Computer Methods in Biomechanics and Biomedical Engineering最新文献

The SARS-CoV-2 virus reportedly originated in Wuhan in 2019, causing the coronavirus outbreak (COVID-19), which was technically designated as a global epidemic. Numerous studies have been carried out to diagnose and treat COVID-19 throughout the midst of the disease's spread. However, the genetic similarity between COVID-19 and other types of coronaviruses makes it challenging to differentiate between them. Therefore it's essential to swiftly identify if an epidemic is brought on by a brand-new virus or a well-known disease. In the present article, the DeepCoV deep-learning (DL) approach utilizes layered convolutional neural networks (CNNs) to classify viral serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) besides other viral diseases. Additionally, various motifs linked with SARS-CoV-2 can be located by examining the computational filter processes. In identifying these important motifs, DeepCoV reveals the transparency of CNNs. Experiments were conducted using the 2019nCoVR datasets, and the results indicate that DeepCoV performed more accurately than several benchmark ML models. Additionally, DeepCoV scored its maximum area under the precision-recall curve (AUCPR) and receiver operating characteristic curve (AUC-ROC) at 98.62% and 98.58%, respectively. Overall, these investigations provide strong knowledge of the employment of deep learning (DL) algorithms as a crucial alternative to identifying SARS-CoV-2 and identifying patterns of disease in the SARS-CoV-2 genes.

Wearable technologies such as inertial measurement units (IMUs) can be used to evaluate human gait and improve mobility, but sensor fixation is still a limitation that needs to be addressed. Therefore, aim of this study was to create a machine learning algorithm to predict gait events using a single IMU mimicking the carrying of a smartphone. Fifty-two healthy adults (35 males/17 females) walked on a treadmill at various speeds while carrying a surrogate smartphone in the right hand, front right trouser pocket, and right jacket pocket. Ground-truth gait events (e.g. heel strikes and toe-offs) were determined bilaterally using a gold standard optical motion capture system. The tri-dimensional accelerometer and gyroscope data were segmented in 20-ms windows, which were labelled as containing or not the gait events. A long-short term memory neural network (LSTM-NN) was used to classify the 20-ms windows as containing the heel strike or toe-off for the right or left legs, using 80% of the data for training and 20% of the data for testing. The results demonstrated an overall accuracy of 92% across all phone positions and walking speeds, with a slightly higher accuracy for the right-side predictions (∼94%) when compared to the left side (∼91%). Moreover, we found a median time error <3% of the gait cycle duration across all speeds and positions (∼77 ms). Our results represent a promising first step towards using smartphones for remote gait analysis without requiring IMU fixation, but further research is needed to enhance generalizability and explore real-world deployment.

Horizontal bone loss (HBL) and dehiscence are common supportive tissue defects. This study evaluated the stress distribution in the presence of HBL or dehiscence and two types of fiber posts. Twelve premolars that were endodontically treated (Model-E), restored with conventional (Model-C), and bundle (Model-B) post were modeled. Bone defects were created as control (Model-1), with 4 mm (Model-4) and 8 mm (Model-8) HBL, and dehiscence involving two-thirds of the root (Model-D). HBL was included in all aspects of the models, while dehiscence was confined to the buccal aspect. The models were subjected to a 200 N force, and von Mises stress was analyzed. Model-B1 showed higher stress than Model-C1 but was more homogeneous. In Model-D, the stress was limited to the area without bone and only occurred at the buccal aspect. The highest stress was observed in Model-B8. The presence of a post caused a 2-5.8 times increase in stress. When the crown-root ratio was 1:0.8, stress was in the coronal two-thirds of the root, while at a ratio of 1:0.3, stress was distributed throughout the entire root. Bundle post with 8 mm HBL increased the stress 5.8 times. HBL resulted in stress extending beyond the marginal bone, while dehiscence did not.

The aim of this study was to append a passive soft back exoskeleton to a validated musculoskeletal model and assess its effectiveness in reducing lumbar loads. Fifteen participants lifted a box, with and without wearing a CORFOR^® exoskeleton. A full body OpenSim model was used to estimate lumbar joint moments and reaction forces, as well as low back muscles forces. Wearing the exoskeleton reduced the peak flexion moment, muscles forces, as well as peak compressive and shear forces. This musculoskeletal modelling study shows that wearing the exoskeleton may reduce lumbar spine loads and may contribute to prevent low back disorders.

