Kappa opioid receptor antagonism and chronic antidepressant treatment have beneficial activities on social interactions and grooming deficits during heroin abstinence
Bibliographic record
Abstract
Addiction is a chronic brain disorder that progressively invades all aspects of personal life. Accordingly, addiction to opiates severely impairs interpersonal relationships, and the resulting social isolation strongly contributes to the severity and chronicity of the disease. Uncovering new therapeutic strategies that address this aspect of addiction is therefore of great clinical relevance. We recently established a mouse model of heroin addiction in which, following chronic heroin exposure, 'abstinent' mice progressively develop a strong and long-lasting social avoidance phenotype. Here, we explored and compared the efficacy of two pharmacological interventions in this mouse model. Because clinical studies indicate some efficacy of antidepressants on emotional dysfunction associated with addiction, we first used a chronic 4-week treatment with the serotonergic antidepressant fluoxetine, as a reference. In addition, considering prodepressant effects recently associated with kappa opioid receptor signaling, we also investigated the kappa opioid receptor antagonist norbinaltorphimine (norBNI). Finally, we assessed whether fluoxetine and norBNI could reverse abstinence-induced social avoidance after it has established. Altogether, our results show that two interspaced norBNI administrations are sufficient both to prevent and to reverse social impairment in heroin abstinent animals. Therefore, kappa opioid receptor antagonism may represent a useful approach to alleviate social dysfunction in addicted individuals.
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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) | 0.001 | 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) | 0.002 | 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".