Quantification of the Bioactivity of Ethanolic Extract From Phoenix dactylifera
Bibliographic record
Abstract
Aim: This study aims to quantitatively assess the anti-inflammatory and antioxidant activities of the ethanolic extract of Phoenix dactylifera seeds. Materials and methods: Around 30 seeds of Phoenix dactylifera were collected, crushed, and powdered; 10 gm of powder was added to 100 ml of ethanolic extract and boiled for further analysis. Egg albumin denaturation assay and hydroxyl radical scavenging assay were done to evaluate the anti-inflammatory and antioxidant activity, respectively. An independent t-test was used to compare the anti-inflammatory and antioxidant potential of the ethanolic extract of Phoenix dactylifera using SPSS Statistics version 22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0; Armonk, NY: IBM Corp.), and values less than 0.05 are considered statistically significant. Results: The seeds of Phoenix dactylifera have potent anti-inflammatory and antioxidant properties. Both anti-inflammatory and antioxidant properties improved with higher concentrations and were comparable to the control substances diclofenac sodium, vitamin E, and ascorbic acid, respectively. The most significant anti-inflammatory and antioxidant effect was observed at a dosage of 50 μL, with a p-value of 0.001. Conclusion: To conclude, we found that the ethanolic extract of Phoenix dactylifera has anti-inflammatory and antioxidant activity, which can further be used for the improvement of pharmaceuticals.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".