Antidiabetic activities of aerial part of Asparagus racemosus Willd. extract: an in vitro, in vivo, and in silico approach
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Priya Das
,
Paramita Paul
Abstract
Asparagus racemosus Willd., a significant medicinal plant in India’s Darjeeling Himalayan region. The current study aims to assess the antidiabetic efficacy of the aerial part of Asparagus racemosus Willd. utilizing a streptozotocin-nicotinamide-induced diabetic rat model. The methanolic extract of plant (ARME) was evaluated for in vitro antidiabetic activity through α-glucosidase and α-amylase enzyme suppression assay and assessed in vivo in a streptozotocin-induced rat model. A 21-day chronic multiple-dose study of ARME was examined. STZ-induced diabetic rats were treated with metformin (70 mg/kg) and ARME at concentrations of 200 and 400 mg/kg. It was discovered that ARME inhibited the activities of α-amylase and α-glucosidase and showed potent therapeutic effects on the rat’s model. ARME helps raise body weight, hemoglobin, and other blood counts and normalizes the serum parameters in the diabetic group. In addition, liver tissues’ abnormal levels of the antioxidant enzymes glutathione and lipid peroxides were returned to those of control animals. HPTLC study revealed the presence of stigmasterol, which showed a strong affinity for the proteins linked to diabetes mellitus through in silico study. As a result of these findings, ARME inhibits the activity of digestive enzymes and has antioxidant and antihyperglycemic effects in STZ-nicotinamide-induced diabetic rats.
Funding source: University of North Bengal
Award Identifier / Grant number: 2323/R-2022
Funding source: Science and Engineering Research Board
Award Identifier / Grant number: SRG/2019/001910
Funding source: University Grants Committee
Award Identifier / Grant number: No. F. 587 30-515/2020(BSR);12/02/2020
Acknowledgments
We are grateful to the University of North Bengal for providing facilities for research.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: The financial support of SERB-SRG (file no. SRG/2019/001910), India, UGC-SRG [No. F. 30-515/2020(BSR);12/02/2020], India and the University assistance/support for the research project (Ref. No. 2323/R-2022), University of North Bengal, India are greatly acknowledged.
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Data availability: Not applicable.
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