Computer Methods in Biomechanics and Biomedical Engineering最新文献

The classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model's generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications.

The S100 family proteins (S100s) participate in multiple stages of tumorigenesis and are considered to have potential value as biomarkers for detecting and predicting various cancers. But the role of S100s in lung adenocarcinoma (LUAD) prognosis is elusive. Transcriptional data of LUAD patients were retrieved from TCGA, and relevant literature was extensively reviewed to collect S100 genes. Differential gene expression analysis was performed on the LUAD data, followed by intersection analysis between the differentially expressed genes (DEGs) and S100 genes. Unsupervised consensus clustering analysis identified two clusters. Significant variations in overall survival between the two clusters were shown by Kaplan-Meier analysis. DEGs between the two clusters were analyzed using Lasso regression and univariate/multivariate Cox regression analysis, leading to construction of an 11-gene prognostic signature. The signature exhibited stable and accurate predictive capability in TCGA and GEO datasets. Subsequently, we observed distinct immune cell infiltration, immunotherapy response, and tumor mutation characteristics in high and low-risk groups. Finally, small molecular compounds targeting prognostic genes were screened using CellMiner database, and molecular docking confirmed the binding of AMG-176, Estramustine, and TAK-632 with prognostic genes. In conclusion, we generated a prognostic signature with robust and reliable predictive ability, which may provide guidance for prognosis and treatment of LUAD.

Total Knee Replacement (TKR) effectively improves mobility and reduces pain in patients with severe arthritis. Polyethylene wear limits implant longevity by inducing osteolysis and aseptic loosening. Therefore, this study evaluates the lubrication performance of four clinically relevant UHMWPE formulations representing distinct combination of crosslinking dosage and antioxidant. An ellipsoidal on-plane model of an artificial knee joint is considered and all governing equations are solved using Newton Raphson method. The results indicate that, vitamin E blended UHMWPE without cross linking exhibits highest contact pressure with only a marginal reduction in film thickness, while achieving the lowest coefficient of friction under EHL conditions.

In this study, two case models of intramural hematoma (IMH) progressing to aortic dissection were analyzed using computational fluid dynamics, with a focus on the hemodynamic characteristics before and after the dissection. In the initial IMH model, disturbed flows were observed during the early and late phases of contraction within the aortic arch and regions of significant vascular curvature. Furthermore, the IMH models also revealed extensive areas of low time-averaged wall shear stress (TAWSS), high oscillatory shear index (OSI), and high endothelial cell activation potential (ECAP) values, particularly on the lesser-curvature side of the aortic arch. Regions with low TAWSS, high OSI, and high ECAP in IMH cases can be identified as potential high-risk areas for disease progression to aortic dissection.

Periapical lesions may compromise the biomechanics of endodontically treated teeth. This study aimed to quantify, via finite element analysis (FEA), the effect of lesion size on stress distribution and deformation in a maxillary central incisor. Five 3D models (control; 2, 4, 6, 8 mm lesions) were analysed under a 300 N oblique load at 135°. Global maximum equivalent stress remained stable (89.856 MPa vs 89.673 MPa; -0.2%), whereas lesion stress increased (0.25-0.57 MPa) and deformation rose from 0.1437 to 0.1533 mm (+6.7%). Lesion enlargement minimally affects global stress but induces adverse local biomechanical changes.

This paper reports on a clinical case of craniofacial displacement from treatment with a Right Angle Maxillary Protraction Appliance (RAMPA). A seven-year-old girl was treated over 17 months using VomPress (4 months) and Hybrid (13 months) intraoral devices with RAMPA. Finite Element Analysis (FEA) simulations of a skull model with all sutures and validated material properties supported clinical findings. RAMPA produced an anterosuperior maxillary shift, counterclockwise mandibular rotation (-1.0°), and a 2.9° clockwise decrease in RAMUS angle. Both clinical and FEA results show RAMPA with Hybrid enhances maxillary protraction while minimizing downward displacement of the mid-palatine suture.

