Pharmacologic Evidence of Green Tea in Targeting Tyrosine Kinases
Bibliographic record
Abstract
BACKGROUND: Green tea has been extensively studied for its potential health benefits against diseases, such as cancers, cognitive degenerative diseases, and cardiovascular diseases. METHODS: The authors undertook a structured search of peer-reviewed research articles from three databases including PubMed, Embase, and Ovid MEDLINE. Recent and up-to-date studies relevant to the topic were included. RESULTS: Green tea extract exerts its functions by interacting with multiple signalling pathways in human cells. Protein tyrosine kinase is one of the examples. Abnormal activation of tyrosine kinase is observed in some tumour cells. Green tea extract inhibits phosphorylation, reduces expression, or attenuates downstream signalling of epidermal growth factor receptor, insulin-like growth factor receptor, vascular endothelial growth factor receptor, and non-receptor tyrosine kinase. Combination of green tea extract with tyrosine kinase inhibitors may provide synergistic effects by overcoming acquired resistance. CONCLUSION: Green tea extract can affect multiple receptor targets. In the current review, we discuss the pharmacological mechanisms of green tea on tyrosine kinases and their implications on common diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 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".