Investigation of the effects of cranberry fractions on atherosclerosis in mice
Bibliographic record
Abstract
Atherosclerosis, a chronic inflammatory disease characterized by formation of fatty plaques in arteries, is a major contributor to cardiovascular risk. Oxidation of LDL by reactive oxygen species is thought to be involved in atherosclerosis progression. Cranberries ( Vaccinium macrocarpon ) are rich in flavonoid and phenolic antioxidants, and therefore we evaluated the effects of cranberry diets in ApoE‐deficient mice, a model of atherosclerosis. ApoE‐deficient mice and control mice were fed a high fat (20%) diet containing either 0%, 3% or 10% freeze‐dried cranberry for 8 wks. Total weight (g) at 8 wks was lower in control mice fed 3% and 10% cranberry diets and in ApoE‐deficient mice fed 10% cranberry compared to mice fed control diet, despite comparable food consumption rates between groups (~3.5 g/day). In control mice, total plasma cholesterol levels were reduced by ~20% (p=0.042) by feeding 3% cranberry for 8 wks, and by ~15% (p=0.013) and ~20% (p=0.032) by feeding 10% cranberry for 6 and 8 wks respectively. Cholesterol levels were reduced by ~17% (p=0.053) in the ApoE deficient mice fed 10% cranberry for 8 weeks. The current results indicate a potential beneficial effect of cranberry extracts on the prevention of atherosclerosis development mice. Supported by the Cranberry Institute of Massachusetts and the AIF in grants to MS and CN.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".