The 5-methylcytosine (m5C) is a common post-transcriptional RNA methylation modification and is involved in the pathological process of many diseases. However, little is known about the role of m5C in osteoarthritis (OA). OA gene data and the corresponding information were downloaded from the Gene Expression Omnibus database. Based on 36 m5C regulators, we constructed the landscape and diagnostic model for OA. Later, two m5C modification patterns were identified, and functional analyses were performed to evaluate whether these patterns were related to endoplasmic reticulum (ER) stress and mitochondrial autophagy. We further comprehensively analyzed the immune cell infiltration characteristics in different modification patterns in OA. We also established the post-transcriptional regulatory networks and drug-gene networks. Our findings suggested that m5C regulators were differentially expressed between OA and normal samples and could serve as novel biomarkers for the diagnosis of OA. Besides, m5C regulators may be involved in regulating ER stress, mitochondrial autophagy, and immune infiltration in OA. The m5C modification can influence the sensitivity to drugs and the potential post-transcriptional regulatory mechanisms might provide promising targets.

A novel semi-dry electrode that can realize self-replenishment of conductive liquid is proposed in this study. Driving fatigue is detected by extracting the refined composite multiscale fluctuation dispersion entropy (RCMFDE) features in electroencephalogram (EEG) signals collected by this electrode. The results show that the new semi-dry electrode can automatically complete the conductive fluid supplement according to its own humidity conditions, which not only notably improves the effective working time, but also significantly reduces the skin impedance. By comparing with the classical entropy algorithms, the computational speed and the stability of the RCMFDE method are Substantially enhanced.

Purpose: The study aimed to investigate the mechanical effects of the taper position in the abutment hole and the screw taper angles on the implant system and peri-implant tissue using finite element analysis.

Methods: Four taper positions (L1, L2, L3, L4) in the abutment hole were established using 3D software and five screw taper angles (30°, 60°, 90°, 120°, 180°) were set.

Result: Taper position significantly affects the stresses in the implant system. The 30° and 180° angles (L4 position) showed less stress than other angles.

Conclusion: Elevated taper position and reasonable taper angle are beneficial in reducing the stress in the implant system.

High-throughput technologies and machine learning (ML), when applied to a huge pool of medical data such as omics data, result in efficient analysis. Recent research aims to apply and develop ML models to predict a disease well in time using available omics datasets. The present work proposed a framework, 'OmicPredict', deploying a hybrid feature selection method and deep neural network (DNN) model to predict multiple diseases using omics data. The hybrid feature selection method is developed using the Analysis of Variance (ANOVA) technique and firefly algorithm. The OmicPredict framework is applied to three case studies, Alzheimer's disease, Breast cancer, and Coronavirus disease 2019 (COVID-19). In the case study of Alzheimer's disease, the framework predicts patients using GSE33000 and GSE44770 dataset. In the case study of Breast cancer, the framework predicts human epidermal growth factor receptor 2 (HER2) subtype status using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. In the case study of COVID-19, the framework performs patients' classification using GSE157103 dataset. The experimental results show that DNN model achieved an Area Under Curve (AUC) score of 0.949 for the Alzheimer's (GSE33000 and GSE44770) dataset. Furthermore, it achieved an AUC score of 0.987 and 0.989 for breast cancer (METABRIC) and COVID-19 (GSE157103) datasets, respectively, outperforming Random Forest, Naïve Bayes models, and the existing research.

Skin flap transplantation is the most commonly used method to repair tissue defect and cover the wound. In clinic, finite element method is often used to design the pre-operation scheme of flap suture. However, the material parameters of skin flap are uncertain due to experimental errors and differences in body parts. How to consider the influence of material parameter uncertainty on the mechanical response of flap suture in the finite element modeling is an urgent problem to be solved at present. Therefore, the influence of material parameter uncertainty propagation in skin flap suture simulation was studied, Firstly, the geometric model of clinical patient's hand wound was constructed by using reverse modeling technology, the patient's three-dimensional wound was unfolded into a flat surface by using curved surface expansion method, yielding a preliminary design contour for the patient's transplant flap. Based on the acquired patient wound geometry model, the finite element model of flap suture with different fiber orientations and different sizes was constructed in Abaqus, and the uncertainty propagation analysis method based on Monte Carlo simulation combined with surrogate model technology was further used to analyze the stress response of flap suture considering the uncertainty of material parameters. Results showed that the overall stress value was relatively lower when the average fiber orientation was 45°. which could be used as the optimal direction for the flap excision. when the preliminary design contour of the flap was scaled down within 90%, the stress value after flap suturing remained within a safe range.

Low-intensity pulsed ultrasound (LIPUS) is a potential effective means for the prevention and treatment of disuse osteoporosis. In this paper, the effect of LIPUS exposure on the mechanical properties distribution of the osteocyte system (osteocyte body contains nucleus, osteocyte process, and primary cilia) is simulated. The results demonstrate that the mechanical micro-environment of the osteocyte is significantly improved by ultrasound exposure, and the mean von Mises stress of the osteocyte system increases linearly with the excitation sound pressure amplitude. The mechanical effect of LIPUS on osteocytes is enhanced by the stress amplification mechanism of the primary cilia and osteocyte processes.