The In vivo Biochemical and Oxidative Changes by Ethanol and Opium Consumption in Syrian Hamsters
Bibliographic record
Abstract
Daily consumption of opium and alcohol can make people have many health problems, including coronary artery disease diseases (CAD) which has been found to be the most common cause death in opium addicts. The aim of this study was to investigate the effect of simultaneous consumption of alcohol and opium on the lipid profile and oxidative stress in Syrian golden hamsters. Twenty-four male golden Syrian hamsters were randomly divided into four treatment groups (n=6): 1-control (received normal chow), 2-opium (received 40 mg/kg of opium two times per day), 3-alcohol (received 6.0 g/kg of 30% ethanol two times per day), 4-combination group (received a combination of the above mentioned doses of opium and ethanol). After one month of treatment, hamsters were sacrificed and blood samples were collected. Lipid levels and atherogenic index were markedly increased in the combination group compared with the controls (p < 0.001). Serum alanine aminotransferase (ALT) (p < 0.05), aspartate aminotransferase (AST) (p < 0.05), and gamma-glutamyl transferase (GGT) (p < 0.01), were significantly increased in alcohol-treated group compared with the control animals. The increase in ALT (p < 0.01) and GGT (p < 0.001) levels were more significant in the combination group when compared with the controls. The plasma concentration of malondialdehyde (MDA) was markedly increased in the ethanol (p < 0.01), opium (p < 0.01) and combination groups (p < 0.001) compared with the controls. Glutathione (GSH), nitric oxide (NO) and catalase (CAT) levels as well as superoxide dismutase activity were markedly reduced in the ethanol (p < 0.05), opium (p < 0.05), and combination groups (p < 0.001) compared with the control group. Results of this study clearly showed that opium and ethanol are capable to provoke the oxidative stress when administered alone or in combination. Moreover, combination opium and alcohol increased total cholesterol, LDL-C, TG, VLDL-C, atherogenic index and non-HDL-C levels.
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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) | 0.000 | 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) | 0.001 | 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".