β2* nicotinic acetylcholine receptor subtypes mediate nicotine-induced enhancement of Pavlovian conditioned responding to an alcohol cue
Bibliographic record
Abstract
Nicotine enhances Pavlovian conditioned responses to reward-associated cues. We investigated through which nicotinic acetylcholine receptor (nAChR) subtypes nicotine acts to produce this behavioral effect to an alcohol-associated cue. Male Long-Evans rats with freely available food and water were first accustomed to drinking 15% ethanol in their home cages using an intermittent access, two-bottle choice procedure. Then the rats were given 15 Pavlovian conditioning sessions in which a 15-s audiovisual conditioned stimulus (CS) predicted the delivery of 0.2 ml of ethanol, the unconditioned stimulus (US). Each session contained 12 CS-US trials. A control group received explicitly unpaired presentations of the CS and US. We measured Pavlovian conditioned approach to the site of US delivery during presentations of the CS, accounting for pre-CS baseline activity. Before each conditioning session, rats were injected subcutaneously with nicotine (0.4 mg/kg) or saline (1 ml/kg). During nAChR antagonist test sessions, rats were first injected systemically with the β2*-selective nAChR antagonist dihydro-beta-erythroidine (DHβE; 3 mg/kg) or the α7-selective nAChR antagonist methyllycaconitine (MLA; 6 mg/kg), followed by their assigned nicotine or saline injection before assessing their conditioned response to the alcohol-associated cue. Consistent with previous reports, nicotine enhanced the Pavlovian conditioned response to the alcohol-paired cue. DHβE attenuated this enhancement, whereas MLA did not. These results suggest that nicotine acts via β2*, but not α7, nAChRs to amplify Pavlovian conditioned responding to an alcohol cue. These findings contribute to a growing literature that identifies nAChRs as potential targets for pharmacological treatment of co-morbid alcohol and tobacco use disorders.
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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 | 0.001 |
| 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".