In vitro Antioxidant Activity of Mangifera indica Leaf Extracts
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it