Hyperlipidaemic effect of fish oil in Bio F<sub>1</sub>B hamsters
Bibliographic record
Abstract
We investigated the dietary influence of low and high levels of fish oil, supplemented with or without dietary cholesterol, on the plasma lipoprotein profile in Bio F1B hamsters, a model susceptible to diet-induced hyperlipidaemia. The MIX diet, a diet supplemented with a mixture of lard and safflower-seed oil, was used as the control diet to maintain the saturated MUFA and PUFA levels similar to the fish-oil diet. The animals were fed the specific diets for 2 weeks and fasted for 14 h before killing. The plasma from the animals fed high levels of fish oil was milky and rich in chylomicron-like particles. The plasma total cholesterol, VLDL- and LDL-cholesterol and -triacylglycerol concentrations were significantly higher, whereas HDL-cholesterol was lower in hamsters fed fish oil compared with the MIX-diet-fed hamsters. Increasing the amount of fat in the diet increased plasma lipids in both the fish-oil- and the MIX-diet-fed hamsters; however, this hyperlipidaemic effect of dietary fat level was greater in the hamsters fed the fish-oil diet. The hepatic lipid concentrations were not dramatically different between the fish-oil-fed and the MIX-diet-fed hamsters. However, the hepatic LDL-receptor mRNA levels were significantly low in the fish-oil-fed hamsters compared with the MIX-diet-fed hamsters. Increasing the amount of fish oil in the diet further decreased the hepatic LDL-receptor mRNA expression. It is concluded that F1B hamsters are susceptible to fish-oil-induced hyperlipidaemia, especially at high fat levels, and this increase is partially explained by the inhibition of hepatic LDL-receptor mRNA expression.
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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 | 0.001 | 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".