ABSTRACT
Background: Coronavirus (COVID 19) pandemic has taken so many lives and has contributed to an increase in morbidity due to its complications. The research is still in infancy and much has yet to be investigated. Objective: To correlate the HRCT findings on chest with antibody testing in patients visiting tertiary care hospitals in Lahore. Methodology: Three hundred and thirty-two patients with mild, moderate and severe symptoms of COVID 19 were recruited. Those with raised CRP levels were sent for HRCT chest and PCR testing as protocol of the hospitals. All patients were tested with Rapid antibody kits for reactivity. Results: Showed that males were affected more than females. Similarly, non-health care workers were more affected. All patients with bilateral involvement of lungs on HRCT chest and positive PCR findings also tested reactive on antibody testing. However, a few people with bilateral lung involvement and negative on PCR testing got reactive results on Rapid antibody testing. Linear regression model shows significant correlation of HRCT chest findings with Ant-SARC-COV 2 antibodies. Conclusion: HRCT findings (Unilateral and bilateral lung infiltrates) correlated significantly with Anti SARC-COV 2 Antibodies.
ABSTRACT
Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe. Objective: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the COVID-19. Method: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification algorithms. Results: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this purpose. Conclusions: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected patients. © The Author(s) 2022. Published by Higher Education Press.
ABSTRACT
In December 2019, a novel coronavirus crossed species barriers to infect humans and was effectively transmitted from person to person, leading to a worldwide pandemic. Development of effective clinical interventions, including vaccines and antiviral drugs that could prevent or limit theburden or transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health priority. It is thus of utmost importance to assess possible therapeutic strategies against SARS-CoV-2 using experimental models that recapitulate aspects of the human disease. Here, we review available models currently being developed and used to study SARS-CoV-2 infection and highlight their application to screen potential therapeutic approaches, including repurposed antiviral drugs and vaccines. Each identified model provides a valuable insight into SARS-CoV-2 cellular tropism, replication kinetics, and cell damage that could ultimately enhance understanding of SARS-CoV-2 pathogenesis and protective immunity.