Computer Methods in Biomechanics and Biomedical Engineering最新文献

Distraction osteogenesis (DO) is a bone regenerative maneuver, which is conventionally done with external fixators and, more recently, with telescopic intramedullary nails. Despite the proven effectiveness, external approaches are intrusive to the patient's life while intramedullary nailing damages the growth plates, making them unsuitable for pediatric patients. An internal DO plate fixator (IDOPF) was developed for pediatric patients to address these limitations. The objective of this study was to test the hypothesis that the IDOPF can withstand a partial weight bearing scenario and create a favorable mechanical microenvironment at the osteotomy gap for bone regeneration as the device elongates. A finite element model of a surrogated long bone diaphysis osteotomy fixation by means of the IDOPF was created and subjected to axial compression, bending and torsion. As the osteotomy gap increased from 2 mm to 20 mm, under compression, The average axial interfragmentary strains decreased from 2.33% to 0.35%. Stress increased from 179 MPa to 281 MPa at the contact interfaces of the telescopic compartments, which exceeded the endurance limit of stainless steel (270 MPa) but was below its yield limit (415 MPa). These results demonstrate, that the IDOPF can withstand a partial load bearing scenario and provide a stable biomechanical environment conductive to bone healing. However, high contact stresses at the telescopic interfaces of the device are likely to cause wear, as is frequently reported in telescopic fixators. This study is a step towards refining the IDOPF design for clinical use.

We investigated the effect of anterior disc displacement without osteoarthritis (ADDwoOA) on the morphology of the temporomandibular joint (TMJ) utilizing three-dimensional (3D) models of 23 asymptomatic individuals and 30 ADDwoOA patients. Statistical analyses between the groups were performed by measuring 10 morphological parameters. ADDwoOA patients showed significantly decreased levels of the sagittal ramus angle (SRA) and joint spaces compared with asymptomatic subjects. Moreover, the patients who had recovered exhibited normal joint spaces levels. Consequently, ADDwoOA caused the condyles to move backward and upward, approaching the articular fossa. Joint spaces can serve as an important observation during the treatment of ADD.

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.

In this work, a novel framework is proposed which includes Hjorth parameters as features from time and time-frequency domain (Multi-Domain) and attention-enhanced temporal modeling, to classify epileptic seizure stages, namely normal, inter-ictal, and ictal. Three different approaches are compared, i.e. Hjorth parameters in time domain, time-frequency domain, and multi-domain. In time-frequency domain, Hjorth parameters are derived from the wavelet coefficients obtained using Discrete Wavelet Transform (DWT). The extracted features are then fed to a 1D Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and attention mechanism. The performance of the proposed framework is evaluated on Bonn EEG dataset using different performance evaluation metrics namely precision, recall, F1-score, and accuracy. The binary, three-class, and five-class seizure classification are examined using the proposed framework. The validation of the model is performed through the 10-fold cross-validation with sample level partitioning. Experimental findings show that the proposed framework with multi-domain features has given outstanding performance with 98.40, 98.00, and 85.40% test classification accuracy for binary, three-class, and five-class discrimination, respectively.

Endoscopic nasopharyngectomy represents a significant intervention for recurrent nasopharyngeal carcinoma (NPC). Various surgical techniques, including transnasal and transoral approaches, are employed. However, the impact of these procedures on nasal airflow dynamics is not well understood. This computational fluid dynamics (CFD) study aimed to investigate alterations in nasal airflow and air conditioning following endoscopic nasopharyngectomy. A 55-year-old male patient with recurrent NPC was selected, whose CT data were utilized for image reconstruction. A preoperative model and two postoperative models, including the transnasal and transoral approach models, were established. The airflow patterns and various CFD parameters were analyzed. In the postoperative models, the high-speed airflow went along the soft palate and into the nasopharyngeal outlet, and there was the low-speed turbulence in the expanded nasopharyngeal cavity. Compared to the preoperative model, the postoperative models exhibited reductions in surface-to-volume ratio, nasal resistance, airflow velocity and proportion of high wall shear stress regions in nasopharynx. The changing trends of nasopharyngeal air temperature and humidity in the preoperative and transoral models were consistent. The heating and humidification efficiency decreased in the transnasal model compared to the transoral model. The endoscopic nasopharyngectomy for recurrent NPC affects the nasal airflow and warming and humidification function. The transoral approach has less influence on aerodynamics of the upper airway compared to the transnasal approach. From a CFD perspective, the endoscopic nasopharyngectomy does not increase the risk of postoperative complications, including the empty nose syndrome and the carotid blowout syndrome.

