Scavenging Activity, Anti-Inflammatory and Diabetes Related Enzyme Inhibition Properties of Leaves Extract from some Varieties of <i>Phoenyx dactylifera l.</i>
Bibliographic record
Abstract
In this study we investigate the antioxidant, anti-inflammatory, and antidiabetic activities of ethanolic leaves extracts of three selected varieties of Phoenyx dactylifera L. namely: “Ghars”, “Deglet Nour” and “Hamraya”. The assessment of the antioxidant potential of crude leaves extracts, using superoxide anions inhibition, DPPH and total antioxidant activity essays, was carried out. Furthermore, the anti-inflammatory properties of the extracts were determined by measuring the inhibition of nitric oxide (NO) production. Moreover, the antidiabetic effect was evaluated by inhibition of α-amylase enzymes. The total phenolic content measured by Folin-Ciocalteu method was as well conducted. The raw leaves extracts of the selected varieties was found to contain a high content of total phenolic content (342.45 mg GAE/gDW for GE) and therefore exhibited a higher antioxidant activity and inhibitory effect of radicals scavenging activity against DPPH and superoxide anion (IC50 = 7.44 μg/mL and 39.11 μg/mL respectively). The three varieties exhibited significant inhibition nitric oxide using In Vitro assay (IC50 = 240.28 μg/mL for GE). The extracts also displayed high inhibition actions against α-amylase enzymes. The results suggest that the leaves of the three selected varieties of Phoenyx dactylifera can be considered as a good source of natural antioxidant and anti-inflammation drugs as well as potent antidiabetic medicine.
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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".