Dietary Whole Cranberry (Vaccinium macrocarpon) Modulates Plasma Lipid and Cytokine Profiles, and Prevents Liver Toxicity in Response to Cholesterol‐Feeding in the JCR‐LA‐cp Corpulent Rat Model
Bibliographic record
Abstract
The antioxidant and anti‐inflammatory properties of natural products like cranberry ( Vaccinium macrocarpon ) offer a novel approach to treating heart disease. The purpose of this study was to determine the effects of dietary whole cranberry on parameters of atherosclerosis using the obese JCR: LA‐cp rat model. Four diet groups were assigned: control (CON), 1% added cholesterol (CHOL), 3% added cranberry (CRAN), and cranberry plus cranberry (CRAN + CHOL). Results show animals fed cholesterol have higher plasma low‐density lipoprotein (LDL) cholesterol levels (2 Way ANOVA; p = 0.015) and a lower high density lipoprotein (HDL) to LDL ratio (p = 0.003), while animals fed cranberry have higher HDL: LDL (p = 0.047). Furthermore, cranberry lowers levels of the anti‐inflammatory cytokine interleukin (IL)‐10, indicating these animals require less endogenous anti‐inflammatory protection. Tests of liver function indicate that cranberry is hepato‐protective: plasma AST levels, a chemical indicator of liver toxicity, were higher in animals fed cholesterol (p = 0.003), while cranberry reversed this effect. A similar trend was observed with another liver enzyme ALT. These results indicate that dietary cranberry, incorporated at physiologically relevant concentrations, has both cardio‐ and hepato‐ protective properties. [Funded by CIHR, PEI Health Research Program, and AIF through ACOA]
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".