Evolving Role of Microparticles in the Pathophysiology of Endothelial Dysfunction
Bibliographic record
Abstract
BACKGROUND: Endothelial dysfunction is an early event in the development and progression of a wide range of cardiovascular diseases. Various human studies have identified that measures of endothelial dysfunction may offer prognostic information with respect to vascular events. Microparticles (MPs) are a heterogeneous population of small membrane fragments shed from various cell types. The endothelium is one of the primary targets of circulating MPs, and MPs isolated from blood have been considered biomarkers of vascular injury and inflammation. CONTENT: This review summarizes current knowledge of the potential functional role of circulating MPs in promoting endothelial dysfunction. Cells exposed to different stimuli such as shear stress, physiological agonists, proapoptotic stimulation, or damage release MPs, which contribute to endothelial dysfunction and the development of cardiovascular diseases. Numerous studies indicate that MPs may trigger endothelial dysfunction by disrupting production of nitric oxide release from vascular endothelial cells and subsequently modifying vascular tone. Circulating MPs affect both proinflammatory and proatherosclerotic processes in endothelial cells. In addition, MPs can promote coagulation and inflammation or alter angiogenesis and apoptosis in endothelial cells. SUMMARY: MPs play an important role in promoting endothelial dysfunction and may prove to be true biomarkers of disease state and progression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".