Epigallocatechin gallate (EGCG) modulates senescent endothelial cell-monocyte communication in age-related vascular inflammation
Bibliographic record
Abstract
Aging significantly affects intercellular communication between vascular endothelial cells (ECs) and hematopoietic cells, leading to vascular inflammation and age-associated diseases. This study determined how senescent ECs communicate with monocytes, whether extracellular vesicles (EVs) released from senescent ECs affect monocyte functions, and investigated the potential for epigallocatechin-3-gallate (EGCG), a flavonoid in green tea, to reverse these effects. Human umbilical vein endothelial cells (HUVECs) were treated with Etoposide (10 µM, 24 h) to induce senescence, followed by EGCG (100 µM, 24 h) treatment to evaluate its potential as a senotherapeutic agent. The interaction between ECs and monocytes was analyzed using a co-culture system and direct treatment of monocytes with EC-derived EVs. EGCG reduced senescence-associated phenotypes in ECs, as evidenced by decreased senescence-associated (SA)-β-Gal activity and reversal of Etoposide-induced senescence markers. Monocytes co-cultured with EGCG-treated senescent ECs showed decreased pro-inflammatory responses compared to those co-cultured with untreated senescent ECs. Additionally, senescent ECs produced more EVs than non-senescent ECs. EVs from senescent ECs enhanced lipopolysaccharide (LPS)-induced pro-inflammatory activation of monocytes, whereas EVs from EGCG-treated senescent ECs mitigated this activation, maintaining monocyte activation at normal levels. Our findings reveal that EGCG confers anti-senescent effects via modulation of the senescent EC secretome (including EVs) with the capacity to modify monocyte activation. These findings suggest that EGCG could act as a senotherapeutic agent to reduce vascular inflammation related to aging.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".