Green Tea Polyphenols as Proteasome Inhibitors: Implication in Chemoprevention
Bibliographic record
Abstract
Next to water, tea is the most popular beverage in the world. The most abundant and active compound in green tea is (-)-epigallocatechin-3-gallate (EGCG), which is extensively studied for its cancer-preventive and anti-cancer activities as well as its cellular targets. One potential molecular target of EGCG is the proteasome. While molecular docking and structure-activity relationship (SAR) analysis suggests that the ester carbon of EGCG is important for mediating its proteasome-inhibitory activity, EGCG is very unstable under physiological conditions. Therefore, a series of analogs were synthesized aiming to improve stability and bioavailability of EGCG. Among them, peracetate-protected or the prodrug of EGCG was found to have increased bioavailability, stability, and proteasome-inhibitory activities against various human cancer cells and tumors compared to EGCG, suggesting its potential use for cancer prevention and treatment. Epidemiological studies have indicated that green tea consumption is associated with the reduced risk of cancers, especially associated with the reduced risk of late stage of cancers. This risk reduction may be attributed not only to proteasome inhibition, but also to numerous other intracellular molecules targeted by EGCG that are involved in cell cycle regulation, apoptosis, angiogenesis, and metastasis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".