Computer Methods in Biomechanics and Biomedical Engineering最新文献

This study aimed to evaluate the effects of linear elastic vs. hyper-viscoelastic periodontal ligament (PDL) models and uniform vs. nonuniform alveolar bone models on dental biomechanics. Four teeth (incisor 31, canine 43, premolar 45, and molar 36) were subjected to 1 N of force in the distal, lingual, labial, and mesial directions, respectively. The simulations indicated that when the PDL was modeled as hyper-viscoelastic, maximum stress decreased by an average of 68.93%, whereas maximum strain increased by an average of 530.02%. This study quantified the effects of different material models on dental biomechanics and provides guidance for finite element modeling.

Chronic Kidney Disease (CKD) requires accurate stage-wise prediction for timely intervention, yet most studies focus on binary classification. This study proposes an AI-driven multiclass machine learning framework for CKD staging using estimated glomerular filtration rate (eGFR). A clinically validated UCI dataset was labeled by stage according to National Kidney Foundation guidelines and augmented using CTGAN to address data imbalance and data scarcity. Random Forest, XGBoost, and Multi-Layer Perceptron models were evaluated using 10-fold stratified cross-validation, with Random Forest achieving the highest accuracy of 97.92%. SHAP-based interpretability identified clinically relevant biomarkers, enabling reliable and explainable CKD stage prediction.

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.