Ethanol self‐administration and nicotine treatment increase brain levels of<scp>CYP</scp>2<scp>D</scp>in<scp>A</scp>frican green monkeys
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: CYP2D6 metabolizes many centrally acting drugs, neurotoxins and endogenous neurochemicals, and differences in brain levels of CYP2D have been associated with brain function and drug response. Alcohol consumers and smokers have higher levels of CYP2D6 in brain, but not liver, suggesting ethanol and/or nicotine may induce human brain CYP2D6. We investigated the independent and combined effects of chronic ethanol self-administration and nicotine treatment on CYP2D expression in African green monkeys. EXPERIMENTAL APPROACH: Forty monkeys were randomized into control, ethanol-only, nicotine-only and ethanol + nicotine groups. Two groups voluntarily self-administered 10% ethanol in sucrose solution for 4 h·day(-1) , whereas two groups consumed sucrose solution on the same schedule. Two groups received daily s.c. injections of 0.5 mg·kg(-1) nicotine in saline bid, whereas two groups were injected with saline on the same schedule. KEY RESULTS: Both nicotine and ethanol dose-dependently increased CYP2D in brain; brain mRNA was unaffected, and neither drug altered hepatic CYP2D protein or mRNA. The combination of ethanol and nicotine increased brain CYP2D protein levels to a greater extent than either drug alone (1.2-2.2-fold, P < 0.05 among the eight brain regions assessed). Immunohistochemistry revealed the induction of brain CYP2D protein within specific cell types and regions in the treatment groups. CONCLUSIONS AND IMPLICATIONS: Ethanol and nicotine increase brain CYP2D protein levels in monkeys, in a region and treatment-specific manner, suggesting that CNS drug responses, neurodegeneration and personality may be affected among people who consume alcohol and/or nicotine.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".