Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Structural Basis for the Understanding of Entry Inhibitors against SARS Viruses.

Outbreaks due to Severe Acute Respiratory Syndrome-Corona virus 2 (SARSCoV- 2) initiated in Wuhan city, China, in December 2019 and continued to spread Internationally, posing a pandemic threat as declared by WHO and as of March 10, 2021, confirmed cases reached 118 million along with 2.6 million deaths worldwide. In the absence of specific antiviral medication, symptomatic treatment and physical isolation remain the options to control the disease and contagion. The recent clinical trials on antiviral drugs highlighted some promising compounds such as umifenovir (haemagglutininand has only 70% similarity to SAmediated fusion inhibitor), remdesivir (RdRp nucleoside inhibitor), and favipiravir (RdRp Inhibitor). WHO launched a multinational clinical trial on several promising analogs as a potential treatment to combat SARS infection. This situation urges a holistic approach to invent safe and specific drugs as a prophylactic and therapeutic cure for SARS-related viral diseases, including COVID-19. It is significant to note that researchers worldwide have been doing their best to handle the crisis and have produced an extensive and promising literature body. It opens a scope and allows understanding the viral entry at the molecular level. A structure-based approach can reveal the molecular-level understanding of viral entry interaction. The ligand profiling and non-covalent interactions among participating amino-acid residues are critical information to delineate a structural interpretation. The structural investigation of SARS virus entry into host cells will reveal the possible strategy for designing drugs like entry inhibitors. The structure-based approach demonstrates details at the 3D molecular level. It shows specificity about SARS-CoV-2 spike interaction, which uses human angiotensin-converting enzyme 2 (ACE2) as a receptor for entry, and the human protease completes the process of viral fusion and infection. The 3D structural studies reveal the existence of two units, namely S1 and S2. S1 is called a receptor-binding domain (RBD) and responsible for interacting with the host (ACE2), and the S2 unit participates in the fusion of viral and cellular membranes. TMPRSS2 mediates the cleavage at the S1/S2 subunit interface in the S-protein of SARS CoV-2, leading to viral fusion. Conformational difference associated with S1 binding alters ACE2 interaction and inhibits viral fusion. Overall, the detailed 3D structural studies help understand the 3D structural basis of interaction between viruses with host factors and open scope for the new drug discovery process targeting SARS-related virus entry into the host cell.