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Sangeeta Borchetia

Researcher at Department of Biotechnology

Publications -  21
Citations -  216

Sangeeta Borchetia is an academic researcher from Department of Biotechnology. The author has contributed to research in topics: Drought tolerance & Camellia sinensis. The author has an hindex of 5, co-authored 20 publications receiving 173 citations.

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Understanding Darjeeling tea flavour on a molecular basis

TL;DR: The first report on gene expression dynamics in thrips infested Darjeeling tea leaves can be extrapolated with increase in volatiles which is responsible for enhancing the quality of Darjeels tea, specially the flavour and aroma of the infusion.
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Molecular analysis of drought tolerance in tea by cDNA-AFLP based transcript profiling.

TL;DR: The cDNA-AFLP technique allowed genes and transcripts to be identified in the tolerant genotype (TV-23) whose expression is responsive to drought stress and would help in identifying and determining the genetic basis of mechanisms involved in conferring drought tolerance in tea.
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Identification of drought tolerant progenies in tea by gene expression analysis

TL;DR: This work identified a set of drought responsive genes under controlled condition using SSH, and validated the identified genes and their pattern of expression under field drought condition to propose five tolerant progenies that could withstand drought stress and thus are candidates for breeding.
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High multiplication frequency and genetic stability for commercialization of the three varieties of micropropagated tea plants (Camellia spp.)

TL;DR: The results convinced that plants derived from axillary as well as adventitious mode of propagation can be genetically true to type and would help in fast clonal propagation at a commercial scale.
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Black tea bioactives as inhibitors of multiple targets of SARS-CoV-2 (3CLpro, PLpro and RdRp): a virtual screening and molecular dynamic simulation study.

TL;DR: In this article, an in-silico strategy involving ADMET property screening, receptor-ligand docking and molecular dynamic simulation was employed to screen potential tea bio-active inhibitors against three selected targets (RdRp, 3CLpro and PLpro) of SARS-CoV-2.