Analgesic and Anti-Inflammatory Effects of 1% Topical Cannabidiol Gel in Animal Models
Bibliographic record
Abstract
Introduction: Cannabidiol (CBD), a phytocannabinoid isolated from cannabis plants, is an interesting candidate for studying its anti-inflammatory effects, especially in the pre-clinical and animal models. Its anti-inflammatory effects, such as reduction of edema and arthritis, have been demonstrated in animal models. However, topical CBD administration requires further evaluation of CBD dosage and efficacy in animal models and clinical settings. Methods: This in vivo study investigated the anti-inflammatory effects of topical CBD administration in an animal model. Scientific experiments, including the formalin test, writhing test, carrageenan-induced edema, histopathological examination, and detection of various proinflammatory mediators, were performed. Results: The anti-inflammatory effects in vivo after inflammation induction, represented by decreased times of paw licking, degree of paw edema, and decreased writhing response, showed that 1% of tropical CBD use had significantly comparable or better anti-inflammatory effects when compared with tropical diclofenac, an anti-inflammatory agent. Moreover, the anti-inflammatory effects were significant compared with the placebo. In addition, the histopathological examination showed that topical CBD drastically reduced leukocyte infiltration and the degree of inflammation. This study also showed that the levels of various proinflammatory mediators in the plasma of mice treated with topical CBD did not differ from those treated with diclofenac. Conclusions: The topical administration of 1% CBD gel is a potentially effective candidate for an anti-inflammatory agent. Candidate for an anti-inflammatory agent.
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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) | 0.001 | 0.000 |
| 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) | 0.002 | 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".