Publications by authors named "R Chaube"

Excitation-contraction coupling in skeletal muscle myofibers depends upon Ca release from the sarcoplasmic reticulum through the ryanodine receptor/Ca-release channel RyR1. The RyR1 contains ∼100 Cys thiols of which ∼30 comprise an allosteric network subject to posttranslational modification by S-nitrosylation, S-palmitoylation and S-oxidation. However, the role and function of these modifications is not understood.

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Cystic artery pseudoaneurysm due to acute on chronic cholecystitis is very rare in spite of the high incidence of cholecystitis, and very few cases have been reported in the literature. Most of the pseudoaneurysms are symptomatic at the time of diagnosis due to rupture. Very few cases of unruptured cystic artery pseudoaneurysm caused by cholecystitis have been reported in the literature.

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In Heteropneustes fossilis, kisspeptins (Kiss) and nonapeptides (NPs; vasotocin, Vt; isotocin, Itb; Val8-isotocin, Ita) stimulate the hypothalamus-pituitary-gonadal (HPG) axis, and estrogen feedback modulates the expression of these systems. In this study, functional interactions among these regulatory systems were demonstrated in the brain and ovary at the mRNA expression level. Human KISS1 (hKISS1) and H.

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Zinc-based nanostructures are known for their numerous potential biomedical applications. In this context, the biosynthesis of nanostructures using plant extracts has become a more sustainable and promising alternative to effectively replace conventional chemical methods while avoiding their toxic impact. In this study, following a low-temperature calcination process, a green synthesis of Zn-hydroxide-based nanostructure has been performed using an aqueous extract derived from the leaves of Litchi chinensis, which is employed as a lignocellulose waste biomass known to possess a variety of phytocompounds.

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The pipeline of drug discovery consists of a number of processes; drug-target interaction determination is one of the salient steps among them. Computational prediction of drug-target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug-target interaction prediction, network-centric methods are also evolving.

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