Computer Methods in Biomechanics and Biomedical Engineering最新文献

To mitigate the limitations of Light Gradient Boosting Machine (LightGBM) in processing heterogeneous cardiovascular disease (CVD) data, a Hierarchical Quantum Ensemble Model (HQEM) is proposed. This architecture deploys a Quantum Neural Network (QNN) and eXtreme Gradient Boosting (XGBoost) as parallel base classifiers to capture non-linear quantum patterns and sequential gradient trends. The resulting ensemble outputs enrich the feature space for a LightGBM meta-classifier. Validation across integrated datasets yielded 97% accuracy and a 98% Area Under the Curve (AUC), demonstrating the model's superior efficacy in handling complex feature distributions for robust CVD classification.

Reliable detection of paroxysmal atrial fibrillation (PAF) poses a significant challenge. We propose a generalizable machine learning (ML) algorithm for PAF detection (Gml-PAF) that uses 21-beat inter-beat intervals (IBI). Gml-PAF employs a model-agnostic framework integrating model selection, feature selection, and hyperparameter tuning. It is trained and evaluated across 16 PhysioNet electrocardiogram (ECG) databases, demonstrating robust cross-database generalization. In independent tests, it achieves F1 scores of 0.747-0.987 and AUC values of 0.933-0.999. The algorithm matches deep learning (DL) performance with longer IBI sequences and surpasses conventional ML methods, confirming its strong utility for wearable screening.

The socket shield technique (SST) is a promising protocol for immediate implant placement in the anterior esthetic zone, yet the biomechanical impact of shield design parameters across different tooth positions remains unclear. This study investigated how shield geometry influences peri-implant stress distribution in lateral incisors and canines, aiming to support anatomy-driven design strategies. A three-dimensional maxillary model was reconstructed from cone-beam computed tomography of a healthy subject. The left central incisor, lateral incisor, and canine were segmented, and nine finite element models with varying shield length, thickness, and jump gap were established. under time-dependent functional loading. A time-dependent oblique load at 45° was applied, and stress distributions (von Mises, maximum and minimum principal stresses) and displacements of the shield and periodontal ligament (PDL) were evaluated. Results showed that direct transfer of central incisor-based designs yielded suboptimal stress regulation in lateral incisors and canines. Larger jump gaps enhanced stress mitigation in lateral incisors, whereas a shield length of half the root outperformed one-third in canines. Increased shield thickness promoted stress dispersion, but under space constraints, reducing thickness to allow a wider jump gap maintained stability. In conclusion, these findings provide finite element evidence that individualized shield designs are essential to optimize mechanical stability and long-term outcomes in SST.

Direct measurement of in vivo glenohumeral joint motion and contact mechanics remains challenging. This study evaluated feasibility of co-simulation of glenohumeral contact and dynamics using best available anatomical and biomechanical data. We augmented an existing shoulder model to include joint contact, passive stabilizers, and three additional translational degrees of freedom. Anthropometric scaling and Monte Carlo analysis were used to examine how subject-specific factors affect joint mechanics during scaption. Model predictions aligned with experimental data, with height and shoulder strength emerging as key predictors. These findings support the utility of co-simulation modeling and highlight importance of individual variability in shoulder loading.

This study aimed to compare functional brain networks and identify recovery markers in 12 stroke patients (SG) and 14 healthy controls (HG) using EEG during three fist-task paradigms. Analyzing clustering coefficient (CC), characteristic path length (CPL), small-world index (SWI), and frontal node strength across frequency bands, passive task revealed significant alpha band differences in CC/CPL/SWI between groups. Lower SG strength in alpha/mu vs. controls predicted better recovery. An automated source imaging pipeline reduced volume conduction effects, providing new insights into stroke rehabilitation outcomes. Large-scale source imaging shows promise for broader disease applications.

Coronary heart disease (CHD) is a significant global health concern, necessitating continuous advancements in treatment modalities to improve patient outcomes. Traditional Chinese medicine (TCM) offers alternative therapeutic approaches, but integration with modern biomedical technologies remains relatively unexplored. This study aimed to assess the efficacy of a combined treatment approach for CHD, integrating traditional Chinese medicinal interventions with modern biomedical sensors and stellate ganglion modulation. The objective was to evaluate the impact of this combined treatment on symptom relief, clinical outcomes, hemorheological indicators, and inflammatory biomarkers. A randomized controlled trial was conducted on 117 CHD patients with phlegm-turbidity congestion and excessiveness type. Patients were divided into a combined treatment group (CTG) and a traditional Chinese medicinal group (CMG). The CTG group received a combination of herbal decoctions, thread-embedding therapy, and stellate ganglion modulation, while the CMG group only received traditional herbal decoctions. The CTG demonstrated superior outcomes compared to the CMG across multiple parameters. Significant reductions in TCM symptom scores, improved clinical effects, reduced angina manifestation, favorable changes in hemorheological indicators, and decreased serum inflammatory biomarkers were observed in the CTG post-intervention. The combination of traditional Chinese medicinal interventions with modern biomedical sensors and stellate ganglion modulation has shown promising results in improving symptoms, clinical outcomes, and inflammatory markers in CHD patients. This holistic approach enhances treatment efficacy and patient outcomes. Further research and advancements in sensor technology are needed to optimize this approach.

