Brain CYP2E1 is induced by nicotine and ethanol in rat and is higher in smokers and alcoholics
Bibliographic record
Abstract
1. Ethanol and nicotine are commonly coabused drugs. Cytochrome P450 2E1 (CYP2E1) metabolizes ethanol and bioactivates tobacco-derived procarcinogens. Ethanol and nicotine can induce hepatic CYP2E1 and we hypothesized that both centrally active drugs could also induce CYP2E1 within the brain. 2. Male rats were treated with saline, ethanol (3.0 g kg(-1) by gavage) or nicotine (1.0 mg kg(-1) s.c.) for 7 days. Ethanol treatment significantly increased CYP2E1 in olfactory bulbs (1.7-fold), frontal cortex (2.0-fold), hippocampus (1.9-fold) and cerebellum (1.8-fold), while nicotine induced CYP2E1 in olfactory bulbs (2.3-fold), frontal cortex (3.0-fold), olfactory tubercle (3.1-fold), cerebellum (2.5-fold) and brainstem (2.0-fold). Immunocytochemical analysis revealed that the induction was cell-type specific. 3. Consistent with the increased CYP2E1 found in rat brain following drug treatments, brains from alcoholics and alcoholic smokers showed greater staining of granular cells of the dentate gyrus and the pyramidal cells of CA2 and CA3 hippocampal regions as well as of cerebellar Purkinje cells compared to nonalcoholic nonsmokers. Moreover, greater CYP2E1 immunoreactivity was observed in the frontal cortices in the alcoholic smokers in comparison to nonalcoholic nonsmokers and alcoholic nonsmokers. 4 To investigate if nicotine could contribute to the increased CYP2E1 observed in alcoholic smokers, we treated human neuroblastoma IMR-32 cells in culture and found significantly higher CYP2E1 immunostaining in nicotine-treated cells (0.1-10 nM). 5. CYP2E1 induction in the brain, by ethanol or nicotine, may influence the central effects of ethanol and the development of nervous tissue pathologies observed in alcoholics and smokers.
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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 | 0.000 |
| 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 | 0.001 |
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".