Cigarette Smoke Extract and Nicotine Evoke Similar Interoceptive Effects in a Pavlovian Occasion Setting Task in Male and Female Sprague–Dawley Rats
Bibliographic record
Abstract
INTRODUCTION: Although nicotine is the primary component of interest in tobacco, the other ~9500 constituents in tobacco are thought to interact with nicotine to contribute to the pharmacological effects relevant to tobacco use disorder. These other tobacco constituents may also contribute to providing an interoceptive stimulus unique from that of nicotine alone. Using a Pavlovian drug discrimination task, we assessed whether rats could discriminate between nicotine and cigarette smoke extract (CSE) of the same nicotine concentration (0.2 mg/kg) based on the presence of constituent chemicals. METHODS: Rats were assigned to one of three training conditions in which intermixed daily injections were administered before chamber placement. The interoceptive stimulus elicited by the injected compound would set the occasion on which a light conditioned stimulus would, or would not, be followed by sucrose. Increased dipper entries during the conditioned stimulus indicate greater anticipation of impending sucrose. Groups included (1) nicotine versus vehicle with nicotine signaling sucrose, (2) CSE versus vehicle with CSE signaling sucrose, and (3) CSE versus nicotine with CSE signaling sucrose. This final group determined whether rats could discriminate based on other tobacco constituents. RESULTS: Subjects readily discriminated between nicotine and vehicle and between CSE and vehicle within ~20 training sessions; however, they were unable to discriminate between CSE and nicotine after 72 sessions. CONCLUSIONS: Our results confirm that CSE is a successful Pavlovian discriminative stimulus and add to previous nicotine literature. Interestingly, we demonstrate that CSE and nicotine do not create distinct interoceptive environments under current training conditions. IMPLICATIONS: Nicotine has long been used as a proxy for tobacco in non-human animal studies. However, there has always been an undercurrent regarding the appropriateness of this approach given the myriad constituents in tobacco. Though generalization to other behavioral preparations must be done with extreme caution, the current findings suggest that, at least in some capacity, there is overlap in the stimulus characteristics of nicotine and CSE.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".