Red ginger confers antioxidant activity, inhibits lipid and sugar metabolic enzymes, and downregulates miR-21/132 expression
Bibliographic record
Abstract
Ginger is a spice and medicinal plant with several varieties. This study aimed to understand the antioxidant, antidiabetic, and antiobesity properties of red ginger (RG) ( Zingiber officinale var. rubrum ), through pharmacoinformatics coupled with in vitro studies. Additionally, the suppression of miR-21/132 expression by RG was studied. Two RG extracts were sequentially produced using hexane (RGH) and ethanol (RGE) and characterized using UHPLC-Q-Orbitrap HRMS-based untargeted metabolomics analysis. Seven compounds identified in RGE and six in RGH were subjected to molecular docking tests on iNOS, lipase, α-glucosidase, α-amylase, and FTO protein receptors. Overall, 5,7-dihydroxy-2-(4-hydroxyphenyl)-6,8-bis(3,4,5-trihydroxyoxan-2-yl)-4H-chromen-4-one and pheophorbide A from RGE, and nictoflorin and rutin from RGH showed superior binding to most receptors. In vitro studies confirmed the ability of both RGE and RGH extracts to scavenge DPPH and ABTS radicals; inhibit activities of three metabolic enzymes, lipase (EC 50 85.58 and 105.50 μg/mL), α-glucosidase (EC 50 of 92.56 and 106.20 μg/mL), and α-amylase (EC 50 of 96.60 and 111.80 μg/mL). Ex vivo RGE and RGH considerably suppressed protein expression associated with obesity, diabetes, and oxidative stress, including miR-21/132. This presents new insights into the molecular mechanism of RG in combating metabolic syndrome; however, further in vivo and clinical trials are needed to validate these findings. • Red ginger extract shows potent antioxidant activity, comparable to Trolox. • Both hexane and ethanol extracts inhibit enzymes linked to obesity. • Red ginger suppresses miR-21/132, reducing metabolic syndrome risk. • Molecular docking reveals strong receptor binding of key ginger compounds. • This study reveals the potential of red ginger in combating diabetes through enzyme inhibtion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".