Hair Growth Promoting Effect Of Topical Poly Herbal Oil On Sprague Dawley Rats
Bibliographic record
Abstract
Hair loss is a common concern for both men and women, usually characterized by hair thinning, dandruff and extreme hair fall or shedding. Although synthetic remedies for hair loss are available but they often have appalling side effects and do not offer a permanent and long-lasting solution. The goal of the study was to formulate a herbal hair repair oil that would promote hair growth and repair while reducing hair loss. It was also aimed to examine and compare the effects of various herbal oils on hair growth in Sprague Dawley rats. For this purpose, a variety of herbs were selected including, the leaves of Hibiscus rosa sinensis, Murraya koenigii, Melaleuca alternifolia, Monarda fistulosa and Salvia rosmarinus. Castor oil, coconut oil, olive oil, almond oil, sesame oil, peppermint essential oil, and rosemary essential oil were among the other oils used in this investigation. The formulated repair hair oil was evaluated through physical, chemical, and hair growth tests by applying it topically to shaved albino rats. Primary skin irritation, antioxidant activity and hair length tests were performed. The results of the hair growth study were compared with locally purchased hair oils. Poly herbal hair repair oil demonstrated the best results among the tested samples in terms of increased hair length, efficacy, safety, promising physicochemical properties, antioxidant activity and hair growth promotion in animal models.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".