Cranberry ( <i>Vaccinium macrocarpon</i> ) extract reduces tumour necrosis factor alpha ‐induced expression of cyclooxygenase‐2 and inducible nitric oxide synthase in vascular smooth muscle cells
Bibliographic record
Abstract
Natural ‘bioactive’ factors have the ability to attenuate atherogenesis in in vitro and in vivo models. The effects of cranberry on the expression of two inflammation‐linked enzymes, cyclooxygenase‐2 (COX‐2) and inducible nitric oxide synthase (iNOS), were examined in aortic smooth muscle (A7r5) cells in the absence/presence of tumour necrosis factor alpha (TNFα). Western blot analysis revealed that while cranberry did not affect base‐line protein expression of COX‐2, it did reduce base‐line levels of iNOS protein. Cranberry was able to effectively reduce the TNFα‐stimulated induction of both these enzymes. This reduction was observed after 6, 12, and 24 hours pre‐exposure to cranberry prior to exposure to TNFα, and after a 6 hour co‐incubation of cranberry with TNF. NFkB (a transcriptional regulator of COX‐2 and iNOS) expression was assessed in response to cranberry treatment. Base‐line cytosolic levels of the phosphorylated form of NFkB (p‐NFkB) were reduced following exposure to cranberry. Cranberry also reduced TNFα‐stimulated p‐NFkB levels. Taken together, these results suggest that cranberry may partly modulate the inflammatory response observed in atherosclerosis by affecting the expression of COX‐2 and iNOS, and that this modulation may occur through interaction with the NFkB transcription factor. The temporal/signal transduction mechanisms involved are being studied. [CIHR‐RPP, ACOA‐AIF‐2, UMassD‐Cranberry Research Program]
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".