Abstract 252: Impact of Endothelial Microparticles on Endothelial Cell Signaling and Endothelium-dependent Vasorelaxation
Bibliographic record
Abstract
Microparticles (MPs), fragments of membrane shed from stressed/damaged cells, are found in the plasma of healthy individuals with levels increased in vascular disease. However, whether MPs themselves contribute to endothelial dysfunction and damage is unclear. We examined the effects of endothelial MPs (eMPs) on cultured endothelial cells (ECs) in vitro and on the function of isolated mesenteric arteries ex vivo . eMPs were isolated from the media of cultured mouse aortic ECs and quantified by flow cytometry. ECs were treated with eMPs (10 5 /ml) and effects on kinase signaling pathways (Akt, c-Src, ERK1/2, p38 MAPK), superoxide anion generation (dihydroethidium) and NO production (diaminofluorescin) were examined. eMPs increased phosphorylation of ERK1/2 at 5, 15, and 30 minutes post-treatment (P<0.05) and increased phosphorylation of c-Src at 2, 4, and 8 hours post-treatment (P<0.05). Phosphorylation of p38 MAPK and Akt were not altered by eMP exposure. eMPs increased superoxide anion production (208% of control, P<0.05) and decreased ionomycin-induced NO production (39% of control, P<0.05) in ECs after 4 hours. Additionally, the sensitivity to acetylcholine was impaired in MP-treated vessels (pD2: 5.6±0.2) compared to untreated vessels (pD2: 6.7±0.1, P<0.01), assessed in 2nd order mesenteric arteries using wire myography. Finally, we explored mechanisms by which MPs achieve their effects. Co-treatment with an epidermal growth factor receptor inhibitor (AG1478, 10 μM), but not a platelet-derived growth factor receptor inhibitor (Tyrphostin AG1296, 10 μM), blocked microparticle-mediated effects on superoxide generation, and NO production in ECs. In summary, we demonstrate that eMPs impair vasorelaxation ex vivo and activate kinase signaling pathways, promote superoxide production and inhibit endothelial NO production in vitro . These effects may be mediated, at least in part, through EGFR.
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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) | 0.004 | 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".