Protein kinase C epsilon modulates nicotine consumption and dopamine reward signals in the nucleus accumbens
Bibliographic record
Abstract
Nicotine addiction and alcohol use disorders are very widespread and often occur together. Currently, there is no single drug approved for the simultaneous treatment of both conditions. Although these conditions share common genetic factors, the molecular mechanisms underlying their comorbidity are unknown. We have previously shown that mice lacking protein kinase C epsilon (PKCε) show decreased ethanol self-administration and reward as well as increased aversion to ethanol. Here we find that Prkce(-/-) mice self-administer less nicotine and show decreased conditioned place preference for nicotine compared with wild-type mice. In Prkce(-/-) mice, these behaviors are associated with reduced levels of α(6) and β(3) nicotinic receptor subunit mRNA in the ventral midbrain and striatum as well as a functional deficit in cholinergic modulation of dopamine release in nucleus accumbens. Our results indicate that PKCε regulates reward signaling through α(6)-containing nicotinic receptors and suggest that PKCε could be a target for the treatment of comorbid nicotine and alcohol addictions.
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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) | 0.000 | 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 | 0.000 |
| 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".