RNA in blood is altered prior to hemorrhagic transformation in ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: Hemorrhagic transformation (HT) is a major complication of ischemic stroke that worsens outcomes and increases mortality. Disruption of the blood-brain barrier is a central feature of HT pathogenesis, and leukocytes may contribute to this process. We sought to determine whether ischemic strokes that develop HT have differences in RNA expression in blood within 3 hours of stroke onset prior to treatment with thrombolytic therapy. METHODS: Stroke patient blood samples were obtained prior to treatment with thrombolysis, and leukocyte RNA was assessed by microarray analysis. Strokes that developed HT (n = 11) were compared to strokes without HT (n = 33) and controls (n = 14). Genes were identified (corrected p < 0.05, fold change ≥|1.2|), and functional analysis was performed. RNA prediction of HT in stroke was evaluated using cross-validation, and in a second stroke cohort (n = 52). RESULTS: Ischemic strokes that developed HT had differential expression of 29 genes in circulating leukocytes prior to treatment with thrombolytic therapy. A panel of 6 genes could predict strokes that later developed HT with 80% sensitivity and 70.2% specificity. Key pathways involved in HT of human stroke are described, including amphiregulin, a growth factor that regulates matrix metalloproteinase-9; a shift in transforming growth factor-β signaling involving SMAD4, INPP5D, and IRAK3; and a disruption of coagulation factors V and VIII. INTERPRETATION: Identified genes correspond to differences in inflammation and coagulation that may predispose to HT in ischemic stroke. Given the adverse impact of HT on stroke outcomes, further evaluation of the identified genes and pathways is warranted to determine their potential as therapeutic targets to reduce HT and as markers of HT risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".