The role of kappa opioid receptors in stress‐induced reinstatement of alcohol seeking in rats
Bibliographic record
Abstract
INTRODUCTION: Stress is related to heavy alcohol use and relapse in alcoholics. Using the reinstatement model, we have shown that corticotropin-releasing factor (CRF) underlies stress-induced relapse to alcohol seeking in laboratory rodents. Little is known about how other neurotransmitters interact with CRF in these effects. Dynorphin and its receptor (kappa opioid receptor, KOR) are involved in stress responses and in alcohol seeking. KOR and CRF receptors (CRF R) may interact in the production of stress-related behaviors but it is not known whether this interaction is involved in reinstatement of alcohol seeking. METHODS: Male Long Evans rats were trained to self-administer alcohol (12% w/v). After extinction of responding, we determined the effects of the KOR agonist, U50,488 (2.5, 5 mg/kg) on reinstatement of alcohol seeking, and their sensitivity to the selective KOR antagonist nor-binaltorphimine dihydrochloride (nor-BNI) (10 mg/kg) administered at different times before U50,488. We then examined the effects of nor-BNI on reinstatement induced by the stressor yohimbine (1.25 mg/kg) and on reinstatement induced by exposure to alcohol-associated cues. Finally, we determined whether CRF R1 blockade with antalarmin (10, 20 mg/kg) attenuates alcohol seeking induced by U50,488. RESULTS: U50,488 reinstated alcohol seeking. Prior treatment with nor-BNI 2, but not 24 h before administration of U50,488 or yohimbine blocked reinstatement induced by these drugs. Cue-induced reinstatement was blocked by nor-BNI administered 2 h prior to testing. Finally, U50,488-induced reinstatement was blocked by antalarmin. CONCLUSIONS: These data further support a role for KOR in reinstatement of alcohol seeking under nonstress and stressful conditions and that KOR and CRF R interact in these effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".