Anti-inflammatory Effect of Cannabidiol and Palmitoylethanolamide Containing Topical Formulation on Skin in a 12-O-Tetradecanoylphorbol-13-Acetate–Induced Dermatitis Model in Mice
Bibliographic record
Abstract
BACKGROUND: Chronic inflammatory skin disorders, such as atopic dermatitis, have significant disease burden worldwide. Although efficacious, the adverse effect profile of topical corticosteroids limits long-term use. As an alternative, cannabinoids have been shown to have anti-inflammatory therapeutic effects. OBJECTIVE: The aim of this study was to assess the effects of a topical cannabinoid product using dermatitis mouse model. METHODS: Thirty-five mice were randomized into treatment groups. 12- O -tetradecanoylphorbol-13-acetate was used as an irritant on 1 ear with the contralateral ear serving as a control. Ear edema was calipered. The test product containing 0.9% cannabidiol and palmitoylethanolamide was compared with a potent topical corticosteroid. RESULTS: Treatment with topical cannabinoid formulation reduced ear edema by 51.27% at 24 hours' and 65.69% at 48 hours' postapplication. Alternatively, mometasone reduced ear edema by 89.82% at 24 hours and 98.25% at 48 hours. Natural reduction (control) in ear edema was 26.32% at 24 hours and 44.21% at 48 hours. Both test groups resulted in significantly decreased edema when compared with baseline ( P < 0.05), as well as compared with the negative control group ( P < 0.05). CONCLUSIONS: Significant reduction in ear edema, a marker for localized cutaneous inflammation, could be attributed to anti-inflammatory properties of cannabinoids. Although effects were less robust than topical corticosteroid use, cannabinoid formulations have therapeutic promise for dermatitis.
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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 | 0.000 |
| 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) | 0.001 | 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".