Computer Methods in Biomechanics and Biomedical Engineering最新文献

Blood glucose levels are essential for metabolism and brain function; insulin regulates sugar to prevent hypo- and hyperglycemia. Proper control prevents diabetic complications from insulin deficiency or resistance. Rapid, precise diabetes identification is critical for effective care. This study proposes SCAW-Net within TabNet to boost prediction accuracy and computational speed, compared with AdaBoost, XGBoost, Bagging, and Random Forest. Trained on diabetes features and tested on multiple datasets, the model achieved 98.9% accuracy, outperforming others. Consistent results on complex, imbalanced data validate SCAW-Net in TabNet as a promising real-world diabetes prediction tool, supporting timely clinical intervention and improved patient management outcomes.

The aim of the present study is to investigate the complexity and stability of human ambulation and the implications on robotic prostheses control systems. Fourteen healthy individuals participate in two experiments, the first group run at three different speeds. The second group ascended and descended stairs of a five-level building block at a self-selected speed. All participants completed the experiment with seven inertial measurement units wrapped around the lower body segments and waist. The data were analyzed to determine the fractal dimension, spectral entropy, and the Lyapunov exponent (LyE). Two methods were used to calculate the long-term LyE, first LyE calculated using the full size of data sets. And the embedding dimensions were calculated using Average Mutual Information (AMI) and the False Nearest Neighbor (FNN) algorithm was used to find the time delay. Besides, a second approach was developed to find long-term LyE where the time delay was based on the average period of the gait cycle using adaptive event-based window. The average values of spectral entropy are 0.538 and 0.575 for stairs ambulation and running, respectively. The degree of uncertainty and complexity increases with the ambulation speed. The short term LyEs for tibia orientation have the minimum range of variation when it comes to stairs ascent and descent. Using two-way analysis of variance we demonstrated the effect of the ambulation speed and type of ambulation on spectral entropy. Moreover, it was shown that the fractal dimension only changed significantly with ambulation speed.

Magnetic fluid hyperthermia and magnetic drug delivery depend on accurate prediction of ferrofluid transport and heat transfer within tumour-surrounded blood vessels. Motivated by the need for physiologically realistic modelling of such therapies, the current study develops a mathematical model of ferrofluid flow and heat transfer in an inclined cylindrical vessel immersed in tumour tissue, taking into account temperature-dependent thermal conductivity and viscosity, as well as magnetic-field-induced body forces. This novel study integrates inclined flow within tumour-surrounded vessels, non-constant thermophysical properties, and magneto-thermal coupling. With the one-dimensional axisymmetric form of coupled momentum and energy equations, in tumour tissue, we describe a nonlinear thermal and flow response to excitation by a magnetic field. This creates a resulting boundary value problem that is non-dimensionalised using a similarity transformation and solved numerically with MATLAB's bvp4c, allowing for a parametric study over the inclination angle, ferromagnetic interaction parameter, and nanoparticle concentration. The results show that temperature-dependent properties influence velocity gradients, skin friction, and heat transfer, particularly near the vessel tumour interface. Thermal transport is further intensified by radiative effects and internal heat generation, leading to a notable enhancement of the Nusselt number, while inclination and curvature introduce secondary but non-negligible modifications. Overall, the study provides quantitative insight into magneto-thermal interactions in ferrofluid-based therapies and offers a theoretical basis for improving magnetic hyperthermia and targeted drug delivery strategies.

This study proposes a mandibular fixation technique based on mortise-and-tenon construction. Mandibular defect models with varying interlocking angles were established using the fibula and iliac as grafting materials, and comparative analyses of static stress distribution, displacement control, and fatigue life were conducted. Optimal performance was achieved with a fibular tenon width of 0.8 cm, a length of 1 cm, and an angle of 75°. Fatigue analyses indicated that the system satisfied clinical requirements. The proposed method shows potential for clinical application in mandibular reconstruction.

Diabetes, one of the most serious diseases in the world, however, early detection can prevent diabetes. This work proposes a novel approach to identifying early signs of diabetes based on deep learning methods. First, the input data is pre-processed and the features are selected using an improved Cheetah Optimization (ICO). Finally, diabetes is classified using a dual attention-based deep cat convolutional stacked sparse autoencoder model (DA_DCC_SSAE). The proposed study improves the results and proves that the proposed method produces better results in terms of accuracy (98.4% - dataset-1, 98% - dataset-2, 97.4% - dataset-3, and 96.8% - dataset-4.

This study constructed a prognostic and immunotherapy predictive model for gastric cancer based on amino acid metabolism-related genes. Using data from TCGA and GEO databases, the model was built via Cox and Lasso regression and validated in independent cohorts. It effectively predicts patient survival and shows significant correlations with the tumor immune microenvironment, immune cell infiltration, and immune checkpoint expression. Drug sensitivity analysis suggests potential therapeutic options. This model may serve as a potential biomarker for predicting prognosis and immunotherapy efficacy in gastric cancer patients.

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.