<i>In vivo</i>effects of CB<sub>2</sub>receptor‐selective cannabinoids on the vasculature of normal and arthritic rat knee joints
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cannabinoids (CBs) are known to be vasoactive and to regulate tissue inflammation. The present study examined the in vivo vasomotor effects of the CB2 receptor agonists JWH015 and JWH133 in rat knee joints. The effect of acute and chronic joint inflammation on CB2 receptor-mediated responses was also tested. EXPERIMENTAL APPROACH: Blood flow was assessed in rat knee joints by laser Doppler imaging both before and following topical administration of CB2 receptor agonists. Vasoactivity was measured in normal, acute kaolin/carrageenan inflamed and Freund's complete adjuvant chronically inflamed knees. KEY RESULTS: In normal animals, JWH015 and JWH133 caused a concentration-dependent increase in synovial blood flow which in the case of JWH133 was blocked by the selective CB2 receptor antagonist AM630 as well as the transient receptor potential vanilloid-1 (TRPV1) antagonist SB366791. The vasodilator effect of JWH133 was significantly attenuated in both acute and chronically inflamed knees. Given alone, AM630 had no effect on joint blood flow. CONCLUSION AND IMPLICATIONS: In normal joints, the cannabinomimetic JWH133 causes hyperaemia via a CB2 and TRPV1 receptor mechanism. During acute and chronic inflammation, however, this vasodilatatory response is significantly attenuated.
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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) | 0.000 | 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) | 0.003 | 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".