Browsing by Author "Gupta, M"

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    Integrated network pharmacology and in-silico approaches to decipher the pharmacological mechanism of Selaginella tamariscina in the treatment of non-small cell lung cancer
    (2023-01) Gupta, M; Kumar, S; Kumar, S
    Background and purpose: Non-small cell lung cancer (NSCLC) is a major pathological type of lung cancer and accounts for more than 80% of all cases. In healthcare management, it is challenging to understand the mech anism of NSCLC due to diverse spectra and the limited number of reported data. Selaginella tamariscina is an evergreen perennial plant, hermaphrodite, and used to treat numerous diseases, including NSCLC. In vitro research revealed the therapeutic importance of S. tamariscina in contrast to NSCLC, but the molecular mecha nism is still unclear. In the present study, a network pharmacology technique was employed to uncover the active ingredients, their potential targets, and signaling pathways in S. tamariscina for the treatment of NSCLC. Methods: Putative ingredients of S. tamariscina and significant genes of NSCLC were retrieved from the public database after screening. The overlapped targets among S. tamariscina related compounds and NSCLC were predicted using Venn plot. Following that, a compound-target-disease network was constructed using Cytoscape to decipher the mechanism of S. tamariscina for NSCLC. KEGG pathway and GO enrichment analysis were per formed to investigate the molecular mechanisms and pathways related to S. tamariscina for NSCLC treatments. Lastly, molecular docking and molecular dynamic simulation analysis were performed to validate the interaction that exists between compounds and target proteins. Results: The findings of the current analysis explored the compound–target–pathway network and figured out that Hinokiflavone, Heveaflavone, Neocryptomerin, Isocryptomerin, Apigenin, Sotetsuflavone, and Cryptomerin B decisively contributed to the development of NSCLC by affecting AKT1, EGFR, VEGFA, and GCK3B genes. Later, molecular docking and simulation analysis was conducted to validate the successful activity of the active compounds against potential targets. Lastly, it is concluded that predicted multi-target compounds of S. tamariscina will help in improving the body’s sensitivity to NSCLC by regulating the expression of AKT1, EGFR, VEGFA, and GCK3B, which may act as potential therapeutic targets of NSCLC. Conclusion: Integrated network pharmacology and docking analysis revealed that S. tamariscina exerted a promising preventive effect on NSCLC by acting on diabetes-associated signaling pathways. The current findings propose that AKT1, VEGFA, EGFR, and GSK3B genes are promising and viable therapeutic targets to reduce the incidence of NSCLC, thereby exerting potential therapeutic effects in NSCLC. This approach introduces a groundwork for further research on the protective mechanisms of S. tamariscina for NSCLC and applications of network pharmacology in drug discovery.
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    An Overview of Synthetic Approaches towards 1,2,3-Triazoles
    (2024) Rohila, Y; Sebastian, S; Meenaskhi; Gupta, M
    Nitrogen-containing heterocyclic compounds, including triazoles, play a crucial role as building blocks for essential biomolecules such as nucleotides, amino acids, and more.1 Triazoles are five-membered heteroaromatic ring struc tures with two carbon atoms and three nitrogen atoms, having two isomers: 1,2,3 and 1,2,4. These isomers exhibit a broad range of biological activities, including anticancer, antibacterial, antiviral, anti-inflammatory, antimalarial, and more, making them useful in lead development for drug design and synthesis. With its ease of synthesis in the laboratory, 1,2,3-triazole derivatives have garnered signifi cant attention in the field of organic chemistry.2 Click chemistry, particularly the Rolf Huisgen cycloaddition reac tion3 continues to be a prominent method for the synthesis of 1,2,3-triazole compounds. Copper-catalyzed and copper free versions have been developed to minimize toxicity and enhance the reaction’s efficiency. These methods have al lowed for the synthesis of diverse triazole-containing com pounds with applications in medicinal chemistry. Until the late 1960s, the synthetic route towards 1,2,3-triazoles was limited (Scheme 1).4 But in the late 1970s and early 1980s, after the investigation of the mechanistic approach to this reaction through frontier molecular orbital (FMO) analysis,5 significant momentum was gained in advancing the syn thesis of triazole derivatives. In 2002, Meldal6 and Sharp less7 independently worked and subsequently reported their findings on the copper-catalyzed azide and terminal alkyne [3+2] cycloaddition, a reaction that would later be come renowned as the ‘click reaction’. Further Bertozzi8 has made significant contributions to bioorthogonal chemistry by developing bioorthogonal reactions that allow for the se lective and efficient labeling of biomolecules in complex bi ological systems, advancing the field of chemical biology and enabling new avenues of research in medicine and di agnostics. In acknowledgement of their pioneering efforts in the field of click chemistry, Morten P. Meldal, K. B. Sharp less, and C. R. Bertozzi were collectively awarded the Nobel Prize in 2022 for the development of ‘click’ and ‘bioorthogo nal’ chemistry.
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    Tracing the COVID‑19 spread pattern in India through aGIS‑based spatio‑temporal analysis of interconnected clusters
    (2024) Gupta, M; Sharma, A; Sharma, D; Nirola, M
    Spatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the Indian Council of Medical Research (ICMR). The research investigates temporal patterns during the frst, second, and third waves in India for an informed policy response in case of any present or future pandemics. Given the colossal size of the dataset encompassing the entire nation’s data during the pandemic, a time-bound convenience sampling approach was employed. This approach was carefully designed to ensure a representative sample from advancing timeframes to observe time-based patterns in data. Data were captured from March 2020 to December 2022, with a 5-day interval considered for downloading the data. We employ robust spatial analysis techniques, including the Moran’s I index for spatial correlation assessment and the Getis Ord Gi* statistic for cluster identifcation. It was observed that positive COVID-19 cases in India showed a positive auto correlation from May 2020 till December 2022. Moran’s I index values ranged from 0.11 to 0.39. It signifes a strong trend over the last 3 years with r2 of 0.74 on order 3 polynomial regression. It is expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high risk zones were identifed namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID 19 cases suggests signifcant patterns of interconnectedness between the Indian Railway network, climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as future pandemics for an informed policy response.