The Effects of Hyperıcum perforatum (St. John's Wort) Oil on Wound Healing and Comparison with Those of Collagenase Drugs
Bibliographic record
Abstract
Background and Objectives: Wound healing is among the most fundamental problems in the field of health. Oil of Hypericum perforatum (HP) has been used for various ailments, particularly depression, burns, and wound healing, globally for a long period of time. We investigated the healing-promoting effect of HP oil on wound healing histopathologically and macroscopically in an experimental wound model in rats, aiming to compare the results with those of collagenase pomade, which is actively used in the literature. Materials and Methods: A total of 32 male Wistar rats of similar were used for the experiment. The animals were randomly divided into four groups, each consisting of eight rats. After the dorsal areas of the rats were shaved and disinfected with povidone-iodine solution, a 1 cm2 full-thickness wound was created on the backs of each rat. The sham (control) group received dry gauze dressings, while the physiological saline (PS) group was treated with saline-soaked gauze. The collagenase ointment pomade (CP) group and HP groups were treated with gauze impregnated with 0.1 grams of these substances. All wounds were dressed twice daily, in the morning and evening. The second group was treated with physiological saline. The third group was treated with collagenase ointment. And the fourth group was treated with St. John's Wort oil. Wound areas were measured on days 3, 7, 10, 14, and 21. In addition, histopathological evaluation was performed on tissue samples obtained on day 14, and the day of complete wound closure was recorded for each animal. Results: When the findings were statistically analyzed, wounds treated with St. John's Wort oil and collagenase ointment healed significantly faster than the other groups (p < 0.05). Conclusions: In conclusion, St. John’s Wort oil appears to be a promising topical agent for enhancing wound healing, as it accelerates the healing process. However, controlled clinical trials in human subjects would provide the most definitive evidence on this topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".