Abstract WMP116: Thromboembolism in Atrial Fibrillation: Relationship to Leukocyte Gene Expression
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is an important cause of stroke, for which anticoagulation provides substantial benefit. However, not all patients with AF will have a stroke. There remains uncertainty regarding factors that promote thromboembolism and stroke in patients with AF. In this study we examined differences in blood cell gene expression unique to AF in acute stroke to better understand factors important to atrial fibrillation thromboembolism in human stroke. Methods: Gene expression in blood was compared in acute stroke patients with AF to non-AF stroke and to controls without stroke. Blood was collected in PAXgene tubes, and leukocyte/platelet gene expression was measured by Affymetrix microarray. Differentially expressed genes were identified using ANOVA adjusted for age, sex and batch. Results: In the 184 patients studied, 40 were acute strokes with AF, 143 had non-AF acute stroke, and 116 were non-stroke controls. There were 43 genes unique to AF in patients with stroke, and 69 genes associated AF that were shared between AF stroke and controls (FDR<0.05, fold change>|1.5|). Functional analysis indicate acute stroke AF genes are associated with changes in the hematological system including blood cell rheology and leukocyte activation. In contrast non-stroke AF genes are associated cardiac hypertrophy and blood vessel injury. Conclusions: AF has differences in blood cell gene expression in acute stroke that may relate to risk of thromboembolism. Acute stroke patients with AF display changes in blood cell rheology and leukocyte activation; whereas non-stroke AF patients have changes in cardiac hypertrophy and vascular injury. These differences are important to understanding blood cell contribution to thrombus formation and stroke risk in patients with AF. Further study is required to assess the relationship of these gene changes to stroke risk and response to anticoagulation in patients with AF.
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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 | 0.001 |
| 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) | 0.006 | 0.001 |
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".