Vitiligo is a chronic asymptomatic disorder affecting melanocytes from the basal layer of the epidermis which leads to a patchy loss of skin color. Even though it is one of the neglected disease conditions, people suffering from vitiligo are more prone to psychological disorders. As of now, various studies have been done in order to project auto-immune implications as the root cause. To understand the complexity of vitiligo, we propose the Vitiligo Information Resource (VIRdb) that integrates both the drug-target and systems approach to produce a comprehensive repository entirely devoted to vitiligo, along with curated information at both protein level and gene level along with potential therapeutics leads. These 25,041 natural compounds are curated from Natural Product Activity and Species Source Database. VIRdb is an attempt to accelerate the drug discovery process and laboratory trials for vitiligo through the computationally derived potential drugs. It is an exhaustive resource consisting of 129 differentially expressed genes, which are validated through gene ontology and pathway enrichment analysis. We also report 22 genes through enrichment analysis which are involved in the regulation of epithelial cell differentiation. At the protein level, 40 curated protein target molecules along with their natural hits that are derived through virtual screening. We also demonstrate the utility of the VIRdb by exploring the Protein–Protein Interaction Network and Gene–Gene Interaction Network of the target proteins and differentially expressed genes. For maintaining the quality and standard of the data in the VIRdb, the gold standard in bioinformatics toolkits like Cytoscape, Schrödinger’s GLIDE, along with the server installation of MATLAB, are used for generating results. VIRdb can be accessed through “http://www.vitiligoinfores.com/”.

The outbreak of COVID-19 had spread at a deadly rate since its onset at Wuhan, China and is now spread across 216 countries and has affected more than 6 million people all over the world. The global response throughout the world has been primarily the implementation of lockdown measures, testing and contact tracing to minimise the spread of the disease. The aim of the present study was to predict the COVID-19 prevalence and disease progression rate in Indian scenario in order to provide an analysis that can shed light on comprehending the trends of the outbreak and outline an impression of the epidemiological stage for each state of a diverse country like India. In addition, the forecast of COVID-19 incidence trends of these states can help take safety measures and policy design for this epidemic in the days to come. In order to achieve the same, we have utilized an approach where we test modelling choices of the spatially unambiguous kind, proposed by the wave of infections spreading from the initial slow progression to a higher curve. We have estimated the parameters of an individual state using factors like population density and mobility. The findings can also be used to strategize the testing and quarantine processes to manipulate the spread of the disease in the future. This is especially important for a country like India that has several limitations about healthcare infrastructure, diversity in socioeconomic status, high population density, housing conditions, health care coverage that can be important determinants for the overall impact of the pandemic. The results of our 5-phase model depict a projection of the state wise infections/disease over time. The model can generate live graphs as per the change in the data values as the values are automatically being fetched from the crowd-sourced database.

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