A novel human coronavirus 2 (SARS-CoV-2) is an extremely acute respiratory syndrome which was reported in Wuhan, China in the later half 2019. Most of its primary epidemiological aspects are not appropriately known, which has a direct effect on monitoring, practices and controls. The main objective of this work is to propose a high speed, accurate and highly sensitive CT scan approach for diagnosis of COVID19. The CT scan images display several small patches of shadows and interstitial shifts, particularly in the lung periphery. The proposed method utilizes the ResNet architecture Convolution Neural Network for training the images provided by the CT scan to diagnose the coronavirus-affected patients effectively. By comparing the testing images with the training images, the affected patient is identified accurately. The accuracy and specificity are obtained 95.09% and 81.89%, respectively, on the sample dataset based on CT images without the inclusion of another set of data such as geographical location, population density, etc. Also, the sensitivity is obtained 100% in this method. Based on the results, it is evident that the COVID-19 positive patients can be classified perfectly by using the proposed method. Keywords Coronavirus Á CT scan Á Diagnosis Á Convolution neural network Mathematics Subject Classification 94B10 Á 62M45 Significant Statement 1. This work proposes an expedite, accurate imagining approach to diagnose COVID19. 2. Small shadow patches, interstitial shifts in the lungs due to the virus are traced through CT images and are trained using ResNet architecture in CNN to obtain 95.09% accuracy. 3. When compared with RT-PCR method, this gives high accuracy, sensitivity, specificity.

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Dengue is the most common and widespread arthropod-borne viral infection in the world. It was carried by mosquitoes and this disease used to be called breakbone fever. Dengue is a quite dangerous febrile disease transmitted by aedus aegypti mosquito that can even cause death. In this paper, we proposed new fusion architecture to support the diagnosis of Arbovirus-Dengue. The architecture combines features of platelets and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. Along with these features and platelet count, CBR is incorporated which contains symptoms of the disease and platelet count. Experiments on a set of 10 images yielded a balanced accuracy of 86.95 %. This was a superior diagnosis performance in comparison with the state-of-the-art works.

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Stem cell therapy has begun as a promising and novel approach for the treatment of various diseases. Stem cell therapy involves the identifi cation of correct cells, area of transplantation. Transplantation of functional and healthy stem cells help to renewal of damaged cells and repair injured tissue. Stem cell isolated from bone marrow were the fi rst cell type used in preclinical and clinical investigations for the have been extended to the use of various populations of stem cells. But there are very few studies which show the roles of stem cells in cardiovascular disease. Cardiovascular Disease (CVD) constitutes the most important cause of mortality and morbidity worldwide. CVD represents a group of disorders connected with the defeat of cardiac function. In spite of signifi cant advances in the understanding of the pathophysiological mechanisms of the disease, the problem of cardiac tissue loss has not yet been addressed. Only few therapeutic approaches suggest through tissue repair and regeneration. The most of treatment options aim to control the scar formation and adverse remodeling, while improving myocardial function. Of all the existing therapeutic approaches, the problem of cardiac tissue loss is addressed uniquely by heart transplantation. This review addresses the present state of research as regards stem cell therapy for CVD.