This study proposes a human motion measurement model combining molecular chain conformation with a silicone rubber strain sensor embedded with carbon nanotubes to enhance signal response stability. An improved least mean square algorithm is used to optimize signal processing. Experimental results show the model achieves 95.12% measurement accuracy, 92.45% F1 score, 35.14 dB SNR, and 60.45 ms latency. Across different age groups and motion states such as gait, running, and jumping, the average detection error remains below 3%, and physiological monitoring errors for heart rate and oxygen saturation are as low as 0.42. The model operates stably in dynamic conditions.

Accurate detection of exercise fatigue based on physiological signals is vital for reasonable physical activity. Existing studies utilize widely Electrocardiogram (ECG) signals to achieve exercise monitoring. Nevertheless, ECG signals may be corrupted because of sweat or loose connection. As a non-invasive technique, Phonocardiogram (PCG) signals have a strong ability to reflect the Cardiovascular information, which is closely related to physical state. Therefore, a novel PCG-based detection method is proposed, where the feature fusion of deep learning features and linear features is the key technology of improving fatigue detection performance. Specifically, Short-Time Fourier Transform (STFT) is employed to convert 1D PCG signals into 2D images, and images are fed into the pre-trained convolutional neural network (VGG-16) for learning. Then, the fusion features are constructed by concatenating the VGG-16 output features and PCG linear features. Finally, the concatenated features are sent to Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) to distinguish six levels of exercise fatigue. The experimental results of two datasets show that the best performance of the proposed method achieves 91.47% and 99.00% accuracy, 91.49% and 99.09% F1-score, 90.99% and 99.07% sensitivity, which has comparable performance to an ECG-based system which is as gold standard (94.32% accuracy, 94.33% F1-score, 94.52% sensitivity).

Machine learning (ML) has been used to predict lower extremity joint torques from joint angles and surface electromyography (sEMG) signals. This study trained three bidirectional Long Short-Term Memory (LSTM) models, which utilize joint angle, sEMG, and combined modalities as inputs, using a publicly accessible dataset to estimate joint torques during normal walking and assessed the performance of models, that used specific inputs independently plus the accuracy of the joint-specific torque prediction. The performance of each model was evaluated using normalized root mean square error (nRMSE) and Pearson correlation coefficient (PCC). Each model's median scores for the PCC and nRMSE values were highly convergent and the bulk of the mean nRMSE values of all joints were less than 10%. The ankle joint torque was the most successfully predicted output, having a mean nRMSE of less than 9% for all models. The knee joint torque prediction has reached the highest accuracy with a mean nRMSE of 11% and the hip joint torque prediction of 10%. The PCC values of each model were significantly high and remarkably comparable for the ankle (∼ 0.98), knee (∼ 0.92), and hip (∼ 0.95) joints. The model obtained significantly close accuracy with single and combined input modalities, indicating that one of either input may be sufficient for predicting the torque of a particular joint, obviating the need for the other in certain contexts.

The simulation of the aortic valve (AV) remains challenging due to its geometric complexity and the multi-physics nature of the problem. In this study, we utilized COMSOL to establish a three-dimensional, three-leaflet AV fluid-structure interaction model and investigated the influence of material properties on the valve's mechanical behavior in a healthy state. The results indicated that variations in the aortic wall material model had a minor impact on AV hemodynamics. Additionally, while the linear elastic properties of the leaflets limit valve opening and closing, this material model allows for rapid assessment of AV performance within the range of material deformation.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. There are currently no effective interventions to slow down or prevent the occurrence and progression of AD. Neutrophil extracellular traps (NETs) have been proven to be tightly linked to AD. This project attempted to identify hub genes for AD based on NETs. Gene expression profiles of the training set and validation set were downloaded from the Gene Expression Omnibus (GEO) database, including non-demented (ND) controls and AD samples. NET-related genes (NETRGs) were collected from the literature. Differential analysis identified 21 AD differentially expressed NETRGs (AD-DE-NETRGs) majorly linked to functions such as defense response to bacterium as well as pathways including IL-17 signaling pathway, as evidenced by enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). Their diagnostic ability was validated in the validation set using receiver operating characteristic (ROC) curves and gene differential expression analysis. A total of 16 miRNAs and 132 lncRNAs were predicted through the mirDIP and ENCORI databases, and a lncRNA-miRNA-mRNA regulatory network was constructed using Cytoscape software. Small molecular compounds such as Benzo(a)pyrene and Copper Sulfate were predicted to target hub genes using the CTD database. This project successfully identified five hub genes, which may serve as potential biomarkers for AD, proffering clues for new therapeutic targets.