Mild COVID- 19 infection: Do we have any effective drugs? A narrative review

Coronavirus disease (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The clinical spectrum of SARS-CoV-2 infection ranges from asymptomatic infection to critical illness. About 80% of COVID-19 infections are mild or asymptomatic, 15% are associated with severe infection requiring oxygen and 5% are critical infections, requiring ventilation support. Patients with mild illness usually recover at home, with supportive care and isolation. But most of the mild COVID-19 patients have been prescribed off-label medication such as Hydroxychloroquine, Azithromycin, Ivermectin, Doxycycline, Favirapivir, Vitamin C, Vitamin D, an oral and inhaled corticosteroid. Literature showed that the sale of all these medications increased in this pandemic The newer cocktail regimen which is a combination of monoclonal antibody Bamlanivimab and Etesevimab showed very promising results. The newer oral antiviral drug Molnupiravir is also showing very good efficacy in terms of reduction in hospitalization and death. This narrative review looked at evidence regarding each drug for its effect on recovery time, viral clearance, and the risk of progression or hospitalization. Among all these, Ivermectin only shows a promising result. The newer cocktail antibody and antiviral drug Molnupiravir is effective in reducing hospitalization and may be a game changer for the patient with mild-moderate COVID-19 infection. There is a lack of evidence for the use of other drugs in the mild case of COVID 19. Only symptomatic treatment with antipyretic with hydration is enough to combat mild COVID-19 infection. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.

Covid-19 Identification with Chest X-Ray Images merging Handcrafted and Automated Features for Enhanced Feature Generalization

Outbreak of COVID-19 pandemic has imposed a major threat to the existence of human lives. High rate of infection due to the lethal virus has caused major sufferings and premature deaths to many budding possibilities. Nevertheless, quick identification of the disease can lead to faster recovery with medical help. Unfortunately, the ratio of COVID infection to methods of early medical identification of disease is quite feeble. This has motivated researchers in designing computer aided diagnosis (CAD) systems which can identify the disease from easily available chest X-Ray images of patients. However, the success rate of identification of COVID cases is not satisfactory since the proposed systems are emphasizing on identifying pneumonia rather than separating the cause of it as COVID or non COVID. In this paper, the authors have investigated the cause of such misclassification and have identified feature generalization as one of the reasons. A feature fusion based approach is proposed by the authors to encourage generalization of input features to CAD for better identification of COVID 19. The reason for proposing the fusion based feature formation is encompassing diverse X-Ray image information by extracting descriptors with assorted techniques and uniting them with fusion. The classification results with fusion based approach have reported substantial improvements compared to individual techniques. This in turn indicates better generalization of input features with feature fusion.