MicroRNAs as sentinels and protagonists of carotid artery thromboembolism
Bibliographic record
Abstract
Stroke is the leading cause of serious disability in the world and a large number of ischemic strokes are due to thromboembolism from unstable carotid artery atherosclerotic plaque. As it is difficult to predict plaque rupture and surgical treatment of asymptomatic disease carries a risk of stroke, carotid disease continues to present major challenges with regard to clinical decision-making and revascularization. There is therefore an imminent need to better understand the molecular mechanisms governing plaque instability and rupture, as this would allow for the development of biomarkers to identify at-risk asymptomatic carotid plaque prior to disease progression and stroke. Further, it would aid in creation of therapeutics to stabilize carotid plaque. MicroRNAs (miRNAs) have been implicated as key protagonists in various stages of atherosclerotic plaque initiation, development and rupture. Notably, they appear to play a crucial role in carotid artery thromboembolism. As the molecular pathways governing the role of miRNAs are being uncovered, we are learning that their involvement is complex, tissue- and stage-specific, and highly selective. Notably, miRNAs can be packaged and secreted in extracellular vesicles (EVs), where they participate in cell-cell communication. The measurement of EV-encapsulated miRNAs in the circulation may inform disease mechanisms occurring in the plaque itself, and therefore may serve as sentinels of unstable plaque as well as therapeutic targets.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".