Bibliographic record
Abstract
Endothelial dysfunction, characterized as a pro-coagulative, pro-inflammatory, secretory and vasoconstrictory phenotype, is an early event in many chronic diseases that frequently precedes cardiovascular complications. Associated with these processes is formation of microparticles, which are anuclear fragments of cellular membrane shed from stressed or damaged cells. Microparticles contain surface proteins and cytoplasmic content of the parental cells and have been identified as biomarkers of endothelial dysfunction in many cardiovascular diseases. However recent evidence indicates that microparticles themselves exert injurious endothelial effects. This is evidenced by our recent studies demonstrating that microparticles 1) interact physically with endothelial cells, 2) stimulate oxidative stress and expression of cell adhesion proteins in endothelial cells, 3) promote a phenotype of senescence and 4) directly modulate vascular contraction/relaxation. Exact mechanisms for this cross-talk between microparticles and endothelial cells are unclear, although reactive oxygen species, lipid rafts and RhoA/Rho kinase may play a role. Accordingly in addition to reflecting endothelial stress/damage, microparticles may themselves have biological activity and contribute to vascular dysfunction. Such processes might occur through a feed-forward system where endothelial cell injury leads to microparticle formation, which in turn interacts with endothelial cells to stimulate pro-inflammatory and vasoconstrictor signaling to amplify endothelial dysfunction. This paradigm identifies putative novel vascular targets that may both reflect and contribute to vascular dysregulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.018 |
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".