Involvement of Nicotinic Receptors in Alcohol Self‐Administration
Bibliographic record
Abstract
BACKGROUND: Alcohol and nicotine, in the form of tobacco, are commonly co-abused. Nicotinic receptors also have been implicated in alcohol action. We designed the present study to examine the possible involvement of nicotinic receptors in alcohol self-administration. METHODS AND RESULTS: Pretreatment with lower doses (0.1-0.4 mg/kg) of nicotine, administered acutely or chronically, did not affect alcohol consumption, whereas a higher dose (0.8 mg/kg) initially suppressed alcohol consumption but stimulated alcohol consumption on repeated treatment. We observed the same pattern of nicotine effects on alcohol self-administration using an operant procedure. A dose of 0.8 mg/kg of nicotine initially suppressed operant responding for alcohol. Such suppression of alcohol self-administration was more pronounced during the first 20 min of the 60 min operant session. Responding for alcohol in the nicotine treated group, however, was significantly increased above the saline treated group by the 5th day of treatment. Mecamylamine, a noncompetitive nicotinic receptor antagonist, reduced alcohol consumption, whereas dihydro-beta-erythroidine (DHbetaE), a competitive nicotinic receptor antagonist, did not modify alcohol consumption. CONCLUSIONS: The stimulation of alcohol intake induced by nicotine treatment and the suppression of alcohol intake induced by mecamylamine provide evidence for the involvement of nicotinic receptors in alcohol consumption and/or self-administration. The failure of DHbetaE to reduce alcohol consumption, however, suggests that ethanol-nicotine interaction is mediated by other nicotinic receptor subtypes rather than alpha4beta2 receptor subtype, or that mecamylamine acts through a nonnicotinic mechanism.
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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) | 0.000 | 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) | 0.002 | 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".