Activation of the cannabinoid 2 receptor (CB2) protects against experimental colitis
Bibliographic record
Abstract
BACKGROUND: Activation of cannabinoid (CB)(1) receptors results in attenuation of experimental colitis. Our aim was to examine the role of CB(2) receptors in experimental colitis using agonists (JWH133, AM1241) and an antagonist (AM630) in trinitrobenzene sulfonic acid (TNBS)-induced colitis in wildtype and CB(2) receptor-deficient (CB(2) (-/-)) mice. METHODS: Mice were treated with TNBS to induce colitis and then given intraperitoneal injections of the CB(2) receptor agonists JWH133, AM1241, or the CB(2) receptor antagonist AM630. Additionally, CB(2) (-/-) mice were treated with TNBS and injected with JWH133 or AM1241. Animals were examined 3 days after the induction of colitis. The colons were removed for macroscopic and microscopic evaluation, as well as the determination of myeloperoxidase activity. Quantitative reverse-transcriptase polymerase chain reaction (RT-PCR) for CB(2) receptor was also performed in animals with TNBS and dextran sodium sulfate colitis. RESULTS: Intracolonic installation of TNBS caused severe colitis. CB(2) mRNA expression was significantly increased during the course of experimental colitis. Three-day treatment with JWH133 or AM1241 significantly reduced colitis; AM630 exacerbated colitis. The effect of JWH133 was abolished when animals were pretreated with AM630. Neither JWH133 nor AM1241 had effects in CB(2) (-/-) mice. CONCLUSIONS: We show that activation of the CB(2) receptor protects against experimental colitis in mice. Increased expression of CB(2) receptor mRNA and aggravation of colitis by AM630 suggests a role for this receptor in normally limiting the development of colitis. These results support the idea that the CB(2) receptor may be a possible novel therapeutic target in inflammatory bowel disease.
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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".