RNA expression studies in stroke: what can they tell us about stroke mechanism?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Diagnosis of stroke and understanding the mechanism of stroke is critical to implement optimal treatment. RNA expressed in peripheral blood cells is emerging as a precision biomarker to aid in stroke diagnosis and prediction of stroke cause. In this review, we summarize available data regarding the role of RNA to predict stroke, the rationale for these changes, and a discussion of novel mechanistic insight and clinical applications. RECENT FINDINGS: Differences in RNA gene expression in blood have been identified in patients with stroke, including differences to distinguish ischemic from hemorrhagic stroke, and differences between cardioembolic, large vessel atherosclerotic, and small vessel lacunar stroke cause. Gene expression differences show promise as novel stroke biomarkers to predict stroke of unclear cause (cryptogenic stroke). The differences in RNA expression provide novel insight to stroke mechanism, including the role of immune response and thrombosis in human stroke. Important insight to regulation of gene expression in stroke and its causes are being acquired, including alternative splicing, noncoding RNA, and microRNA. SUMMARY: Improved diagnosis of stroke and determination of stroke cause will improve stroke treatment and prevention. RNA biomarkers show promise to aid in the diagnosis of stroke and cause determination, as well as providing novel insight to mechanism of stroke in patients. While further study is required, an RNA profile may one day be part of the stroke armamentarium with utility to guide acute stroke therapy and prevention strategies and refine stroke phenotype.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".