Prevention of Cannabinoid Withdrawal Syndrome by Lithium: Involvement of Oxytocinergic Neuronal Activation
Bibliographic record
Abstract
Cannabis (i.e., marijuana and cannabinoids) is the most commonly used illicit drug in developed countries, and the lifetime prevalence of marijuana dependence is the highest of all illicit drugs in the United States. To provide clues for finding effective pharmacological treatment for cannabis-dependent patients, we examined the effects and possible mechanism of lithium administration on the cannabinoid withdrawal syndrome in rats. A systemic injection of the mood stabilizer lithium, at serum levels that were clinically relevant, prevented the cannabinoid withdrawal syndrome. The effects of lithium were accompanied by expression of the cellular activation marker Fos proteins within most oxytocin-immunoreactive neurons and a significant increase in oxytocin mRNA expression in the hypothalamic paraventricular and supraoptic nuclei. Lithium also produced a significant elevation of oxytocin levels in the peripheral blood. We suggest that the effects of lithium against the cannabinoid withdrawal syndrome are mediated by oxytocinergic neuronal activation and subsequent release and action of oxytocin within the CNS. In support of our hypothesis, we found that the effects of lithium against the cannabinoid withdrawal syndrome were antagonized by systemic preapplication of an oxytocin antagonist and mimicked by systemic or intracerebroventricular injection of oxytocin. These results demonstrate that oxytocinergic neuronal activation plays a critical role in the action of lithium against the cannabinoid withdrawal syndrome in rats, thus providing a potentially novel strategy for the treatment of cannabis dependence in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".