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      In Vivo and in Silico Based Evaluation of Antidiabetic Potential of an Isolated Flavonoid from Allium hookeri in Type 2 Diabetic Rat Model

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          Abstract

          Allium hookeri (F: Liliaceae), an indigenous plant of Manipur, India, is traditionally used to treat various diseases and disorders like diabetes, hypertension, and stomach ache. In our previous study, the methanol extract of the plant showed significant antidiabetic potential in rats. In the present study, we evaluated the antidiabetic potential of a flavonoid compound named MEA isolated from the methanolic leaf extract of A. Hookeri in rats. Additionally, we assessed the compound's mode of action through the molecular docking study. The MEA reduced the blood glucose level from 317±12.8 to 99.4±6.67 mg/dl after 21 days of treatment. Besides, MEA also restored the body weights and other biochemical parameters including lipid profile significantly compared to the diabetic group (p<0.001). The histoarchitecture of the pancreatic tissues of the MEA treated group was also improved compared to the diabetic group. In the docking study, the compound showed good binding affinity in the active binding site of the two structures of pancreatic beta‐cell SUR1 (Sulfonylurea Receptor 1) subunit with CDocker energy −31.556 kcal/mol and −39.703 kcal/mol, respectively. The compound MEA was found to be drug‐like with non‐carcinogenic, non‐mutagenic and non‐irritant properties. These findings indicate the antidiabetic potential of MEA, which might act by modulating the pancreatic beta‐cell SUR1 subunit present in the K ATP channel. Hence, the MEA would be a promising lead molecule to develop new antidiabetic drug candidates of the future.

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          WITHDRAWN: Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition

          To provide global estimates of diabetes prevalence for 2019 and projections for 2030 and 2045.
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            Is Open Access

            Historical review of medicinal plants’ usage

            Healing with medicinal plants is as old as mankind itself. The connection between man and his search for drugs in nature dates from the far past, of which there is ample evidence from various sources: written documents, preserved monuments, and even original plant medicines. Awareness of medicinal plants usage is a result of the many years of struggles against illnesses due to which man learned to pursue drugs in barks, seeds, fruit bodies, and other parts of the plants. Contemporary science has acknowledged their active action, and it has included in modern pharmacotherapy a range of drugs of plant origin, known by ancient civilizations and used throughout the millennia. The knowledge of the development of ideas related to the usage of medicinal plants as well as the evolution of awareness has increased the ability of pharmacists and physicians to respond to the challenges that have emerged with the spreading of professional services in facilitation of man's life.
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              Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm.

              The influence of various factors on the accuracy of protein-ligand docking is examined. The factors investigated include the role of a grid representation of protein-ligand interactions, the initial ligand conformation and orientation, the sampling rate of the energy hyper-surface, and the final minimization. A representative docking method is used to study these factors, namely, CDOCKER, a molecular dynamics (MD) simulated-annealing-based algorithm. A major emphasis in these studies is to compare the relative performance and accuracy of various grid-based approximations to explicit all-atom force field calculations. In these docking studies, the protein is kept rigid while the ligands are treated as fully flexible and a final minimization step is used to refine the docked poses. A docking success rate of 74% is observed when an explicit all-atom representation of the protein (full force field) is used, while a lower accuracy of 66-76% is observed for grid-based methods. All docking experiments considered a 41-member protein-ligand validation set. A significant improvement in accuracy (76 vs. 66%) for the grid-based docking is achieved if the explicit all-atom force field is used in a final minimization step to refine the docking poses. Statistical analysis shows that even lower-accuracy grid-based energy representations can be effectively used when followed with full force field minimization. The results of these grid-based protocols are statistically indistinguishable from the detailed atomic dockings and provide up to a sixfold reduction in computation time. For the test case examined here, improving the docking accuracy did not necessarily enhance the ability to estimate binding affinities using the docked structures. Copyright 2003 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Chemistry & Biodiversity
                Chemistry & Biodiversity
                Wiley
                1612-1872
                1612-1880
                January 2024
                December 04 2023
                January 2024
                : 21
                : 1
                Affiliations
                [1 ] Escent Institute of Pharmaceutical Sciences and Research Imphal 795140 Manipur India
                [2 ] Department of Pharmaceutical Sciences Faculty of Science and Engineering Dibrugarh University Dibrugarh 786004 Assam India
                [3 ] Department of Biotechnology Royal School of Bio-Sciences Royal Global University Guwahati 781035 India
                [4 ] Department of Pharmaceutical Sciences School of Biotechnology and Pharmaceutical Sciences Vignan's Foundation for Science Technology &amp; Research (Deemed to be University) Guntur 522213 Andhra Pradesh India
                Article
                10.1002/cbdv.202301299
                d7bd6c11-7a4c-4d92-acf6-db475f647d49
                © 2024

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