Computer Methods in Biomechanics and Biomedical Engineering最新文献

This study attempts to develop a novel apoptosis-related predictive model for cervical cancer. Differentially expressed apoptosis-related genes were identified using TCGA, GEO, and MSigDB databases. A 13-gene prognostic model was constructed using multiple regression analyses. The low-risk group exhibited low tumor purity and high ESTIMATE and immune scores. Most of the immune checkpoints in the low-risk group were expressed at higher levels than those in the high-risk group. The low-risk group also had relatively more infiltrating immune cells. An independent prognostic model pertaining to cell apoptosis has been built by this work, which performs well in prediction.

The addition of microstructures to the inner surface of the stent reduces resistance and inhibits the phenomenon of blood adhesion. In this study, the design of a fish-scale microstructured vascular stent was proposed based on bionics, and its main design parameters were optimized using the finite element method. In addition, the hemodynamic effects of a standard stent and a fish-scale microstructured stent on an ideal cerebral aneurysm were comparatively analyzed. The results showed that the fish-scale microstructured stent significantly accelerated intraluminal blood flow velocity by 11.6% compared to the standard stent. In addition, the fish-scale microstructured stent was able to reduce blood flow into the aneurysm lumen by 28.6%.

Ten porcine tibiae were scanned by computed tomography (CT) and the three‑dimensional (3D)-printed navigation template for posterior cruciate ligament (PCL) reconstruction was designed using the Rhinoceros software. The outcomes of the control group and experimental group were obtained from the preoperative design and the navigation template, respectively. This paper focuses on evaluating the accuracy of the 3D-printed navigation template used to assist the anatomical location of tibial tunnel in PCL reconstruction in an in-vitro experimental study.

Prefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast to existing machine learning-based approaches, this generalized RBF-SVM classifier effectively identified observed data with an overall 96.91% accuracy validated with a 10-fold repeated random train test split cross validation technique using confusion matrix analysis.

The accurate prediction of cardiovascular disease (CVD) or heart disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. Many deep learning and machine learning frameworks have been developed recently to predict cardiovascular disease in intelligent healthcare. However, a lack of data-recognized and appropriate prediction methodologies meant that most existing strategies failed to improve cardiovascular disease prediction accuracy. This paper presents an intelligent healthcare framework based on a deep learning model to detect cardiovascular heart disease, motivated by present issues. Initially, the proposed system compiles data on heart disease from multiple publicly accessible data sources. To improve the quality of the dataset, effective pre-processing techniques are used including (i) the interquartile range (IQR) method used to identify and eliminate outliers; (ii) the data standardization technique used to handle missing values; (iii) and the 'K-Means SMOTE' oversampling method is used to address the issue of class imbalance. Using the Enhanced Binary Grasshopper Optimization Algorithm (EBHOA), the dataset's appropriate features are chosen. Finally, the presence and absence of CVD are predicted using the Enhanced MobileNetV2 (EMobileNetV2) model. Training and evaluation of the proposed approach were conducted using the UCI Heart Disease and Framingham Heart Study datasets. We obtained excellent results by comparing the results with the most recent methods. The proposed approach beats the current approaches concerning performance evaluation metrics, according to experimental results. For the UCI Heart Disease dataset, the proposed research achieves a higher accuracy of 98.78%, precision of 99%, recall of 99% and F1 score of 99%. For the Framingham dataset, the proposed research achieves a higher accuracy of 99.39%, precision of 99.50%, recall of 99.50%, and F1 score of 99%. The proposed deep learning-based classification model combined with an effective feature selection technique yielded the best results. This innovative method has the potential to enhance the accuracy and consistency of heart disease prediction, which would be advantageous for clinical practice and patient care.

The aim of this study is to examine the mechanical behavior of different types of composite resins (short fiber-reinforced composite, conventional high-fill hybrid composite and bulk-fill composite) used in the restoration of class II MOD cavities of primary molar teeth by the finite element analysis (FEA). Three three-dimensional tooth models were created in a computer environment. Model 1: tooth model without restoration (control group), Model 2: class II MOD cavity tooth model restored using composite resin (incremental technique), and Model 3: class II MOD cavity tooth model restored using composite resin (bulk technique). Subgroups were formed using the properties of different types of composite resins tested in the class II MOD cavity tooth model. To simulate the average bite force in a child with primary dentition, vertical static loading of 245 N was applied to each of the occlusal contact points of the models. The maximum von Mises stress values were calculated for the models. For all models, the von Mises stress values obtained in enamel were higher than those obtained in dentin. Similar von Mises stress values were obtained in all subgroups of Model 2. The lowest von Mises stress values transmitted to the dental tissues were obtained in Model 3.

Different internal strut architectures affect the biomechanical performance of porous lattice structures. This study aims to investigate these properties under various conditions using different methods.The finite element simulations of tetrahedral microstructures were conducted with varying internal strut thicknesses under different loads. The effective elastic modulus from compression tests aligned with the homogenization results. However, both the number and size of unit cells can influence the modulus at identical porosity levels. Smaller unit cell sizes demonstrated superior mechanical properties while utilizing less material.

Cardiovascular diseases are, according to the World Health Organization, the leading cause of deaths worldwide. A common cardiovascular disease is the coronary artery disease which results in the development of a plaque inside the coronary artery. This process, called atherosclerosis, reduces the gross cross section area of the lumen and creates an enhanced stress distribution in the arterial tissue which can further lead to rupture of the plaque initiating a myocardium infarction and manifesting a sharp angina in the patient. This work aims to analyse the evolution of the response of a sclerotic coronary artery as compared to a healthy artery. A three layered arterial tissue has been modeled in commercial finite element software. The layers were adhered by implementing contact modeling. Next a plaque was introduced in the model that would result in the reduction of the cross section of the lumen by 50 percentage. The plaque was modeled using hydrostatic fluid elements and was assumed to be incompressible. An axisymmetric finite element analysis was carried out for the tissues under a radial pressure. The results portray the evolution of the Von Mises stress, shear stresses as well as contact stresses on the interface between layers for a diseased coronary artery as compared to a healthy one.

The impact of posterior tooth loss on the temporomandibular joint (TMJ) is controversial. Existing studies mainly rely on clinical observation, lacking biomechanical research. This study recruited 10 healthy volunteers (Control group) and 20 patients with posterior tooth loss (PTL group). Three-dimensional maxillofacial models were reconstructed. Finite element analysis showed stress concentration in the PTL group, with stress magnitudes much higher than the Control group, especially on the tooth-missing side. Posterior tooth loss causes asymmetric TMJ stress and higher stress levels, related to symptoms of temporomandibular disorders.

Firefighters carrying self-contained breathing apparatus (SCBA) during training or firefighting may reduce gait stability and increase the risk of injury. Twelve firefighters participated in treadmill gait experiments while carrying SCBA. Multiscale entropy (MSE) and fused pressure region amplitude ratios were used to assess gait stability and pressure distribution. Results showed that as speed increased from 4.5 km/h to 6 km/h, pressure amplitude decreased by 14.65% in the forefoot region and increased by 14.12% in the hindfoot region. MSE showed increased gait complexity at 6 km/h and increased stability at 4.5 km/h. These findings provide insights for potential boot optimization.