Inhibition of cancer cell proliferation and suppression of TNF-induced activation of NFkappaB by edible berry juice.
Bibliographic record
Abstract
BACKGROUND: Berries contain several phytochemicals, such as phenolic acids, proanthocyanidins, anthocyanins and other flavonoids. There has been growing interest in a variety of potential chemopreventive activities of edible berries. The potential chemopreventive activity of a variety of small berries cultivated or collected in the province of Québec, Canada were evaluated here. MATERIALS AND METHODS: Strawberry, raspberry, black currant, red currant, white currant, gooseberry, high-bush blueberry, low-bush blueberry, velvet leaf blueberry, serviceberry, blackberry, black chokeberry, sea buckthorn and cranberry were evaluated for antioxidant capacity, anti-proliferative activity, anti-inflammatory activity, induction of apoptosis and cell cycle arrest. RESULTS: The growth of various cancer cell lines, including those of stomach, prostate, intestine and breast, was strongly inhibited by raspberry, black currant, white currant, gooseberry, velvet leaf blueberry, low-bush blueberry, sea buckthorn and cranberry juice, but not (or only slightly) by strawberry, high-bush blueberry, serviceberry, red currant, or blackberry juice. No correlation was found between the anti-proliferative activity of berry juices and their antioxidant capacity (p > 0.05). The inhibition of cancer cell proliferation by berry juices did not involve caspase-dependent apoptosis, but appeared to involve cell-cycle arrest, as evidenced by down-regulation of the expression of cdk4, cdk6, cyclin D1 and cyclin D3. Of the 13 berries tested, juice of 6 significantly inhibited the TNF-induced activation of COX-2 expression and activation of the nuclear transcription factor NFkappaB. CONCLUSION: These results illustrate that berry juices have striking differences in their potential chemopreventive activity and that the inclusion of a variety of berries in the diet might be useful for preventing the development of tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".