Effects of policosanols and phytosterols on lipid levels and cholesterol biosynthesis in hamsters
Bibliographic record
Abstract
The current study was carried out to examine the effects of policosanols and phytosterols, alone and in combination, on lipid profiles, cholesterol biosynthesis, and tissue histopathological changes in hamsters. Fifty male Golden Syrian hamsters, weighing 100 to 120 g, were fed a regular rodent chow for 2 wk before being randomly assigned into 5 groups of 10 animals each fed semisynthetic diets for 4 wk. Group 1 was given a control diet that contained 0.25% cholesterol and 5% fat with a PUFA to saturated FA ratio of 0.4. Groups 2 to 5 were fed the control diet and given Octa-6 [a policosanol mixture from sugar cane wax, 25 mg/kg body weight (BW)], Ricewax (a policosanol mixture from rice wax with 50% being converted to the corresponding acids, 50 mg/kg BW), phytosterols (Cholestatin; 1,000 mg/kg BW), and Ricewax (50 mg/kg BW) plus phytosterols (1,000 mg/kg BW), respectively. The results showed that there was no difference between Octa-6 and Ricewax treatments in any of the lipid parameters measured, and both had similar levels of triglyceride (TG), total cholesterol (T-C), and HDL cholesterol (HDL-C) as the control. Octa-6 but not Ricewax increased (P = 0.03) non-HDL-C as compared with the control. Phytosterols reduced T-C (P < 0.0003) and HDL-C (P < 0.004) without a significant effect on TG and non-HDL-C as compared to the control. Ricewax plus phytosterols had effects similar to those with phytosterols alone. Free cholesterol synthetic rates were not different among the treatments. Policosanols or phytosterols did not show any toxic effects in liver, heart, brain, or kidney. Results suggest that, although phytosterols reduce T-C and HDL-C levels, policosanols have no significant favorable effect in changing lipid levels in hamsters.
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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) | 0.001 | 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".