Consumption of Cranberry Reduces Weight Gain in Mice Fed High Fat Diets
Bibliographic record
Abstract
Obesity and dyslipidemia are major risk factors for cardiovascular disease. To test the health effects of antioxidant‐enriched diets, high‐fat diets (20% by weight) were fed to control (C57BL/6J) and apolipoprotein E‐deficient (ApoE‐/‐) mice, a strain that develops atherosclerotic lesions. Mice were fed either normal AIN‐93 purified diet (CON) or one that contained 5% freeze‐dried cranberry ( Vaccinium macrocarpon; CB) for 8 weeks. There was less weight gain in animals fed CB from weeks 3 through 8 (p<0.05; two‐way ANOVA). At week 8, control mice fed CON weighed 39.4 ± 1.95 g, compared to 35.4 ± 1.55 g for control mice fed CB. Similarly, at week 8, ApoE‐/‐ mice fed CON weighed 40.5 ± 1.22 g, compared to 37.5 ± 1.60 g for those fed CB. The amount of abdominal white adipose tissue was lower in CB‐fed mice. Diets were found to be isocaloric, and there were no differences measured in the levels of carbohydrates, fats, cholesterol, and protein in the 2 diets. ApoE‐/‐ mice had elevated plasma cholesterol, HDL, and triglyceride values (p<0.0001), but diet had no effect on any of these endpoints. Plasma pyruvate, lactate, and keto acids will be measured to determine a potential metabolic mechanism for reduced weight gain. These data suggest that CB diet may improve energy balance which would benefit cardiovascular disease and obesity. (Funded by AIF and NSERC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".