This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy.

Myocardial Infarction (MI) refers to damage to the heart tissue caused by an inadequate blood supply to the heart muscle due to a sudden blockage in the coronary arteries. This blockage is often a result of the accumulation of fat (cholesterol) forming plaques (atherosclerosis) in the arteries. Over time, these plaques can crack, leading to the formation of a clot (thrombus), which can block the artery and cause a heart attack. Risk factors for a heart attack include smoking, hypertension, diabetes, high cholesterol, metabolic syndrome, and genetic predisposition. Early diagnosis of MI is crucial. Thus, detecting and classifying MI is essential. This paper introduces a new hybrid approach for MI Classification using Spectrogram and Bayesian Optimization (MI-CSBO) for Electrocardiogram (ECG). First, ECG signals from the PTB Database (PTBDB) were converted from the time domain to the frequency domain using the spectrogram method. Then, a deep residual CNN was applied to the test and train datasets of ECG imaging data. The ECG dataset trained using the Deep Residual model was then acquired. Finally, the Bayesian approach, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were used to derive performance measures. The MI-CSBO method achieved a 100% correct diagnosis rate, as detailed in the Experimental Results section.

The fundamental function of an optimal cervical pillow is to provide sufficient support to maintain normal spinal alignment and minimize biological stress on the contact surface throughout sleep. The recent advancements in cervical pillows have mainly focused on the subjective and objective evaluations of support comfort, as well as the relationship between pillow height and cervical vertebrae posture. However, only a few studies have addressed shape design guidelines and mechanical performances of the pillows themselves. In this study, a two-sectional contour cervical pillow comprising an arc and a Bezier curve is designed to support the head and neck. The design of the arc-shaped neck section incorporates the Cobb's angle and Borden value from healthy individuals to reflect the consistency of normal cervical anatomical features. The Bezier curve-based head section takes the head length and neck depth into account as significant individual differences. Static analysis and lattice optimization are performed in ANSYS Workbench to develop a variable-density cellular structure, aimed at improving air permeability and reducing the risk of pressure ulcers associated with the cervical pillow. The rapid prototyping technique fused deposition modeling (FDM) and thermoplastic material polylactic acid (PLA) are employed for fabricating different cellular structures. The results demonstrate that the neck section experiences less stress and greater deformation in comparison to the head section, indicating good comfort and support provided by the designed cervical pillow. Additionally, the compressive, bending, and cushion properties of the 3D-printed cervical pillow with variable-density cellular structure are experimentally validated, further confirming its effectiveness.

Metatarsal stress fractures (MSF), particularly the 2^nd and 3^rd MSF, are common injuries among athletes. Although there are several practices to reduce foot and ankle injuries, there is no injury prevention program specifically designed to minimize MSF. This is mainly due to the lack of information about the loadings/postures that cause MSF. Therefore, this study aimed to investigate dangerous loadings/postures potentially causing MSF during push-off (PO). The analysis was conducted with Finite Element Modelling (FEM), calibrated with the three-point bending test, and validated with peak plantar pressure (PPP) and fracture force measurement. Extended Finite Element Method was used for MSF simulation such that ten different foot and ankle configurations were designed, with five for each of the 2^nd and 3^rd MSF under pure vertical loadings. A more complex loading, ankle eversion/inversion during PO, was also examined for the MSF. The average error percentage for the calibration of the model with the three-point bending test was 3.05%. The average error percentages for the validation of the model with PPP and fracture force measurements were 18% and 30%, respectively. The outcomes of pure vertical loadings indicated the higher potential for the 2^nd and 3^rd MSF at 30% PO and 70% PO, respectively. The results of ankle eversion/inversion loadings represented that the most dangerous posture for MSF was 30° ankle eversion for the 3^rd metatarsal at 70% PO. These results provide a guide, including what postures to avoid for the 2^nd and 3^rd MSF among people who are at high risk of MSF.