Win 55212-2, a cannabinoid receptor agonist, attenuates leukocyte/endothelial interactions in an experimental autoimmune encephalomyelitis model
Bibliographic record
Abstract
Multiple sclerosis (MS) is the most common of the immune demyelinating disorders of the central nervous system (CNS). Leukocyte/endothelial interactions are important steps in the progression of the disease and substances that interfere with these activities have been evaluated as potential therapeutic agents. Cannabinoid receptor agonists have been shown to downregulate immune responses and there is preliminary evidence that they may slow the progress of MS. The purpose of this investigation was to determine how cannabinoid receptor agonists interfere with leukocyte rolling and adhesion. This was investigated in an experimental autoimmune encephalomyelitis (EAE) model using six to eight week old C57BL/6 mice. Mouse myelin oligodendrocyte protein and pertussis toxin were used to induce EAE. WIN 55212-2, CB1 and CB2 antagonist were given. By use of in vivo intravital microscopy, leukocyte/endothelial interactions were evaluated via a cranial window implanted two days before. The results demonstrated that EAE increases leukocyte rolling and firm adhesion in the brain, and that this increased leukocyte/endothelial interaction can be attenuated by administration of WIN 55212-2. Furthermore, use of the selective antagonists for the CB1 receptor (SR 141716A) and the CB2 receptor (SR144528) in this study demonstrated that the cannabinoid's inhibitory effects on leukocyte/endothelial interactions can be mediated by activating CB2 receptor.
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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 | 0.000 | 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".