Pharmacological investigations of a yohimbine–impulsivity interaction in rats
Bibliographic record
Abstract
Both impulsivity and stress are risk factors for substance abuse, but it is not clear how these two processes interact to alter susceptibility for the disorder. The aim of this project was to examine the pharmacology of a stress-impulsivity interaction in rats. To do so, we tested the effects of yohimbine on impulsive action and then assessed whether behavioural changes could be reduced by antagonists at different receptor subtypes. Male Long-Evans rats were injected with various doses of yohimbine (0-5.0 mg/kg) before testing in the response-inhibition task. In subsequent experiments, yohimbine (2.5 mg/kg) was injected following pretreatment with the following receptor antagonists: corticotropin-releasing factor receptor 1, antalarmin (0-20 mg/kg); glucocorticoid, mifepristone (0-30 mg/kg); noradrenergic (NA) α1, prazosin (0-2 mg/kg); NA α2, guanfacine (0-0.5 mg/kg); NA β2, propranolol (0.5-2.0 mg/kg); dopamine D1/5, SCH 39166 (0-0.0625 mg/kg); μ opioid, naloxone (0-2 mg/kg); or 5-HT2A, M100907 (0.005-0.05 mg/kg). In all experiments, impulsive action was measured as increased premature responding. Yohimbine dose dependently increased impulsive action, but the effect was not reversed by antagonist pretreatment. None of the drugs altered any other behavioural measure. We conclude that stress-impulsivity interactions are likely mediated by a synergy of multiple neurotransmitter systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".