Long non-coding RNA (lncRNA) screening holds promise for elucidating mechanisms behind graphene-related tumor therapy. This study aimed to investigate the role of graphene therapy-related lncRNA signatures (GTLncRNASig) in lung adenocarcinoma (LUAD) and potential pathways within the tumor microenvironment. LUAD transcriptome and clinical data from The Cancer Genome Atlas (TCGA) were analyzed to develop a prognostic risk model for GTLncRNASig using Cox regression. Further analyses included Kaplan-Meier survival analysis, principal component analysis (PCA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, a nomogram risk model, and tumor immune dysfunction and exclusion (TIDE) assessment. Drug sensitivity was explored using this model. Mendelian randomization (MR), Double Machine Learning (DML) and Bayesian weighting validated causal relationships between enriched pathways and LUAD. Supervised and unsupervised machine learning algorithms evaluated robustness and uncovered hidden correlations in MR results. A 35-lncRNA risk model (GTLncRNASig) was established, identifying strong associations with immune pathways, including Type II IFN Response and MHC class I. High-risk subgroups exhibited immune microenvironment-linked prognostic traits. Screening revealed 12 potential chemotherapy agents, and the stem cell index mRNAsi correlated with LUAD prognosis. MR and Bayesian weighting implicated the systemic lupus erythematosus (SLE) pathway as a LUAD risk factor. Machine learning confirmed the reliability of these findings. This study identified 35 lncRNAs that constitute a prognostic signature in the context of graphene-related LUAD treatment, highlighting immune-related processes and the SLE pathway's role in LUAD. These insights link autoimmune diseases with tumorigenesis and provide valuable guidance for immunotherapy predictions.

The timely prediction of preterm labor plays a crucial role in improving neonatal survival, as well as in providing professional care for the infant and mother. The Electrohysterography (EHG) signals have higher sensitivity, which provides a promising avenue for analyzing preterm labor contractions. The conventional preterm labor prediction approaches are highly vulnerable to various limitations regarding higher correlations, a lack of robustness, as well as lower sensitivity to minor variations. The research proposes a multi-head attention-enabled distributed neural network with a bidirectional long short-term memory (multi-DNBiTM) framework that aims to overcome the challenges of conventional approaches by exhibiting accurate predictions. The research employs Root mean energy-entropy deep features (RMEn2D) that are beneficial in analyzing the variations of each frequency subbands, providing a higher representation of minor uterine contractions. The multi-head attention categorizes the feature maps into diverse heads, which facilitates the learning of intrinsic patterns. Moreover, multi-level training improves prediction accuracy through analyzing the signals at different levels and allows to learn fine details. The efficiency of the multi-DNBiTM model is evaluated with the prevailing approaches, which reveal better outcomes by acquiring 96.93% accuracy, 98.45% sensitivity, and 98.19% specificity.

EEG-based emotion recognition has attracted considerable research interest due to its non-invasive characteristics and superior temporal resolution. Challenges still remain in modeling global dependencies, cross-channel interactions, and cross-domain generalization. This study proposes a novel model called DAVG-ViT (a Domain-Adaptive Vision Transformer with GA-BiLSTM for EEG Emotion Recognition). It enhances global modeling using Adapters, captures cross-channel relationships and bidirectional temporal dynamics via Group Attention and BiLSTM, and reduces distribution shift through a gradient reversal layer-based adversarial domain adaptation mechanism. Experiments on SEED, SEED-IV, and DEAP show superior performance, achieving accuracies of 99.37% on SEED, 95.91% on SEED-IV, and 98.95% for arousal and 98.47% for valence on DEAP.

This study introduces a generative machine learning-based framework for the reverse design of puncture needle deflection curves, aiming to address the challenge of precise navigation of puncture needles in soft tissues. The framework consists of two primary components: reverse prediction and forward validation, which work together to derive optimal needle tip design parameters from the target deflection curve. A needle-tissue interaction physics model is developed based on Euler-Bernoulli beam theory, and 10,000 datasets are generated through a parametric sampling method. Experimental results demonstrate that the curve classifier achieves an F1 score ranging from 0.945 to 0.969, while the deflection curve regressor shows an R² value of 0.9981 with an average error of only 0.0008 mm. The average relative error of the predicted design parameters is kept within 5%. Furthermore, an innovative ensemble learning strategy is employed, which leads to a further reduction in the prediction error by 4.3%. In cases involving high-deflection curves, the optimal design (d = 1.01 mm, L = 113.84 mm, θ = 39.08°) was validated via finite element analysis and showed excellent agreement with the target trajectory, with an NRMSE value of 1.15903E - 4 and deviation controlled within ±0.3 mm. This study not only provides an efficient solution for the precise design of medical needle tips but also establishes a new research paradigm for the intelligent design of medical devices.