Abstract WP212: Association Of Thrombin Generation With Leukocyte Inflammatory Profile In Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
Thrombosis is central to the pathogenesis of acute ischemic stroke, with higher thrombin generation being associated with increased stroke risk. The immune system may contribute to thrombin generation in stroke and thus may offer novel strategies for stroke prevention. This study addresses the research question regarding the relationship of thrombin generation to leukocyte gene expression in patients with acute ischemic stroke. We isolated RNA from whole blood and examined the relationship to thrombin generation capacity in patients with acute ischemic stroke. Due to its effects on thrombin generation, patients on anticoagulants were excluded from the study. The relationship of gene expression with peak thrombin was evaluated by analysis of covariance across peak thrombin quartiles adjusted for sex and age. In 97 patients with acute ischemic stroke, peak thrombin was variable, ranging from 252.0 to 752.4 nM. Increased thrombin generation was associated with differences in thromboinflammatory leukocyte gene expression, including a decrease in ADAM metallopeptidase with thrombospondin type 1 motif 13 and an increase in nuclear factor B (NF-kB)-activating protein, protein disulfde isomerase family A member 5, and tissue factor pathway inhibitor 2. Pathways associated with peak thrombin included interleukin 6 signaling, thrombin signaling, and NF-kB signaling. A linear discriminant analysis model summarizing the immune activation associated with peak thrombin in a first cohort of stroke could distinguish patients with low peak thrombin from high peak thrombin in a second cohort of 112 patients with acute ischemic stroke.The identified genes and pathways support a role of the immune system contributing to thrombus formation in patients with stroke. These may have relevance to antithrombotic strategies for stroke prevention.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".