The serotonin 2C receptor agonist lorcaserin, alone and in combination with the opioid receptor antagonist naltrexone, attenuates binge‐like ethanol drinking
Bibliographic record
Abstract
receptor agonist, attenuates drug self-administration in animal models. We investigated the effects of lorcaserin on EtOH intake using the drinking-in-the-dark (DID) procedure, an animal model of binge-like drinking. We compared the effects of lorcaserin to those of the Food and Drug Administration (FDA)-approved drug naltrexone and examined the effects of combining lorcaserin and naltrexone. To examine whether effects were specific for EtOH, we examined the effects of lorcaserin and naltrexone, administered alone and in combination, on saccharin intake. Adult male C57BL/6J mice received EtOH access (20% v/v) for 2 h in the home-cage during the first 3 days of the DID procedure, beginning 3 h into the dark cycle. On day 4, mice were injected with lorcaserin, naltrexone, or a combination of lorcaserin and naltrexone prior to a 4-h EtOH access. Intake was measured at 2 and 4 h. Lorcaserin reduced EtOH intake in a dose-dependent fashion over the 2- and 4-h measurement periods. Naltrexone also reduced EtOH intake when administered alone, with dose-dependent effects being more pronounced over 2 h rather than the full 4-h session. Combining lorcaserin and naltrexone reduced binge-like EtOH drinking to a greater extent than either drug alone. A similar pattern of results was obtained for saccharin intake. These results suggest that lorcaserin and naltrexone can have additive effects on binge-like EtOH drinking. They also support continued research into the therapeutic potential of lorcaserin for alcohol use disorders.
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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) | 0.001 | 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".