Preparation and Standardization of Vaginal Suppository from Punica granatum Flower Extract Known as “Golnar” in Persian Medicine
Bibliographic record
Abstract
Background: Punica granatum L. flower, known as "Persian Golnar", is used in Persian medicine to treat excessive menstrual bleeding.Most therapeutic activities of pomegranate are due to the presence of phenolic compounds such as ellagitannins and gallotannins.Aim: This study served the purpose of developing and standardizing P. granatum vaginal suppository.Materials and Methods: Total phenolic and tannin content were assessed for both the extract and the final formula.Moreover, physicochemical properties of vaginal suppository such as appearance, weight variation, melting point, and dissolution test were evaluated.Results and Discussion: It was indicated by the findings of this study that the odorless suppositories have a brownish-red color and uniform appearance with an average weight of 2.06 ± 0.04 and a melting time of 28.93 ± 0.40.100% of the plant extract was released for up to 90 min after administration.The results also indicated that the average phenolic percentages of the extract and suppositories were 17.03 % and 15.73% within three days respectively.Conclusion: P. granatum flower extract as a vaginal suppository can be applied as an anti-hemorrhagic agent due to the presence of polyphenols, particularly tannins, in the extract.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".