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A Comprehensive review of Medicinal Plant Database, Drug Discovery, and Model Organism Databases: Insights into Biomedical Research

by Menaka Karuppannan, Swathi Sakthivel and Gnanendra Shanmugam
Department of Biotechnology, Vivekanandha College of arts and Sciences for Women (Autonomous),
Elayampalayam, Tiruchengode, Tamilnadu, India

Abstract : In this review, our focus is on biomedical research database that are reported housed mostly by the National institute of Health of the United States .This review presents an overview of three critical domains in biomedical research: medicinal plant databases, drug discovery databases, and model organism databases. By compiling these sources, we aimed to put forth the relevant resources that are key in drug discovery and development. Our focus includes comprehensive searches across multiple databases, including Medicinal Plant Database, Drug Discovery, and Model Organism Databases. Moreover, our investigation on 11 Model organism databases provides valuable insights into drug efficacy, toxicity, and translational research. Medicinal plant databases provide information about classifications, activities, phytochemicals, test targets and structure of phytochemicals in different formats. Drug and drug discovery databases help researchers find and develop new medications by providing organized and accessible information about existing drugs and potential candidates for further study. Brief descriptions of each Database, as well as details including data source, type, study model, availability of access are provided. Furthermore, the entire databases reported here are continuously updating databases according to their user feedbacks and with advancing technologies. Thus, this review highlights how combining updated information from medicinal plants, drug discovery, and model organism databases helps the researchers to make faster progress in medical research and developing treatments.

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Comparative Genomics of SARS-CoV and SARS-CoV-2 Using Fuzzy Sets: Insights into Viral Therapeutic Implications

by Tamil Bharathi Viswanathan¹, Anitha Devi Dinakaran² and Gnanendra Shanmugam^1
1Department of Biotechnology, Vivekanandha College of arts and Sciences for Women (Autonomous),
Elayampalayam, Tiruchengode, Tamilnadu, India
²Department of Mathematics, Shri Sakthikailassh Women’s College, Salem, Tamilnadu, India

Abstract : Severe acute respiratory syndrome coronavirus (SARS-CoV) and the 2019 novel coronavirus (SARS-CoV-2) are highly infectious pathogens that primarily affect the human respiratory system, causing a range of respiratory illnesses from mild to severe. The SARS-CoV outbreak in 2003 resulted in significant global fatalities, while the ongoing COVID-19 pandemic, caused by SARS-CoV-2, has led to millions of deaths worldwide and has had profound impacts on healthcare systems and the global economy. Genomic analysis plays a crucial role in understanding these viruses and developing effective therapies. In this commentary, we discuss the use of genomic data, including high-throughput sequencing and gene expression analysis, in drug development for COVID-19. We highlight the similarities between the genomes of SARS-CoV and SARS-CoV-2, using fuzzy logic to estimate their distance. Our analysis indicates considerable similarity between the two genomes, suggesting potential commonalities in drug targets. The comparison of these genomes demonstrates the importance of genomics in understanding viral pathogenicity and developing targeted therapies. The use of fuzzy logic in genomic analysis provides a valuable tool for comparing genetic sequences and identifying potential drug targets. Continued research in genomics and bioinformatics is essential for combating current and future viral outbreaks