Green Tea Catechins as Novel Antitumor and Antiangiogenic Compounds
Bibliographic record
Abstract
The concept of cancer prevention by use of naturally occuring substances that could be included in the diet is under investigation as a practical approach towards reducing cancer incidence, and therefore the mortality and morbidity associated with this disease. Tea, which is the most popularly consumed beverage aside from water, has been particularly associated with decreased risk of various proliferative diseases such as cancer and atherosclerosis in humans. Various studies have provided evidence that polyphenols are the strongest biologically active agents in green tea. Green tea polyphenols (GTPs) mainly consist of catechins (3-flavanols), of which (-)-epigallocatechin gallate is the most abundant and the most extensively studied. Recent observations have raised the possibility that green tea catechins, in addition to their antioxidative properties, also affect the molecular mechanisms involved in angiogenesis, extracellular matrix degradation, regulation of cell death and multidrug resistance. This article will review the effects and the biological activities of green tea catechins in relation to these mechanisms, each of which plays a crucial role in the development of cancer in humans. The extraction of polyphenols from green tea, as well as their bioavailability, are also discussed since these two important parameters affect blood and tissue levels of the GTPs and consequently their biological activities. In addition, general perspectives on the application of dietary GTPs as novel antiangiogenic and antitumor compounds are also presented.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".