Endothelial and leukocyte-derived microvesicles and cardiovascular risk after stroke: PROSCIS-B
Bibliographic record
Abstract
Objective: To determine the role of circulating endothelial microvesicles (EMV) and microvesicles (MV) of other origins on long-term cardiovascular outcomes after stroke, we measured them in a cohort of first-ever stroke patients and observed them for three years. Methods: In the PROSpective Cohort with Incident Stroke Berlin (PROSCIS-B), patients with first-ever ischemic stroke were followed for three years. The primary combined endpoint consisted of recurrent stroke, myocardial infarction, and all-cause mortality. Levels of EMV, leukocyte-derived MV (LMV), monocytic MV (MMV), and platelet-derived MV (PMV) were measured in citrate blood using flow cytometry. Kaplan-Meier curves and Cox proportional hazards models were used to estimate the effect of MV levels on the combined endpoint after adjustment confounding. Results: 571 patients were recruited (median age 69y; 39% female; median NIHSS 2, interquartile range 1-4). During the follow-up, 95 endpoints occurred. Patients with levels of EMV [adjusted hazard ratio (HR)=2.5, 95% confidence interval (CI) 1.2-4.9] or LMV (HR=3.1, 95%CI 1.4-6.8) in the highest quartile were more likely to experience an event than participants with lower levels using the lowest quartile as reference category. The association was less pronounced for PMV (HR=1.7, 95%CI 0.9-3.2) and absent for MMV (HR=1.1, 95%CI 0.6-1.8). Conclusion: High levels of EMV and LMV after ischemic stroke were associated with worse cardiovascular outcome within three years. These results reinforce that endothelial dysfunction and vascular inflammation affect the long-term prognosis after stroke. EMV and LMV might play a potential role in risk prediction for stroke patients.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".