Pueblo Community College

About one-fourth of the global population is either overweight or obese, both of which increase the risk of insulin resistance, cardiovascular diseases, and infections. In obesity, both immune cells and adipocytes produce an excess of pro-inflammatory cytokines that may play a significant role in disease progression. In the recent coronavirus disease 2019 (COVID-19) pandemic, important pathological characteristics such as involvement of the renin-angiotensin-aldosterone system, endothelial injury, and pro-inflammatory cytokine release have been shown to be connected with obesity and associated sequelae such as insulin resistance/type 2 diabetes and hypertension. This pathological connection may explain the severity of COVID-19 in patients with metabolic disorders. Many studies have also reported an association between type 2 diabetes and persistent viral infections. Similarly, diabetes favors the growth of various microorganisms including protozoal pathogens as well as opportunistic bacteria and fungi. Furthermore, diabetes is a risk factor for a number of prion-like diseases. There is also an interesting relationship between helminths and type 2 diabetes; helminthiasis may reduce the pro-inflammatory state, but is also associated with type 2 diabetes or even neoplastic processes. Several studies have also documented altered circulating levels of neutrophils, lymphocytes, and monocytes in obesity, which likely modifies vaccine effectiveness. Timely monitoring of inflammatory markers (e.g., C-reactive protein) and energy homeostasis markers (e.g., leptin) could be helpful in preventing many obesity-related diseases.

COVID-19 virus development has been recognized as a broadly perceived kind of disease. This research work has accomplished several deep convolutional networks (DRN) with pre-training techniques for classifying X-ray chest images into three broad collection, viz., normal or negative, pneumonia positive and COVID-19 positive, based on two other open-source data sets. This fivefold research work is based on data sets consisting of 980 X-ray chest images of contaminated COVID-19 patients, along with experimentation using various deep (Wang et al. in A deep learning algorithm using CT images to screen for COVID-19 virus disease (COVID-19), [10]) learning (Farooq and Hafeez in COVID-ResNet: a deep learning framework for screening of COVID-19 from radiographs, [22]) and neural network methods; organized as primarily it introducing some pre-training techniques that helps the network to learn better, especially in an imbalanced data set, where less cases of COVID-19 are seen along with more cases of other classes. Second, proposing a new and modified convolutional (Wang and Wong in COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images, [11]) neural network (MCNN) that is convolutional with the simple application of a filter to an input, which results in activation. Third, repeat the application of the same filter to input entails in a map of activation (viz., feature map), indicates the locations/strength of detected feature in the input; which is an image concatenation of the neural networks and VGG Net networks. Fourth, the computational work reveals that the resulted network achieves the best accuracy by utilizing multiple features extracted by two other robust networks. Fifth, our network evaluation (based on 980 images) reveals that the actual accuracy is surely possible and adaptable in real-life circumstances. At the same time, the average accuracy of our proposed network for detecting COVID-19 cases is 95.60%, with overall average accuracy for all other classes at 82.37%. Finally, a comparative study is to be made with others, in terms of its accuracy, time complexity and high performance and is found to reduce computational cost, by working with large amount of training data which is better than the existing system.KeywordsDetectionClassificationNeural networks (NN)CNNMCNNCOVID-19 virusDWICADImage processingDeep learning (DL)Transfer learningDeep learning feature extractionChest X-ray imagesPrediction

1Professor of nursing emeritus, Texas Woman's University, Denton, TX. 2Nursing instructor in the department of nursing, Pueblo Community College, Pueblo, CO.

Article1 May 1931Scurvy in the Presence of ThyrotoxicosisR. H. KAMPMEIER, M.D., F.A.C.P.R. H. KAMPMEIER, M.D., F.A.C.P.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-4-11-1469 SectionsAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail ExcerptA search of the literature available to me has failed to reveal a case report in which this peculiar association of diseases occurred.The patient whose case is reported below showed a group of symptoms and signs which confused the diagnosis so that, though scurvy was considered at once, the thyrotoxicosis was not appreciated at first.Case ReportCase Report—J. T., a Spaniard aged 52, barber by occupation, entered the Pueblo Clinic on April 5, 1930, and was referred to me by Dr. H. A. Black.Chief Complaint—Diarrhea, fever, and weakness.Present Illness—In December, 1929, the patient had had a... This content is PDF only. To continue reading please click on the PDF icon. Author, Article, and Disclosure InformationAffiliations: Pueblo, Colo.*From the Pueblo Clinic, Pueblo, Colo. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 1 May 1931Volume 4, Issue 11Page: 1469-1471KeywordsFeversSigns and symptoms Issue Published: 1 May 1931 PDF downloadLoading ...