Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) are receiving increased attention as circulating biomarkers of disease. Since they are produced by cells of the cardiovascular system, their contents (including lipids, proteins and microRNAs) can be measured to provide information about the relative health or dysfunction of the cardiovascular system. We recently demonstrated that healthy endothelium can package anti-inflammatory microRNAs into secreted EVs. The transfer of EV-encapsulated microRNAs can suppress monocyte activation through the targeting of genes encoding inflammatory signaling components in the recipient cell. The microRNA content of EVs therefore serve not only as a biomarker but can functionally contribute to cell-cell communication in the cardiovascular system to affect vascular health and disease. We have developed a methodology for high-throughput profiling of microRNAs in human plasma EV samples and will present data elucidating microRNA biomarkers in the setting of co-morbidities that elevate cardiovascular risk. In these settings, we are identifying predictive biomarkers of cardiovascular disease and are determining the functional importance of EVs to disease progression. Disclosures No relevant conflicts of interest to declare.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".