Blueberry Puree Increases HDL‐cholesterol in Rats
Bibliographic record
Abstract
Oxidative stress has come to the forefront as a contributor to a number of diseases, including cancer, neurodegenerative and cardiovascular diseases (CVD). High‐fat diets (HFD) are thought to increase LDL, which can contribute to oxidative stress by becoming oxidized in artery walls, leading to atherosclerotic plaques that promote CVD. Blueberry (BB) polyphenolics are thought to act as antioxidants, which can potentially mitigate oxidative stress and improve CVD outcomes. The objective of our study was to evaluate the effect of a HFD supplemented with BB puree on serum lipids and biomarkers of oxidative stress. Male Wistar‐Kyoto rats (n=7) were fed either a control diet (CON), CON with 5mL/kg BB (CON+BB), 20% lard diet (HFD) or HFD with BB (HFD+BB) for eight weeks. A serum lipid panel was measured in a clinical laboratory analyzer and urinary 8‐isoprostane, a marker of lipid peroxidation, and urinary nitrite, a product of free‐radical reactivity, were measured using Cayman and Promega kits, respectively. HDL in CON group was 0.747±0.014mM and was increased by 10% in CON+BB to 0.822±0.019mM (p=0.002, ANOVA). All other serum lipids, urinary 8‐isoprostane and urinary nitrite were not significantly changed by either HFD or BB enrichment. We have shown that feeding BB with a normal, healthy diet can improve serum HDL, which could potentially improve excretion of cholesterol from artery walls, improving CVD. Funding support provided by NSERC, UPEI internal grants and WBANA.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".