The effects of whey and pumpkin seed oil on blood biochemical parameters of liver function and lipid profile in rats chronically drinking low concentrations of ethanol
Bibliographic record
Abstract
We studied the effects of whey and pumpkin seed oil supplementation on the biochemical parameters in blood serum of male rats after chronic ad libitum alcohol consumption. The levels of AST, ALT, total bilirubin, ALP, LDH, triglycerides, total cholesterol, HDL, LDL, VLDL, triglyceride/HDL ratio, total cholesterol/HDL ratio (cholesterol ratio) and LDL/HDL ratio (index of atherosclerosis) were determined in rats after six weeks of treatment with: (i) ethanol (12% ethanol, ad libitum), (ii) whey (2 g/kg per day), (iii) pumpkin seed oil (2 mL/kg per day), (iv) both ethanol and whey, and (v) both ethanol and pumpkin seed oil. The results showed no changes in the levels of AST, ALT, total bilirubin, ALP, total cholesterol, HDL and VLDL in alcoholic rats when compared to the controls (fed with a standard laboratory diet ad libitum) and rats supplemented with whey and pumpkin seed oil. Our results suggest that alcohol consumption in small doses for 6 weeks changes lipid metabolism and significantly elevates the LDL/HDL ratio (index of atherosclerosis) but does not induce extensive liver damage. Ethanol consumption in our experimental conditions lowered the triglyceride level as well as the triglyceride/HDL ratio, suggesting lipid redistribution and the induction of some cardio-protective effect. However, ethanol induced a higher index of atherosclerosis. Pumpkin seed oil showed some protective potential in alcoholic rats by lowering the total cholesterol/HDL ratio, but it elevated the LDH. Whey consumption prevented elevation of the atherosclerosis index, pointing to its protective role, probably through the redistribution of lipids. However, whey in combination with ethanol elevated LDH.
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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 | 0.000 |
| 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 | 0.001 |
| 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".