Corona virus disease 2019 (COVID-19) is caused by a Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2), which is a positive strand RNA virus. The SARS-CoV-2 genome and its association to SAR-CoV-1 vary from ca. 66% to 96% depending on the type of betacoronavirdeae family members. With several drugs, viz. chloroquine, hydroxychloroquine, ivermectin, artemisinin, remdesivir, azithromycin considered for clinical trials, there has been an inherent need to find distinctive antiviral mechanisms of these drugs. Curcumin, a natural bioactive molecule has been shown to have a therapeutic potential for various diseases, but its effect on COVID-19 has not been explored. In this study, we show the binding potential of curcumin targeted to a variety of SARS-CoV-2 proteins, viz. spike glycoproteins (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17) along with nsp10 (PDB ID: 6W4H) and RNA dependent RNA polymerase (PDB ID: 6M71) structures. Our results indicate that curcumin has high binding affinity towards nucleocapsid and nsp 10 proteins with potential antiviral activity.

Delayed cancer detection is one of the common causes of poor prognosis in case of many cancers including the cancers of the oral cavity. Despite improvement and development of new and efficient gene therapy treatments, very little has been done to algorithmically assess the impedance of these carcinomas. In this work, we attempt to annotate viable attributes in oral cancer gene datasets for identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated to other forms of oral cancer detection.