Publications by authors named "C V Sruthi"

Three phenazines, 1-methoxyphenazine (1), methyl-6-methoxyphenazine-1-carboxylate (2), 1,6-dimethoxyphenazine (4), and a 2,3-dimethoxy benzamide (3) were isolated from the Streptomyces luteireticuli NIIST-D75, and the antibacterial effects of compounds 1-3, each in combination with ciprofloxacin, were investigated. The in vitro antibacterial activity was assessed by microdilution, checkerboard, and time-kill assay against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Salmonella typhi. According to the checkerboard assay results, each combination of compounds 1, 2 and 3 with ciprofloxacin resulted in a significantly lower minimum inhibitory concentrations (MICs) of 0.

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Endoplasmic reticulum (ER) stress and associated oxidative stress are involved in the genesis and progression of skeletal muscle diseases such as myositis and atrophy or muscle wasting. Targeting the ER stress and associated downstream pathways can aid in the development of better treatment strategies for these diseases with limited therapeutic approaches. There is a growing interest in identifying natural products against ER stress due to the lower toxicity and cost effectiveness.

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Ethnopharmacological Relevance: Dillenia indica L. is an edible plant from the Dilleniaceae family present in the forest of India and other Asian countries. Different parts of this plant are being used in the traditional system of medicines for various diseases like diabetes, indigestion, asthma, jaundice, and rheumatic pain by various rural communities.

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Methylglyoxal (MGO) is a toxic, highly reactive metabolite derived mainly from glucose and amino acids degradation. MGO is also one of the prime precursors for advanced glycation end products formation. The present research was performed to check whether MGO has any role in the promotion of cancer in HepG2 cells.

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Mutational effects predictions continue to improve in accuracy as advanced artificial intelligence (AI) algorithms are trained on exhaustive experimental data. The next natural questions to ask are if it is possible to gain insights into which attribute of the mutation contributes how much to the mutational effects and if one can develop universal rules for mapping the descriptors to mutational effects. In this work, we mainly address the former aspect using a framework of interpretable AI.

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