Host‑dependent molecular factors mediating SARS‑CoV‑2 infection

Gowhar Shaf · Shruti Desai· Krithika Srinivasan · Aarthi Ramesh · Rupesh Chaturvedi · Mohan Uttarwar

Received: 14 October 2020 / Accepted: 9 March 2021
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Abstract
Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2, and clinically characterized by common symptoms
including fever, dry cough, loss of taste/smell, myalgia, and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demands more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in the rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from this evidence, we perceive that an initial rapid screening using an integrated AI approach could be a way forward in efficient patient stratification.

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