomedical Engineering最新文献

This study aims to explore the role of necroptosis in osteoporosis and identify potential diagnostic biomarkers. By analyzing the GSE56815 and GSE7429 datasets, we identified 107 differentially expressed genes associated with necroptosis. Enrichment analysis revealed that these genes were significantly enriched in necroptosis, the NOD-like receptor signaling pathway, and the IL-17 signaling pathway. Furthermore, through protein-protein interaction network analysis, multiple algorithms (MCC and MCODE) screening, and LASSO regression modeling, we ultimately established a diagnostic model consisting of 13 key genes. In vitro cell experiments suggest that CASP3 may serve as a potential target for Minocycline in the treatment of osteoporosis.

This study presents a robust deep learning framework for automatic motor imagery detection from raw EEG signals. Six band-power features were extracted using STFT, and dedicated 2D CNN-LSTM models were trained for each band. Their outputs were fused using a Choquet fuzzy integral to enhance decision reliability under noisy EEG conditions. Alpha- and sigma-band models achieved 88% and 87.1% accuracy, respectively. The fused architecture reached 90.4% on BCI IV-2a and 92.21% on BCI IV-1, outperforming existing methods in motor imagery classification.

Towards 120° spiral notch pattern, this paper proposes a finite element model to characterize the nonlinear deformation and designs an inconsistent-configuration device to achieve constant-curvature bending. Effects of the notch geometry on bending stiffness are analyzed using the model. By deriving bending stiffness equations and modeling cable friction, the non-constant curvature bending behavior of the device are characterized. A nonuniform pattern is designed to maintain a constant force-to-stiffness ratio, ensuring uniform curvature. The optimized design reduces tip displacement errors to <1.81 mm under constant-curvature assumptions. This work provides a systematic approach to modeling and optimizing notched devices for neurosurgical applications.

Neck pain is a highly prevalent condition in the general population, but the addition of helmet loads contributes to the neck pain that 97% of fighter pilots report. The mechanism for neck pain in pilots is unknown; however, there is likely nerve root (NR) dysfunction involved. This study implemented a spinal cord, NRs, and novel foraminal ligaments into the previously validated VIVA OpenHBM. After performing sensitivity analyses and validating added tissues, helmet loads and muscle forces were applied. The results indicate that helmet loads generate compression on NRs that temporarily impair blood flow and impulse propagation in a neutral posture.

Bicondylar tibial plateau fractures are often managed with plate fixation clinically, but no unified standard for fixation mechanical stability evaluation exists. This study investigated the mechanical properties of 15 internal fixation schemes via finite element analysis, simulating three postoperative stages to acquire four key indicators: medial/lateral fracture fragment displacements, internal fixation stress, and fracture site stress shielding rate. The coefficient of variation was used to determine the objective weights of these indicators, establishing a multi-indicator integrated mechanical stability evaluation system. Results showed that medial plate fixation exhibited superior stability, providing a valuable reference for clinical selection of fixation schemes.

Ice hockey is a sport with a high incidence of concussion, but growing attention has been placed on the broader exposure to head impacts, regardless of clinical symptoms, due to their potential cumulative effects. While player education, protective equipment, and rule modifications have been introduced to reduce head impact risk, the effect of rule changes on head impact exposure in adult professional ice hockey has not been well quantified. This study compared the frequency and magnitude of head impacts between two NHL seasons of 2003-04 and 2016-17, that bracketed a period of rule changes targeting contact to the head. Twenty regular-season games from each season were analyzed using a combination of video analysis, physical reconstructions, and finite element modeling to estimate brain tissue strain for each head impact. Total head impact frequency per game did not differ significantly between seasons. However, a significant decrease in glove-to-head contacts and fight-related impacts was observed in 2016-17, reflecting the enforcement of stricter penalties for these actions. Additionally, players in the 2016-17 season experienced a significantly higher frequency of impacts classified within the Low brain strain category. These results suggest that while targeted rules may reduce specific types of dangerous contact, they may also shift the nature of impacts without reducing overall exposure. Understanding how such shifts influence cumulative biomechanical load is essential for guiding future rule evaluations and injury prevention strategies in professional ice hockey.

Prosthetic heart valve (PHV) replacement is an effective treatment for valvular heart disease. The mechanical behavior of heart valves, encompassing solid mechanics, fluid dynamics, and FSI, is fundamental to diagnosing dysfunction and guiding design. This review critically analyzes PHV mechanics, including constitutive modeling, stiffness and strength evaluation, vibroacoustic response, fatigue and fracture, FSI simulation, viscoelastic effects, and hemodynamics. It further summarizes numerical, experimental, and AI-assisted investigation methods, and outlines current challenges and future directions in computational mechanics for PHVs, providing interdisciplinary insights for bioengineering and biomedical applications.

We sought to establish whether dynamic Bayesian optimization (DBO) is a suitable algorithm for human-in-the-loop-optimization (HILO) of the control input of devices interacting with individuals whose output changes during optimization as resulting from motor learning. Simulations were conducted assuming either purely time-dependent participant responses, or assuming responses from state-space models of motor learning. DBO generally outperformed standard Bayesian optimization (BO) in convergence to optimal inputs and outputs after a certain number of iterations. DBO may improve the performance of HILO over BO when a sufficient number of iterations can be evaluated to accurately distinguish between unstructured variability and learning.

The inertial motion unit (IMU) is an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation(LDH). However, the current clinical assessment methods for LDH gait focus on patients' subjective scoring indicators and lack the assessment of kinematic ability; at the same time, individual differences in the motor function degradation of the healthy and affected lower limbs of LDH patients are also ignored. To solve this problem, we propose an LDH gait feature model based on multi-source adaptive Kalman data fusion of acceleration and angular velocity. The gait phase is segmented by using an adaptive Kalman data fusion algorithm to estimate the attitude angle, and obtaining gait events through a zero-velocity update technique and a peak detection algorithm. Two IMUs were used to analyze the gait characteristics of lumbar disc patients and healthy gait people, including 12 gait characteristics such as gait spatiotemporal parameters, kinematic parameters, gait variability and stability. Statistical methods were used to analyze the characteristic model and verify the biological differences between the healthy affected side of LDH and healthy subjects. Finally, feature engineering and machine learning technology were used to identify the gait pattern of inertial movement units in patients with lumbar intervertebral disc disease, and achieved a classification accuracy of 95.50%, providing an effective gait feature set and method for clinical evaluation of LDH.

This study uses transfer learning architectures to detect cardiac murmurs in phonocardiogram signals by denoising the signal, extracting relevant features for spectrograms generation. The Short-Time Fourier Transform, Mel-Frequency Cepstral Coefficients, and Continuous Wavelet Transform techniques were applied on Physionet's CirCor Digiscope PCG dataset. VGG16, VGG19, ResNet50, and InceptionV3 models, were trained on these spectrograms for binary classification. Fourth-order Butterworth bandpass filter, used with Savitzky-Golay filtering, gave the best results. The CWT Spectrogram and VGG19 combination yielded best accuracy of 89.44%. Different combinations of spectrograms and transfer learning architectures performed better on performance metrics of precision, recall, F1-score, and ROC-AUC.