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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".