Cranberry extract and quercetin modulate the expression of cyclooxygenase‐2 (COX‐2) and IκBα in human colon cancer cells
Bibliographic record
Abstract
Abstract BACKGROUND: Cranberry (Vaccinium marcocarpon) fruit and quercetin, a major flavonoid found in cranberries, are likely contributors to chemoprevention, and their anti‐inflammatory activities may play a potential role in colon cancer prevention. The aim of this study was to examine the effect of cranberry extract and quercetin on basal expression of cyclooxygenase‐2 (COX‐2) and IκBα as well as the effect on phorbol 12‐myristate 13‐acetate (PMA)‐induced COX‐2 expression in colon cancer cells. RESULTS: HT‐29 human colon adenocarcinoma cells were treated with various concentrations of cranberry extract or quercetin and/or PMA, and the protein expression of COX‐2 and IκBα was determined. The results indicated that cranberry extract and quercetin decreased COX‐2 expression and suppressed degradation of IκBα in unstimulated cells. In PMA‐stimulated cells, cranberry extract was also able to decrease COX‐2 expression and suppress degradation of IκBα. CONCLUSION: The results suggest that a possible mechanism involved in the anti‐cancer activity of cranberry and quercetin is partly mediated through its anti‐inflammatory action. These findings indicate that cranberry and quercetin may reduce the risk of colon cancer possibly by suppressing inflammatory responses. Copyright © 2008 Society of Chemical Industry
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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) | 0.002 | 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".