Blueberry supplementation lowers iNOS but not COX‐2 expression in LPS challenged rats
Bibliographic record
Abstract
Inflammation has been implicated in a number of chronic diseases including cardiovascular disease and aging. We examined whether supplementation with blueberry extracts would decrease LPS‐induced inflammation in vivo . Male Wistar rats (125–150 g) were fed control (AIN‐93G) or supplemented (0.02% post C18 blueberry extract) diet for 4 weeks. Feed intake was monitored daily and weight gain weekly. Animals (n=9) were subjected to LPS or saline injections (5 mg/kg i.p.); after 3 hours they were anesthetized with sodium pentobarbital (60 mg/kg) and blood was collected by cardiac puncture. Liver, lungs, brain and kidneys were collected, flash frozen in liquid nitrogen and stored at −80°C until analysis. One‐way ANOVA revealed no differences in feed intake (p=0.653), weight gain (p=0.563) or feed efficiency (p=0.868) among the three groups. Analysis of serum inflammatory markers by ELISA demonstrated a significant increase in TNFα (p=0.000) and IL‐1β (p=0.016) but not IL‐6 (p=0.241) in LPS treated animals when compared to saline treated controls. Supplementation with blueberries failed to significantly ameliorate these increases. Assessment of liver COX‐2 protein level by Western blot showed no differences among groups. Conversely, liver iNOS levels were increased with LPS treatment; blueberry supplementation reduced these levels to control. Analysis of COX‐2 and iNOS gene expression by real‐time PCR revealed no changes in COX‐2 among groups. Blueberry treatment failed to ameliorate the LPS‐induced increase in iNOS expression. Our results suggest that the anti‐inflammatory effects of blueberries may work through reduction in nitric oxide production by iNOS. Support by NSERC and AIF grants to KGP and MS.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".