Curcumin Reduces Tumour Necrosis Factor-Enhanced Annexin V-Positive Microparticle Release in Human Vascular Endothelial Cells
Bibliographic record
Abstract
PURPOSE: Circulating microparticles have been highlighted as biomarkers of cardiovascular disease state and progression. The aim of this study was to evaluate the effects of curcumin on microparticle release from endothelial cells undergoing TNF-induced cell activation and apoptosis. METHODS: This study evaluated the effects of curcumin on microparticle release, cytotoxicity, apoptosis, cell adhesion molecule expression and monocyte adhesion in EAhy926 human endothelial cells. RESULTS: The results showed that the numbers of microparticles were increased by tumour necrosis factor (TNF) or the combination of TNF and cycloheximide (CHX). Curcumin attenuated microparticle release caused by TNF or TNF plus CHX treatments. The pretreatment by curcumin not only negated the accelerated cell death and apoptosis caused by TNF and CHX, but also diminished TNF-induced cell activation, as assessed by reduced surface expression of intercellular adhesion molecule 1, and adhesion of monocytes to endothelial monolayers. CONCLUSION: Curcumin reduced microparticle release from endothelial cells undergoing cell activation and apoptosis, which supports its protective role in TNF-associated endothelial dysfunction, and highlights its potential use as a nutraceutical agent for vascular inflammatory diseases. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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) | 0.001 | 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".