Involvement of cannabinoid receptors in peripheral and spinal morphine analgesia
Bibliographic record
Abstract
The interactions between the cannabinoid and opioid systems for pain modulation are reciprocal. However, the role and the importance of the cannabinoid system in the antinociceptive effects of opioids remain uncertain. We studied these interactions with the goal of highlighting the involvement of the cannabinoid system in morphine-induced analgesia. In both phases of the formalin test, intra paw and intrathecal morphine produced similar antinociceptive effects in C57BL/6, cannabinoid type 1 and type 2 receptor wild-type (respectively cnr1WT and cnr2WT) mice. In cnr1 and cnr2 knockout (KO) mice, at the dose used the antinociceptive effect of intra paw morphine in the inflammatory phase of the formalin test was decreased by 87% and 76%, respectively. Similarly, the antinociceptive effect of 0.1μg spinal morphine in the inflammatory phase was abolished in cnr1KO mice and decreased by 90% in cnr2KO mice. Interestingly, the antinociceptive effect of morphine in the acute phase of the formalin test was only reduced in cnr1KO mice. Notably, systemic morphine administration produced similar analgesia in all genotypes, in both the formalin and the hot water immersion tail-flick tests. Because the pattern of expression of the mu opioid receptor (MOP), its binding properties and its G protein coupling remained unchanged across genotypes, it is unlikely that the loss of morphine analgesia in the cnr1KO and cnr2KO mice is the consequence of MOP malfunction or downregulation due to the absence of its heterodimerization with either the CB1 or the CB2 receptors, at least at the level of the spinal cord.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".