Inhibitory Effect of Several Mangifera indica Cultivar Leaf Extracts on the Formation of Advanced Glycation End Products (AGEs)
Bibliographic record
Abstract
As a part of our ongoing research to find novel functions in mango leaves, we have reported that the methanolic extract of pruned old dark green mango leaf (Mangifera indica ‘Irwin’) exhibited inhibitory effects on the formation of advanced glycation end products (AGEs) in nonenzymatic glycation of albumin. The purpose of this study was to find other mango cultivars with more potent activity in this regard. We examined the inhibitory effect of seventeen mango (Mangifera indica) cultivar leaf extracts on AGEs formation. We also investigated the relationship between the inhibitory activity of the extracts and the contents of their active components, 3-C-β-D-glucosyl-2,4,4’,6-tetrahydroxybenzophenone (1), mangiferin (2) and chlorophyll (3). On the basis of the evaluation of the inhibitory activity of mango cultivar leaf extracts, the HPLC determination of the contents of 1 and 2, and the spectrophotometric determination of 3, it was found that almost all extract showed a significant activity, and the content of 2 and 3 detected in each was similar. In contrast, AGEs formation inhibition tended to be higher as the content of 1 in the leaf extracts increased. This is the first report of phytochemical analysis of compounds 1, 2 and 3 in various cultivars of mango leaf. From the phytochemical point of view, these results suggest that the pruned leaves of any cultivar of Mangifera indica except ‘Chiin Hwang No. 1’ and ‘Kyo Savoy’ may be useful for the preparation of natural ingredients with inhibitory activity of AGEs formation.
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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) | 0.001 | 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".