Publications by authors named "Nandan Dixit"

Article Synopsis
  • Aurora kinases A and B are vital for cell division, and their abnormal activity can lead to cancer; thus, inhibitors targeting these kinases are being researched as potential cancer treatments.
  • The study highlights a new compound, 6k, which showed strong cytotoxic effects against HeLa and HepG2 cancer cells, specifically inhibiting Aurora kinases A and B at very low concentrations.
  • 6k was found to induce cell cycle arrest at the G2/M phase, increase signs of apoptosis, and demonstrated promising drug-like properties, suggesting it may be a candidate for further testing in cancer therapy.
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HepatoCellular Carcinoma, being one of the most mortally convoluted malignancy with mounting number of occurrences across the world and being classified as the third most prevalent cause of cancer-associated mortalities and sixth most prevalent neoplasia. The active phytoconstituent andrographolide, derived from is conveyed to reconcile a number of human ailments including various oncologies. However, the molecular mechanism underlying the anti-oncogenic effects of Andrographolide on HCC remains skeptical and unclear, emerging as a budding challenge for researchers and oncologists.

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Cross-species post-transcriptional regulatory potential of plant derived small non-coding microRNAs (miRNAs) has been well documented by plenteous studies. MicroRNAs are transferred to host cells via oral ingestion wherein they play a decisive role in regulation of host genes; thus, miRNAs have evolved as the nascent bioactive molecules imparting pharmacological values to traditionally used medicinal plants. The present study aims to investigate small RNA profiling in order to uncover the potential regulatory role of miRNAs derived from Andrographis paniculata, one of the most widely used herb by tribal communities for liver disorders and document the pharmacological properties of A.

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Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity.

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