Effects of ethanol and haloperidol on plasma levels of hepatic enzymes, lipid profile, and apolipoprotein in rats
Bibliographic record
Abstract
This work studied the effects of ethanol in the absence and presence of haloperidol under two experimental conditions. In protocol 1, rats were treated daily with ethanol (4 g/kg, p.o.) for 7 days, and received only haloperidol (1 mg/kg, i.p.) from the 8th day to the 14th day. In protocol 2, animals received ethanol, and the treatment continued with ethanol and haloperidol from the 8th day to the 14th day. Results show increases in alanine transaminase (ALT; 48% and 55%) and aspartate transaminase (AST; 32% and 22%) levels after ethanol or haloperidol (14 days) treatments, as compared with controls. Apolipoprotein A-1 (APO A1) levels were increased by haloperidol, after 7- (148%) but not after 14-day treatments, as compared with controls. Levels of lipoprotein (high-density lipoprotein (HDL-C)) tended to be increased only by ethanol treatment for 14 days. ALT (80%) and AST (43%) levels were increased in the haloperidol plus ethanol group (protocol 2), as compared with controls. However, an increase in APO A1 levels was observed in the haloperidol group pretreated with ethanol (protocol 1), as compared with controls and ethanol 7-day treatments. Triglyceride (TG) levels were increased in the combination of ethanol and haloperidol in protocol 1 (234%) and 2 (106%), as compared with controls. Except for a small decrease in haloperidol groups, with or without ethanol, as related to ethanol alone, no other effect was observed in HDL-C levels. In conclusion, we showed that haloperidol might be effective in moderating lipid alterations caused by chronic alcohol intake.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".