Cellular and behavioral basis of cannabinioid and opioid interactions: Implications for opioid dependence and withdrawal
Bibliographic record
Abstract
The brain's endogenous opioid and endocannabinoid systems are neuromodulatory of synaptic transmission, and play key roles in pain, memory, reward, and addiction. Recent clinical and pre-clinical evidence suggests that opioid use may be reduced with cannabinoid intake. This suggests the presence of a functional interaction between these two systems. Emerging research indicates that cannabinoids and opioids can functionally interact at different levels. At the cellular level, opioid and cannabinoids can have direct receptor associations, alterations in endogenous opioid peptide or cannabinoid release, or post-receptor activation interactions via shared signal transduction pathways. At the systems level, the nature of cannabinoid and opioid interaction might differ in brain circuits underlying different behavioral phenomenon, including reward-seeking or antinociception. Given the rising use of opioid and cannabinoid drugs, a better understanding of how these endogenous signaling systems interact in the brain is of significant interest. This review focuses on the potential relationship of these neural systems in addiction-related processes.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".