Dietary flaxseed inhibits atherosclerosis in the LDL receptor-deficient mouse in part through antiproliferative and anti-inflammatory actions
Bibliographic record
Abstract
Dietary flaxseed has been shown to have potent antiatherogenic effects in rabbits. The purpose of the present study was to investigate the antiatherogenic capacity of flaxseed in an animal model that more closely represents the human atherosclerotic condition, the LDL receptor-deficient mouse (LDLrKO), and to identify the cellular mechanisms for these effects. LDLrKO mice were administered a regular diet (RG), a 10% flaxseed-supplemented diet (FX), or an atherogenic diet containing 2% cholesterol alone (CH) or supplemented with 10% flaxseed (CF), 5% flaxseed (CF5), 1% flaxseed (CF1), or 5% coconut oil (CS) for 24 wk. LDLrKO mice fed a cholesterol-supplemented diet exhibited a rise in plasma cholesterol without a change in triglycerides and an increase in atherosclerotic plaque formation. The CS mice exhibited elevated levels of plasma cholesterol, triglycerides, and saturated fatty acids and an increase in plaque development. Supplementation of the cholesterol-enriched diet with 10% (wt/wt) ground flaxseed lowered plasma cholesterol and saturated fatty acids, increased plasma ALA, and inhibited plaque formation in the aorta and aortic sinus compared with mice fed a diet supplemented with only dietary cholesterol. The expression of proliferating cell nuclear antigen (PCNA) and the inflammatory markers IL-6, mac-3, and VCAM-1 was increased in aortic tissue from CH and CS mice. This expression was significantly reduced or normalized when flaxseed was included in the diet. Our results demonstrate that dietary flaxseed can inhibit atherosclerosis in the LDLrKO mouse through a reduction of circulating cholesterol levels and, at a cellular level, via antiproliferative and anti-inflammatory actions.
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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 | 0.001 | 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".