Computer Methods in Biomechanics and Biomedical Engineering最新文献

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.

Brain-computer interfacing facilitates usage of medical devices such as Electroencephalograms to study brain activities using signal processing techniques. Hand movements are motor activities which cause signature electrical signals in the electroencephalogram recordings. Signal processing and machine learning can be used to remove artefact contamination, amplify subtle features associated with hand movement and classify them. This paper experiments to utilize mathematical models to extract features and classify hand movement from electroencephalogram data up to 98% accuracy based on tests performed on an open-sourced dataset. The study, after further tests, can be used to build prosthetic limbs and mind-controlled robotic arms.

Biodegradable poly-lactic acid (PLA) stents reduce the risk of restenosis and late thrombosis, but their clinical adoption is limited by the trade-off between radial strength and deliverability. To address this, a novel auxetic-structured PLA (ASP) coronary stent is proposed. Finite element analysis was conducted to evaluate its performance against commercial Absorb and Fantom stents. The ASP stent achieved a 12.6% higher radial strength than the Fantom stent, along with -31.3% axial foreshortening, as well as reduced radial recoil and plastic strain. This improvements are attributed to the synergistic combination of PLA's properties and auxetic structure, which balances stability with deliverability.

Alternative splicing (AS) generates diverse mRNA isoforms and plays a key role in cancer. Using Drosophila homologous genes, this study identified prognostic AS events across pan-cancer via Cox regression and constructed robust prognostic signatures validated by survival and ROC analyses. AS was shown to significantly influence the tumor immune microenvironment and immunotherapy response. COL1A1 emerged as a strong pan-cancer prognostic factor. Splicing factor regulatory networks and Mendelian randomization analyses further revealed genes associated with tumor development, highlighting AS as a valuable source of prognostic biomarkers and therapeutic targets.

This paper introduces a multi-scale feature fusion framework for EEG-based motor imagery (MI) classification, designed to leverage the spectral-temporal-spatial structure of EEG data, its nonlinear intrinsic characteristics, and convolutional features. Several proposed feature fusion models surpass current state-of-the-art classification systems for MI tasks. A support vector machine (SVM) model achieves an accuracy of 86.92% on the BCIC-IV-2a dataset. To mitigate redundancy, the proposed models incorporate dimensionality reduction via factor analysis (FA) and channel selection using common spatial pattern (CSP). Selecting 12 channels yields superior classification performance compared to using all 22or only 8 selected channels, achieving an accuracy of 88.17%.