In vitro Antioxidant Activity of Mangifera indica Leaf Extracts
Bibliographic record
Abstract
In this study, we aimed to identify the utility of pruned mango (Mangifera indica ‘Irwin’) leaves as a resource for ingredients with antioxidant activity. Firstly, we examined the antioxidant activity of extracts obtained from the pericarps, flesh, flowers, barks, seeds, young dark reddish brown leaves (YDL-ext), young yellow leaves (YYL-ext), and pruned old dark green leaves (OML-ext) obtained from ‘Irwin’ mango. Among them, methanolic extract of flower and OML-ext showed the most potent 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging and superoxide dismutase (SOD)-like activity. The flesh extract showed weak DPPH radical scavenging activity, but did not show SOD-like activity. Secondly, we investigated the relationship between the maturation of leaves and their antioxidant activity by considering the contents of their two active polyphenolic components, 3-C-β-D-glucosyl-2,4,4’,6-tetrahydroxybenzophenone (1) and mangiferin (2), in addition to chlorophyll (3) and anthocyanins represented by cyanidin-3-O-glucoside (4). The DPPH radical scavenging activity of YDL-ext, YYL-ext and OML-ext were mainly attributable to 1, 2 and 3, whereas their SOD-like activity was partly attributable to 2. The DPPH radical scavenging and SOD-like activities of YDL-ext and YYL-ext were attributable to 1 and 2. These activities were also due to anthocyanins whose content is highest in YDL-ext. Considering the amounts of leaves obtained from pruning, old dark green leaves may be a reasonable natural resource for preparing cosmetics and/or supplemental ingredients with health-enhancing properties, antioxidant activity and inhibitory effect on AGEs formation and pancreatic lipase.
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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) | 0.001 | 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) | 0.002 | 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".