Wound Healing Effect and Anti-Microbial Activity of Methanolic and Petrolum Ether Extract of Punica granatum in Polyethylene Glycol
Bibliographic record
Abstract
In this study the wound healing effect of the methanol and petroleum ether extract of Punica granatum fruits was investigated on open skin wound model in rats. Thirty Swiss Wistar Albino rats of either sex weighing 90-120g were used during the study. Hair of the lower back and right flank of the animal was completely shaved. Full-thickness circular excision wound one cm in diameter was made on the shaved area. Methanolic and petroleum ether extract of Punica granatum was added to ointment to give 2% (w/w) in Polyethylene Glycol. Tetracycline ointment 3% was used as standard control; both ointments were applied twice daily. Two trials were performed; the first using three groups of non-infected rats and the second using three groups of artificially infected rats with standardized Staphylococcus aureus. Treated groups were compared with non-treated (control group). Healing was determined by reduction in wound area. The results of this study confirmed that the 2% Punica granatum fruits ointment is a potent healing agent even better than the tested Tetracycline ointment 3%. Also methanol extracts of Punica granatum fruits belonging to the family Punicaceae were screened for their antimicrobial activity against Gram positive organisms, Gram negative organism and fungi. The extract pronounced antibacterial activity (20-25 mm) against Bacillus Subtilis moderate activity (18-19 mm) against Escherichia coli, (19-20 mm) Pseudomonas aeruginosa and high activity (25-30) against Staphylococcus aureus, and fungi Aspergillus niger (20-20 mm).
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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".