Computer Methods in Biomechanics and Biomedical Engineering最新文献

The most common type of proximal humerus fracture is surgical neck fracture. The purpose of this paper is to study the mechanical mechanism and the effect of bone degeneration on humeral surgical neck fractures. The right humerus finite element models were established based on CT computed tomography. The stress values and crack propagation process under an axial force were obtained. Three indexes (mechanical property, cortical bone thickness of diaphysis and cancellous bone volume fraction) in this article were used to describe bone degeneration. The results showed that the three models group with different index had the same conclusions. The calculation results showed that the higher the bone degeneration level, the shorter the fracture time and the lower the fracture stress. The crack initiated from the medial side of the humerus, then gradually grew toward lateral side along the both sides, and finally broke. The medial crack was flat and single like "a thin line", while the lateral fracture of the humerus was irregular and crushed into fragments. The medial humerus cracks were generated by tensile stress, while the lateral cracks were generated by compressive stress. The thickness of humerus diaphysis might be used as the index of fracture risk due to direct readability from clinical images and quantitative relation of fracture risk. This article would provide reference data for the treatment and prevention of humeral surgical neck fracture.

Total talus replacement has been demonstrated to increase ankle instability. However, no studies have explored how to enhance postoperative stability. This study aims to explore the effect of collateral ligament reconstruction on ankle stability by finite element analysis. The results identify that the reconstruction of the posterior talofibular ligament or anterior tibiotalar ligament has little effect on ankle stability. Besides, the reconstruction of the posterior tibiotalar ligament can significantly enhance the eversion stability. Additionally, the traction force of the fibula on the total talar prosthesis after reconstruction of the anterior talofibular ligament can significantly enhance ankle inversion and anterior stability.

The global rise in Crohn's Disease (CD) incidence has intensified diagnostic challenges. This study identified circadian rhythm-related biomarkers for CD using datasets from the GEO database. Differentially expressed genes underwent Weighted Gene Co-Expression Network Analysis, with 49 hub genes intersected from GeneCards data. Diagnostic models were constructed using machine learning algorithms, and biologic therapy efficacy was predicted with advanced regression techniques. Single-cell sequencing showed high gene expression in stem cells, immune, and endothelial cells, with validation confirming significant differences between CD patients and controls. These findings suggest circadian rhythm-related genes as promising diagnostic biomarkers for CD.

This study examines heat transfer and nanofluid-enhanced blood flow behaviour in stenotic arteries under inflammatory conditions, addressing critical challenges in cardiovascular health. The blood, treated as a Newtonian fluid, is augmented with gold nanoparticles to improve thermal conductivity and support drug delivery applications. A hybrid methodology combining finite element method (FEM) for numerical modelling and artificial neural networks (ANN) for stability prediction provides a robust analytical framework. Parametric analysis reveals that increasing stenosis severity (60% to 80%) results in a 45% enhancement in heat transfer, demonstrating the efficacy of nanoparticle integration. The results show that the size of the vortices decreases due to the position changing of the upper stenoses, whereas it rises with increasing stenosis peak. Higher nanoparticle volume fraction ($\varphi$) amplifies momentum diffusion, resulting in larger vortices, while improved thermal conductivity enhances heat transfer. Inflammation significantly affects flow patterns and heat transport with important implications in treating cardiovascular disorders and biological applications. The regression analysis confirms a close match between predicted and target data, showcasing the robustness of the FEM-ANN hybrid approach for modelling biofluid systems.

Bronchial stenosis impacts cough mechanisms and respiratory function. This study used MIMICS and Fluent to construct and simulate a 3D airway model of an elderly female patient with bronchial stenosis. Utilizing dynamic mesh and fluid-structure interaction, airflow during coughing was analyzed, including velocity, wall shear stress, and deformation. The Eulerian wall film model quantified sputum dynamics, revealing that stenosis increases shear stress, exacerbates deformation, and reduces sputum expulsion efficiency, particularly for medium to high viscosity sputum. These findings deepen understanding of bronchial stenosis pathophysiology and offer insights for improving diagnosis, treatment, and prevention of respiratory diseases.

Impeller radial gap is one of important parts within a blood pump, which may affect the hemodynamics and hemocompatibility. In this study, computational fluid dynamics method was performed to evaluate the impact of radial gap sizes. The volume of scalar shear stress decreased with radial gap sizes increasing. On the contrary, the residence time increased with radial gap sizes increasing, especially in the bottom gap. The hemolysis index and platelet activation status at three flow rates decreased with the increase of radial gap sizes. Compared with the hemolysis index when the radial gap size was 0.6 mm, the hemolysis index for the radial gap of 1.0 mm decreased by 27.6%, 25.4% and 21.1% from low flow rate to high flow rate, respectively. Similarly, the platelet activation status for the radial gap of 1.0 mm decreased by 13.0%, 11.5% and 9.1%, respectively. As a novelty, this study revealed that radial gap sizes can significantly influence the blood pump hemocompatibility, especially at low flow rate. In addition, the hemolysis performance can be more affected by radial gaps than that on thrombosis risk.

This paper presents a fractional-order model using the Caputo differential operator to study Ebola Virus Disease (EVD) dynamics, calibrated with Liberian data. The model demonstrates improved accuracy over integer-order counterparts, particularly in capturing behavioral changes during outbreaks. Stability analysis, Lyapunov functions, and a validated numerical method strengthen its mathematical foundation. Simulations highlight its utility in accurately describing EVD evolution and guiding outbreak management. The study underscores the role of behavioral interventions in epidemic control, offering valuable insights for public health and policymaking. This research advances infectious disease models and enhances strategies for mitigating EVD outbreaks.

Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding. Specifically, MBMSNet first extracts multi-view representations from raw EEG signals, followed by independent branches to capture spatial, spectral, temporal-spatial, and temporal-spectral features. Each branch includes a domain-specific convolutional layer, a variance layer, and a temporal attention layer. Finally, the features derived from each branch are concatenated with weights and classified through a fully connected layer. Experiments demonstrate MBMSNet outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% on 2b, and 74.58% on OpenBMI, showcasing its potential for robust BCI applications.

Extrusion-based 3D printing is a widely utilized tool in tissue engineering, offering precise 3D control of bioinks to construct organ-sized biomaterial objects with hierarchically organized cellularized scaffolds. Topological properties in flowing polymers are determined by macromolecule conformation, namely orientation and stretch degree. We utilized the micro-macro approach to describe hydrogel macromolecule orientation during extrusion, offering a two-scale fluid behavior description. Results show that shear rate significantly drives alignment, while the interaction coefficient ($C_i$)captures particle interactions. This approach provides an initial but robust framework to model scaffold anisotropy, enabling optimization of the extrusion process while maintaining computational feasibility.

Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.