Phenolics, antioxidant and anti-inflammatory activities of Melia azedarach extracts
Bibliographic record
Abstract
Summary. The leaf, seed and almonds of Melia azedarach grown in Algeria were defatted and extracted with three solvents, aqueous (70%, v/v) acetone, (80%) ethanol and water and evaluated for antioxidant activity in relation to phenolic contents. Defatting improved the total phenolic and tannin contents and antioxidant activities of extracts. Aqueous ethanol (50%) extracted the highest level of total phenolics from the leaf at about 98 mg/g catechin equivalent exhibiting the strongest antioxidant (139 mg/g Trolox equivalent). The defatted aqueous ethanol (50%) leaf extract induced the least lipid peroxidation, an indicator for mitigating oxidative stress. Melia azedarach water and aqueous ethanol (50%) leaf extracts contained considerable amounts of quercetin derivatives and rutin (7-13 and 5-10 mg/g of dry weight, respectively) and exerted strong anti-inflammatory effects by inhibiting human monoacylglycerol lipase (MAGL). Industrial relevance. Immunity ingredients, in addition to their potent antioxidant activity, are of significant interest in nutraceuticals particularly of naturally derived compounds that reduce the risk of disease and conditions. The present study demonstrates that Melia azedarach water and aqueous ethanol (50%) leaf extracts exhibit strong antioxidant and anti-inflammatory activity by inhibiting human monoacylglycerol lipase (MAGL). Human MAGL inhibitors are rarely found naturally and its presence in natural products, particularly in Melia azedarach extracts provides a venue for its use mitigating CNS diseases where the imbalance of endocannabinoids plays a major role. Keywords. Melia azedarach ; antioxidant activity; anti-inflammatory; lipid peroxidation; lipase inhibition
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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".