Computer Methods in Biomechanics and Biomedical Engineering最新文献

Pedestrian injury risks in car-to-pedestrian collisions are strongly influenced by anthropometric characteristics, yet existing human body models rarely represent small-stature Chinese female pedestrians. This study presents the Tianjin University of Science and Technology Injury Bionic Model (TUST IBMs F05-P), developed to represent a 5^th percentile Chinese female pedestrian. Detailed anatomical structures were reconstructed directly from medical imaging data without geometric scaling, preserving subject-specific anatomical geometry, and the model was meshed predominantly with hexahedral elements. A representative walking posture was defined, and the model was evaluated through a certification procedure conducted according to the Euro NCAP CP540 pedestrian human body model certification protocol. Simulation results showed that key biomechanical indicators, including Head Impact Time (HIT), contact forces, and kinematic trajectories, predominantly fell within the response corridors specified in CP540. Quantitative assessment using the CORA (CORrelation and Analysis) method defined in ISO/TS 18571:2024 yielded an overall score of 0.84, indicating a high level of correlation with the CP540 reference corridors. The certification results indicate that the TUST IBMs F05-P produces stable and reproducible responses under the tested impact conditions. By providing an anatomically realistic representation of a small-stature Chinese female pedestrian, this model addresses the lack of population-specific pedestrian models and offers a validated basis for pedestrian injury analysis and vehicle front-end safety evaluation.

Exercise-induced fatigue assessment via ECG classification relies on accurate R-peak detection for reliable HRV features. Addressing the lack of robust models for noisy exercise ECGs, we propose UNet-M-D, integrating positional encoding, multi-head self-attention, and dynamic convolution. Evaluated on GUDB and EPFL datasets, it achieves superior R-peak detection performance (up to 99.2% accuracy) with high noise resilience (6-18 SNR). Using optimally selected HRV features, our method attains 77.4% accuracy in fatigue classification, providing a scientific basis for sports health management and training adjustment.

We have modified the summary format as per the requirements to an unstructured presentation. The revised content is as follows: Sevoflurane dose-dependently suppresses respiratory activity, yet its molecular mechanisms remain incompletely understood. In this study, we integrated transcriptomic data from the Gene Expression Omnibus and GeneCards to identify differentially expressed genes associated with sevoflurane anesthesia and respiratory depression. A protein-protein interaction network was constructed, and hub genes were screened using the MCC, Betweenness, Closeness, and MCODE algorithms. Functional associations of these hub genes were further explored using the GeneMANIA platform. Potential therapeutic compounds were predicted through the Connectivity Map (CMap) database. The effects of sevoflurane on ICAM1 expression and the intervention of PD-98059 were experimentally validated in SH-SY5Y cells. Ultimately, nine hub genes were identified, including MMP9, CXCL1, IL1B, NF-κB1, CXCL8, IL6, CCL2, ICAM1, and VCAM1. These genes were mainly enriched in pathways related to inflammatory responses, immune regulation, and chemotaxis. Increased infiltration of dendritic cells, MHC class I molecules, and neutrophils was observed in the sevoflurane-treated group. PD-98059 was predicted as a potential therapeutic candidate and was confirmed to reverse sevoflurane-induced upregulation of ICAM1. Overall, this study identifies key genes and inflammatory pathways associated with sevoflurane-induced respiratory suppression, providing new insights into its molecular mechanisms and potential therapeutic strategies.

Cardiovascular disease is one of the important diseases affecting human life and health, and the effective prediction and classification of cardiovascular diseases through the development of medical informatization can reduce the diagnosis and treatment time and misdiagnosis rate of doctors, better serve patients with cardiovascular diseases, and help reduce patients' expenses and improve patients'medical efficiency. Our comprehensive diagnosis and treatment system has made breakthroughs in the prediction of cardiovascular diseases, the noise reduction of ECG signal, and the classification of arrhythmias, which can better serve the cardiovascular diseases and provide help for patients with cardiovascular diseases.

To address data scarcity and distribution shifts in motor imagery electroencephalogram (MI-EEG) based brain computer interface, we propose a 1-dimensional convolution-based deep transfer learning model with embedded Feature Alignment block (1DC-DTL-FA) in this article. It integrates multi-stage feature extraction, classification, and FA block. Unlike complex models, it utilizes Neural Architecture Search (NAS) to automatically locate the optimal FA position in Euclidean space Evaluated on BCI 2000 and BCI IV2a datasets, 1DC-DTL-FA achieved superior accuracies of 89.80% and 82.96%. The results demonstrate that this simple architecture effectively handles complex feature extraction and online alignment, outperforming state-of-the-art models in MI-EEG decoding.

Understanding instrument-tissue interaction is vital for safe surgery. We used Abaqus to simulate arterial response to EasyEndo-Lite Staple rotation, focusing on abrupt motion and strain-rate effects. Sudden angular rotation produced high strain rates and peak stresses in the clamped arterial segment, reaching 1.4 MPa at 15° and ∼3.7 MPa at 30°. Rapid cessation of rotation increased forces by up to 28%, indicating elevated damage risk. The simulations provided detailed deformation and stress data across scenarios, demonstrating how optimizing instrument design and operating parameters could substantially reduce tissue trauma and improve surgical outcomes.

