MétaCan
Menu
Back to cohort
Record W1775942876

Green tea catechins inhibit vascular endothelial growth factor receptor phosphorylation.

2002· article· en· W1775942876 on OpenAlexaff
Sylvie Lamy, Denis Gingras, Richard Béliveau

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à Montréal
Fundersnot available
KeywordsAngiogenesisGallateVascular endothelial growth factorEpigallocatechin gallatePharmacologyCatechinPhosphorylationChemistryEpicatechin gallateKinase insert domain receptorTyrosine phosphorylationBiochemistryCancer researchVEGF receptorsBiologyVascular endothelial growth factor APolyphenolAntioxidant
DOInot available

Abstract

fetched live from OpenAlex

Vascular endothelial growth factor (VEGF) receptors (VEGFR) play a major role in tumor angiogenesis and, thus, represent attractive targets for the development of novel anticancer therapeutics. In this work, we report that green tea catechins are novel inhibitors of VEGFR-2 activity. Physiological concentrations (0.01-1 microM) of epigallocatechin-3 gallate, catechin-3 gallate, and, to a lesser extent, epicatechin-3 gallate induce a rapid and potent inhibition of VEGF-dependent tyrosine phosphorylation of VEGFR-2. The inhibition of VEGFR-2 by epigallocatechin-3 gallate was similar to that induced by Semaxanib (SU5416), a specific VEGFR-2 inhibitor. The inhibition of VEGFR-2 activity by the catechins displayed positive correlation with the suppression of in vitro angiogenesis. These observations suggest that the anticancer properties of green tea extracts may be related to their inhibition of VEGF-dependent angiogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.204
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations202
Published2002
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicTea Polyphenols and EffectsFrench-language works237,207