COVID-19 mainly causes the collapse of the pulmonary system thereby causing a dearth of oxygen in the human body. Patients infected with this viral disease have been reported to experience various signs and symptoms associated with brain dysfunction, from the feeling of vagueness to loss of smell and taste to severe strokes. These neurological problems have been reported by younger COVID-19 infected patients mainly in their thirties and forties. Various researchers from around the globe have discerned numerous other brain dysfunctions, such as headache, dizziness, numbness, major depressive disorder, anosmia, encephalitis, febrile seizures, and Guillain-Barre syndrome. The involvement of the CNS by this viral infection has been predicted to be for a longer period of time, even if the patient recovers from COVID-19. The neuronal cell damage caused by COVID-19 is a potent factor responsible for cognitive, behavioral, and psychological problems among its sufferers. The hypoxic conditions can also trigger the formation of beta-amyloid plaques and tau-tangles and thus the virus can even induce Alzheimer’s in patients in the near future. The virus affects the brain directly, thereby causing encephalitis. This pandemic has also been shown to have a negative psychological toll on people. This research aims to highlight the brain dysfunction associated with the ACE2 receptor that is known to be a crucial player in the COVID-19 pandemic using genetic networking approaches. Furthermore, we have identified herbal drug candidates that bind to the ACE2 receptor in order to identify potential treatments for the neurological manifestations of COVID-19.

The severe acute respiratory syndrome coronavirus 2 that causes coronavirus disease 2019 (COVID-19) disrupted the normal functioning throughout the world since early 2020 and it continues to do so. Nonetheless, the global pandemic was taken up as a challenge by researchers across the globe to discover an effective cure, either in the form of a drug or vaccine. This resulted in an unprecedented surge of experimental and computational data and publications, which often translated their findings in the form of databases (DBs) and tools. Over 160 such DBs and more than 80 software tools were developed, which are uncharacterized, unannotated, deployed at different universal resource locators and are challenging to reach out through a normal web search. Besides, most of the DBs/tools are present on preprints and are either underutilized or unrecognized because of their inability to make it to top Google search hits. Henceforth, there was a need to crawl and characterize these DBs and create a compendium for easy referencing. The current article is one such concerted effort in this direction to create a COVID-19 resource compendium (COVIDium) that would facilitate the researchers to find suitable DBs and tools for their research studies. COVIDium tries to classify the DBs and tools into 11 broad categories for quick navigation. It also provides end-users some generic hit terms to filter the DB entries for quick access to the resources. Additionally, the DB provides Tracker Dashboard, Neuro Resources, references to COVID-19 datasets and protein–protein interactions. This compendium will be periodically updated to accommodate new resources. Database URL: The COVIDium is accessible through http://kraza.in/covidium/

Background: Gastric cancer has been ranked the third leading cause of cancer death worldwide. Its detection at the early stage is difficult because patients mostly experience vague and non-specific symptoms in the early stages. Methods: The RNA-seq datasets of both gastric cancer and normal samples were considered and processed. The obtained differentially expressed genes were then subjected to functional enrichment analysis and pathway analysis. An implicit atomistic molecular dynamics simulation was executed on the selected protein receptor for 50 ns. The electrostatics, surface potential, radius of gyration, and macromolecular energy frustration landscape were computed. Results: We obtained a large number of DEGs; most of them were down-regulated, while few were up-regulated. A DAVID analysis showed that most of the genes were prominent in the KEGG and Reactome pathways. The most prominent GAD disease classes were cancer, metabolic, chemdependency, and infection. After an implicit atomistic molecular dynamics simulation, we observed that DLC1 is electrostatically optimized, stable, and has a reliable energy frustration landscape, with only a few maximum energy frustrations in the loop regions. It has a good functional and binding affinity mechanism. Conclusions: Our study revealed that DLC1 could be used as a potential druggable target for specific subsets of gastric cancer.