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Record W4206547183 · doi:10.5530/pj.2021.13.211

Molecular Study of Acalypha indica to Leptin, Alpha Glucosidase, and its Antihyperglycemic Effect on Alpha Glucosidase

2021· article· en· W4206547183 on OpenAlexaff
Rani Wardani Hakim, Fadilah Fadilah, Tri Juli Edi Tarigan, Sri Widia A. Jusman, Erni Hernawati Purwaningsih

Bibliographic record

VenuePharmacognosy Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsInnovation Cluster (Canada)
FundersFakultas Kedokteran, Universitas IndonesiaUniversitas Indonesia
KeywordsAlpha-glucosidaseLeptinDocking (animal)ChemistryAlpha (finance)PharmacologyIn vitroBiochemistryEnzymeMedicineObesityInternal medicine

Abstract

fetched live from OpenAlex

Considering the complexity of chronic complications that could be happen in obesity patient, it seems probable that it will be necessary to apply combined treatment, with pathways directed at various types of cells in various stages of the disease process.Acalypha indica L (Ai) has a promising potential to be developed as an herb to treat obesity.Ai comes from the Euphorbiaceae family and taxonomically belongs to the genus Acalypha which is the fourth largest genus in the Euphorbiaceae family.12 This plant can grow to 1.5-2.5mand has empirically been used to treat diabetes and hyperlipidaemia.Most of the international manuscripts on Ai were published from the Indian region because this plant ABSTRACT Introduction: The purpose of this study is to find potential inhibitors of leptin as a proinflammatory adipokine and alpha glucosidase as an enzyme that mediate hyperglycaemia; to alter the chronic complications of obesity from herbal Acalypha indica (Ai).This study was conducted using in silico molecular docking to evaluate the Ai compounds interaction with leptin and alpha glucosidase.The in vitro assay to alpha glucosidase was done to explore antihyperglycemic effect of Ai, as hyperglycaemia is the key process of chronic complication of obesity.Material and Methods: Protein target were leptin and alpha glucosidase; compounds from Ai plant were repundusinic, mauritanin, hesperetin, acaindinin, and glucogalin in pdb format.Molecular docking using autodock vinna.In vitro assay of Ai antihyperglycemic activity was done to alpha glucosidase and was define as IC50 level.Result: The results from the docking analysis demonstrated that compounds from Ai roots contain antihyperglycemic-antiobesity activity which acted by inhibiting leptin and alpha glucosidase receptors.Repundusininc and mauritanin compounds contain hydrogen bond with the greatest leptin enhancer activity on Ser9, Thr35, Glu8, Ser9, Thr25, Gln111, Lys211, Leu7 for repundisinic and Glu8, Thr25, Gly112 and Leu7 for mauritanin.Hesperetin, acaindinin and glucogallin were the most identical compounds with similar affinity binding value to alpha glucosidase.Ai roots was already proven as anti-hyperglycemic-antiobesity which was further confirmed by in vitro assay to alpha glucosidase (IC50 19,429 μg/ml.).Conclusion: The results demonstrated that Ai have anti hyperglycaemic-antiobesity effects and was found to be potentially as antihyperglycemic by in vitro assay to alpha glucosidase.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.331
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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