The inhibition of TNFα‐induced monocyte chemoattractant protein‐1 production by epigallocatechin‐3‐O‐gallate in vascular endothelial cells
Bibliographic record
Abstract
Monocyte chemoattractant protein‐1 (MCP‐1) plays a pivotal role in the recruitment of monocytes and in the development of atherosclerosis. Epigallocatechin‐3‐O‐gallate (EGCG), the major catechin derived from green tea, has multiple pharmacologic effects. In this study, we investigated the mechanism(s) by which EGCG inhibits TNFα‐induced MCP‐1 production in bovine coronary artery endothelial cell (BCAEC). TNFα (5 ng/ml) increased MCP‐1 production (284±19%, n=6, P<0.001) in BCAEC. EGCG (40 μM) significantly attenuated TNFα‐induced MCP‐1 production (31±11%, n=9, P<0.001). On the other hand, inhibitors of MEK (PD98059, 50 μM), PI‐3 kinase (LY294002, 10 μM; wortmannin, 20 nM), c‐Jun N‐terminal kinase (SP600125, 20 μM), but not p38 MAPK (SB203580, 10 μM), decreased TNFα‐induced MCP‐1 production . Interestingly, EGCG markedly reduced phosphoryaltion of Akt by TNFα. Conclusions EGCG decreased TNFα‐induced MCP‐1 production in BCAEC . The mechanism for TNFα effect of MCP‐1 in endothelial cell involves Erk1/2, PI‐3 kinase and c‐Jun N‐terminal kinase pathways. Determining the effect of EGCG on these signaling pathway will contribute to understanding its beneficial effects on cardiovascular disease. Dr. HYA is a recipient of visiting scholarship from Alberta Heritage Foundation for Medical Research
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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 | 0.000 | 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) | 0.001 | 0.000 |
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.
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".