Extracellular Vesicles as Protagonists of Diabetic Cardiovascular Pathology
Bibliographic record
Abstract
Extracellular vesicles (EVs) represent an emerging mechanism of cell-cell communication in the cardiovascular system. Recent data suggest that EVs are produced and taken up by multiple cardiovascular cell types, influencing target cells through signaling or transfer of cargo (including proteins, lipids, messenger RNA, and non-coding RNA). The concentration and contents of circulating EVs are altered in several diseases and represent explicit signatures of cellular activation, making them of particular interest as circulating biomarkers. EVs also actively contribute to the progression of various cardiovascular diseases, including diabetes-related vascular disease. Understanding the relationships between circulating EVs, diabetes, and cardiovascular disease is of importance as diabetic patients are at elevated risk for developing several debilitating cardiovascular pathologies, including diabetic cardiomyopathy (DCM), a disease that remains an enigma at the molecular level. Enhancing and exploiting our understanding of EV biology could facilitate the development of effective non-invasive diagnostics, prognostics, and therapeutics. This review will focus on EV biology in diabetic cardiovascular diseases, including atherosclerosis and DCM. We will review EV biogenesis and functional properties, as well as provide insight into their emerging role in cell-cell communication. Finally, we will address the utility of EVs as clinical biomarkers and outline their impact as a biomedical tool in the development of therapeutics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".