Machine learning techniques have recently shown significant promise in electroencephalograph (EEG)-based depression recognition. However, existing methods often rely on simple feature concatenation to fuse information from multiple perspectives, failing to adequately exploit the complementarity of heterogeneous features. To this end, we propose a novel affective computing framework for identifying depression that integrates nonlinear analysis with adaptive feature coupling. The framework first uses different entropy measures to effectively characterize the intricate and chaotic dynamics present in EEG signals. Then, a new fusion strategy based on weighted average is proposed to adaptively aggregate complementary information among multi-view features. More importantly, this strategy can mitigate the influence of redundant features. Experimental results on publicly available datasets show that our methodology can dramatically improve the accuracy of depression recognition. Meanwhile, visualization analysis reveals that compared with healthy controls, patients with depression exhibit lower EEG entropy values, reflecting reduced complexity in their brain activity. Due to the good performance of the framework, this study provides important insights into the usefulness of nonlinear analysis and adaptive feature fusion in EEG decoding tasks.

To quantify the response characteristics of varying positions of the human head in an upright sitting posture under fore-aft whole-body vibration, modal and random response analysis in 0-20 Hz was conducted on a previously created whole-body finite element model with detailed anatomical structure. The study revealed two main peaks in the transmissibility of the seat to the head, with direct-axis peak frequencies of 0.76 and 2.74 Hz, respectively, and cross-axis peak frequencies of around 2.9 and 5.3 Hz, respectively. Moreover, the direct-axis peaks decreased from the top of the head to four sides, with the first peak decreasing by 21% (from 3.44 to 2.73) and the second peak decreasing by 74% (from 2.88 to 0.76). The maximum and minimum values of cross-axis peaks in the sagittal plane were located at the forehead and crown, respectively, and the maximum value (2.1) was 5.8 times the minimum value (0.36). Although the peak frequencies of vibration transmissibility of the seat to different positions of the head were the same under fore-aft whole-body vibration, the amplitudes of transmissibility varied greatly. Therefore, there were significant differences in vibration measurement and vibration comfort evaluation at different positions of the head.

Cancer chemotherapy scheduling presents a significant optimization challenge: it aims to minimize the tumor burden while adhering to toxicity and pharmacokinetic constraints. This study employs a bang-bang optimal control framework applied to a nonlinear cancer chemotherapy model with state constraints. The model incorporates pharmacodynamic parameters and cumulative toxicity limits and is numerically solved via a high-resolution discretization approach in the AMPL modeling environment with IPOPT. The proposed method yields a final tumor size of $9.9466\times {10}^3\text{,}$ demonstrating a 32.8% improvement over previous optimization techniques. We also investigated the role of time-dependent tumor reduction constraints and performed a sensitivity analysis on key biological parameters, such as the tumor growth rate, drug responsiveness, and biochemical clearance. The proposed framework, through its integration of parameter sensitivity analysis and constrained optimal control, provides a basis for adaptive and patient-specific chemotherapy scheduling that can dynamically adjust to individual tumor and pharmacokinetic profiles. These findings highlight the potential of optimal control methods to inform personalized chemotherapy regimens and suggest directions for clinical translation. However, further validation using real patient data is necessary to confirm the robustness and applicability of the proposed approach.

Inertial measurement units (IMUs) are low-cost, wearable sensors that can estimate body segment orientation by tracking relative sensor orientations. This review aimed to synthesize and evaluate studies investigating the accuracy and reliability of IMUs in measuring shoulder kinematics for clinical application in patients with musculoskeletal injuries. Shoulder kinematics were chosen due to their importance in assessing upper extremity function, performing overhead activities, and the increasing demand for objective, accessible motion-tracking tools in clinical settings. Studies within PubMed/MEDLINE, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, and Google Scholar were screened for eligibility. They were selected based on the following inclusion criteria: (1) application of inertial sensors to assess motions, (2) sensors used accelerometers and gyroscopes or similarly functioning technologies, (3) sensors applied to shoulders, (4) studies published from 2011 to 2024, (5) studies written in English, (6) studies found in peer reviewed, original research articles, (7) studies with full text available. Of 1900 articles identified in our initial literature search, 49 were included. Articles were excluded based on these criteria: (1) Reviews, systematic reviews, or meta-analyses, (2) Studies without ethical approval, (3) Animal or cadaveric studies, (4) Studies prior to 2011. A data extraction was included with key findings of each article. The Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) quality assessment tool was used to assess each article's risk of bias. We compared outcome metrics across studies quantifying IMU accuracy and reliability, including root mean square error (RMSE) and intraclass correlation coefficient (ICC), respectively. IMU-based shoulder kinematics exhibited a wide-range of RMSEs (<1° - 12°) and ICCs (0.32 - 0.98) depending on the motion and number of sensors used. Overall, there was a tolerable RMSE (between 5-10°; mean = 7.10 ± 3.97) and good ICC (>0.75; mean = 0.810 ± 0.145) across studies for 6.77 IMUs on average. The goal of this review was to assess the current IMU use in upper extremities, identify factors preventing clinical use, and inform future research. More IMU-based clinical studies are needed to understand shoulder pathology motor deficits. Additional validation studies are needed to demonstrate IMU efficacy when paired with other technologies.