Abstract WP67: Acute ischemic stroke patients with high senescence gene expression have worse outcomes.
Bibliographic record
Abstract
Introduction: Senescent cells accumulate with advancing age, and exposure to cellular stressors, such as oxidative and inflammatory molecules, which can be circulating in blood. Senescent endothelial cells have altered cellular functions that may influence stroke outcomes. Studying senescence might reveal improved strategies to improve vascular health and prevent stroke or better outcomes. Hypothesis: Acute ischemic stroke patients with enriched senescence gene set expression have worse 90-day stroke outcomes. Methods: In 225 patients with acute ischemic stroke, RNA was isolated from whole blood and transcriptome measured by microarray. Enrichment of senescence genes was assessed using the SenMayo gene set in Gene Set Enrichment Analysis (GSEA). The relationship between the SenMayo senescence score and patients’ 90-day modified Rankin scale (mRS) outcome was determined. Results: SenMayo senescence genes are enriched (p=0.05) in circulation of patients of acute ischemic stroke with bad outcome (90 day mRS>2) and not in patients with good outcomes (90 day mRS≤2) (fig.1). This suggests a role for senescent cells in the pathophysiology of stroke or as a contributing factor to the failure of the patient's full recovery. Conclusion: In acute ischemic stroke patients, increased senescence gene expression is associated with worse stroke outcome. Further evaluation is needed to determine whether targeting senescence could be a strategy to improve outcome in stroke.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.007 | 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".