Mixture of Mastic Gum and Peppermint Extracts Promotes Hair Growth and Health in Vitro and in C57BL/6 Mice
Bibliographic record
Abstract
PURPOSE: Hair disorders, which are often attributed to conditions associated with a shortened anagen growth phase, oxidative stress, and hormonal dysregulation, especially during aging, have profound psychological implications. Currently, only minoxidil has been approved as a topical hair growth solution; thus, alternative therapies for treating hair loss and promoting hair health are urgently needed. Herein, we aimed to develop and assess a novel method to promote hair growth and health using mastic (Pistacia lentiscus) gum and peppermint (Mentha piperita L.) extracts. MATERIALS AND METHODS: After determining the optimal ratio of mastic gum and peppermint extracts, we performed in vitro and in vivo experiments to verify the efficacy of the 7:3 mastic gum-peppermint mixture (MP73; FHH-MG) for enhancing hair growth and health. RESULTS: Mastic gum significantly promoted cell proliferation and demonstrated synergistic benefits when combined with peppermint extract. In vitro, FHH-MG increased human dermal follicle papilla cell proliferation and demonstrated anti-inflammatory and antioxidant effects. In vivo, treatment with FHH-MG dose-dependently enhanced hair growth and gloss and increased the expression of vascular endothelial growth factor, epidermal growth factor, β-catenin, and insulin-like growth factor-1 in C57BL/6 mice compared to the negative control. CONCLUSION: The novel mixture exhibited hair growth-promoting effects in C57BL/6 mice; thus, FHH-MG may serve as a botanical alternative for hair growth and health promotion.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".