Computer Methods in Biomechanics and Biomedical Engineering最新文献

This study established a canine model of non-transport disc distraction osteogenesis (NTDDO) to reconstruct segmental mandibular defects and evaluated its impact on temporomandibular joint (TMJ) biomechanics. Cone Beam computed tomography (CBCT) tracked new bone regeneration in the distraction gap and condylar changes. Three-dimensional finite element analysis (FEA) models were developed to assess the stress changes of condyles, articular discs and distractor at different time points. Condyles and articular discs histological changes were observed. The results showed that the newly formed bone increased in density with prolonged consolidation. On the healthy side, the lateral pole of the condylar head translated forwards and downwards, and the condyle underwent clockwise rotation in both the orbital-auricular and coronal planes. On the distracted side, the medial pole of the condylar head moved downwards, with the condyle rotating clockwise in the coronal plane postoperatively. However, comparisons of the overall condylar positions preoperatively, at the end of distraction, and after eight weeks of consolidation revealed no statistically significant changes. At the postoperative period, FEA revealed a concentrated area of stress on both condyles and articular discs, whereas the stress distribution was relatively uniform preoperatively and after 8 weeks of consolidation. The maximum stress of the distractor occurred at the joint between the distractor wing and the bar. Histological analysis of the condyles and articular discs harvested from stress concentration zones showed intact cartilage structure. The established NTDDO model effectively repairs segmental mandibular defects while inducing temporary TMJ biomechanical alterations without causing irreversible joint damage.

Prostate cancer (PCa) is a leading male malignancy. This study explores the anti-PCa mechanism of Qianlie Xiaozheng decoction (QLXZD) using network pharmacology. From 34 ingredients and 23 potential therapeutic targets, 3 hub ingredients (baicalein, kaempferol, quercetin) and 4 hub targets (CCNB1, CDK1, EGFR, TOP2A) were prioritized. Enrichment analysis of the 23 targets linked them to cell cycle and kinase signaling. Molecular docking confirmed strong binding of the hub ingredients to the hub targets, comparable to known inhibitors. Molecular dynamics simulations supported baicalein-TOP2A complex stability. These findings reveal QLXZD exerts anti-PCa effects via a multi-component, multi-target mechanism, supporting its clinical application.

The biomechanics of breasts during dynamic activities exhibits complex nonlinear dynamics, which cannot be accurately captured by conventional one-dimensional models. To address this limitation, a three-dimensional (3D) nonlinear mass-spring-damper (MSD) model was developed to simulate breast dynamics. The proposed model integrates 16 elastic springs, 16 dampers, and 9 mass blocks to replicate tissue property heterogeneity and multi-directional displacements. Model parameters, including stiffness and damping coefficients, were optimized via iterative calibration against motion capture data from 5 km/h running, and validated using independent data from 10 km/h running. Results show that the simulated displacement trajectories are in good agreement with experimental data, achieving mean relative errors < 3% in all three directions. The proposed framework demonstrates that a computationally efficient MSD model, when coupled with data-driven parameter optimization, can reliably simulate complex breast biomechanics. This work provides a novel and practical modeling tool for breast biomechanical research, with promising utility in clinical and biomechanical areas.

The public has been greatly impacted by the spread of the rabies virus, which highlights the importance of studying its transmission in epidemiology. In this article, we developed a Caputo-Fabrizio fractional derivative model for rabies spread, incorporating the impact of awareness on vaccination and treatment. Existence and uniqueness of solutions are established using a fixed-point theorem, and threshold values with equilibrium stability are analysed. The influence of awareness on infected populations is examined. Numerical simulations illustrate the effects of awareness, vaccination, treatment rates, and derivative orders, with comparisons between fractional and integer-order models.

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.