Anti-inflammatory potential of ellagic acid, gallic acid and punicalagin A&B isolated from Punica granatum
Bibliographic record
Abstract
BACKGROUND: Punica granatum (pomegranate), an edible fruit originating in the Middle East, has been used as a traditional medicine for treatment of pain and inflammatory conditions such as peptic ulcer. The numerous risks associated with nonsteroidal anti-inflammatory drugs (NSAIDs) for treatment of pain and inflammation give rise to using medicinal herbs as alternative therapies. This study aimed to evaluate the anti-inflammatory effect of isolated compounds from the ethyl acetate (EtOAc) fraction of P. granatum by determination of their inhibitory effects on lipopolysaccharide (LPS), stimulated nitric oxide (NO), prostaglandin E2 (PGE-2), interleukin-6 (IL-6) and cyclooxxgenase-2 (COX-2) release from RAW264.7 cells. METHODS: The compounds ellagic acid, gallic acid and punicalagin A&B were isolated from EtOAc by high performance liquid chromatography (HPLC) and further identified by mass spectrometry (MS). The inhibitory effect of ellagic acid, gallic acid and punicalagin A&B were evaluated on the production of LPS-induced NO by Griess reagent, PGE-2 and IL-6 by immunoassay kit and prostaglandin E2 competitive ELISA kit, and COX-2 by Western blotting. RESULTS: Ellagic acid, gallic acid and punicalagin A&B potentially inhibited LPS-induced NO, PGE-2 and IL-6 production. CONCLUSION: The results indicate that ellagic acid, gallic acid and punicalagin may be the compounds responsible for the anti-inflammatory potential of P. granatum.
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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 | 0.001 | 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".