Computer Methods in Biomechanics and Biomedical Engineering最新文献

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.

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).

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.

The global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD $I{L}_2$ Z, is developed for the early detection and treatment of lung cancer. This model incorporates $I{L}_2$ cytokine and anti-PD-L1 inhibitors, enhancing the immune system's anticancer response within five epidemiological compartments. The TCD $I{L}_2$Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number ($R_0$), stability, and sensitivity analysis. Next-generation techniques based on $R_0$ that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with $I{L}_2$ and anti-PD-L1 inhibitors admiring the Lyapunov functions' applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler's method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with $I{L}_2$ and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes.

ADHD is a prevalent childhood behavioral problem. Early ADHD identification is essential towards addressing the disorder and minimizing its negative impact on school, career, relationships, as well as general well-being. The present ADHD diagnosis relies primarily on an emotional assessment which can be readily influenced by clinical expertise and lacks a basis of objective markers. In this paper, an innovative IoT based ADHD detection is proposed using an EEG signal. To the input EEG signal, the min-max normalization technique is processed. Features are extracted as the subsequent step, where improved fuzzy feature, in which the entropy is estimated to increase the effectiveness of recognizing the vector along with, fractal dimension, wavelet transform and non-linear features are extracted. Also, proposes the new hybrid PUDMO algorithm to select the optimal features from the extracted feature set. Subsequently, the selected features are fed to the proposed hybrid detection system that including IDBN and LSTM classifier to detect whether it is ADHD or not. Further, the weights of both classifiers are tuned optimally as per the hybrid PUDMO algorithm to enhance the detection performance. The PUDMO achieved an accuracy of 0.9649 in the best statistical metric, compared to the SLO's 0.8266, SOA's 0.8201, SMA's 0.8060, BRO's 0.8563, DE's 0.8083, POA's 0.8537, and DMOA's 0.8647, respectively. Thus, the assessments and detection help the clinicians to take appropriate decision.