Webb Rankin (bagelsnake91)

This proposal intends to achieve a satisfying growth in determining the real cases of COVID-19 and minimize the spreading rate of this virus to the max, ultimately avoiding more deaths.Automatic diagnosis of coronavirus (COVID-19) is studied in this research. Deep learning methods especially convolutional neural networks (CNNs) have shown great success in COVID-19 diagnosis in recent works. But they are efficient when the depth of network is high enough. However, the use of a deep network requires a sufficiently large training set, which is not available in practice. From the other hand, the use of a shallow CNN may not provide superior results because it is not able to rich feature extraction due to lacking enough convolutional layers. To deal with this difficulty, the contextual features reduced by convolutional filters (CFRCF) is proposed in this work. CFRCF extracts shape and textural features as contextual feature maps from the chest X-ray radiographs and abdominal computed tomography (CT) images. Morphological operators, Gabor filter banks and attribute filters are used for contextual feature extraction. Then, two convolutional filters are applied to the contextual feature cube to extract the nonlinear sub-features and hidden relationships among the contextual features. Finally, a fully connected layer is used to produce a reduced feature vector which is fed to a classifier. Support vector machine and random forest are used as classifier. The experimental results show the superior performance of the proposed method from the recognition accuracy and running time point of view using limited training samples. More than 76% and 94% overall classification accuracy is obtained by the proposed method in CT scan and X-ray images datasets, respectively.Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p less then 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 % , a sensitivity of 96.1 % , and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes. To explore the possible mechanism of electroacupuncture to improve insulin sensitivity in type 2 diabetes rats. Fourteen Zucker Diabetic Fatty (ZDF) rats were randomly divided into two groups a model group and an electroacupuncture group, with 7 rats in each group. Seven Zucker Lean (ZL) rats served as a control group. All rats were fed with Purina #5008 for 4 weeks, and the electroacupuncture group received 4-week electroacupuncture intervention, while the control group and model group received no intervention. We measured fasting blood glucose (FBG) on the fourth weekend. After 4 weeks of intervention, the expression levels of insulin receptor substrate-1 (IRS-1) tyrosine phosphorylation, IRS-1 serine/threonine phosphorylation, and GLUT4 in quadriceps femoris muscles were detected by western Blot. Compared with the model group, the electroacupuncture group had a lower level of fasting blood glucose, serum insulin level, and insulin resistance index ( < 0.05). The electroac