Green tea extracts decrease carcinogen‐induced mammary tumor burden in rats and rate of breast cancer cell proliferation in culture
Bibliographic record
Abstract
Epidemiological evidence suggests tea (Camellia sinensis L.) has chemopreventive effects against various tumors. Green tea contains many polyphenols, including epigallocatechin-3 gallate (EGCG), which possess anti-oxidant qualities. Reduction of chemically induced mammary gland carcinogenesis by green tea in a carcinogen-induced rat model has been suggested previously, but the results reported were not statistically significant. Here we have tested the effects of green tea on mammary tumorigenesis using the 7,12-dimethylbenz(a)anthracene (DMBA) Sprague-Dawley (S-D) rat model. We report that green tea significantly increased mean latency to first tumor, and reduced tumor burden and number of invasive tumors per tumor-bearing animal; although, it did not affect tumor number in the female rats. Furthermore, we show that proliferation and/or viability of cultured Hs578T and MDA-MB-231 estrogen receptor-negative breast cancer cell lines was reduced by EGCG treatment. Similar negative effects on proliferation were observed with the DMBA-transformed D3-1 cell line. Growth inhibition of Hs578T cells correlated with induction of p27(Kip1) cyclin-dependent kinase inhibitor (CKI) expression. Hs578T cells expressing elevated levels of p27(Kip1) protein due to stable ectopic expression displayed increased G1 arrest. Thus, green tea had significant chemopreventive effects on carcinogen-induced mammary tumorigenesis in female S-D rats. In culture, inhibition of human breast cancer cell proliferation by EGCG was mediated in part via induction of the p27(Kip1) CKI.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".