MicroRNAs as sentinels and protagonists of carotid artery thromboembolism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it