Abstract P646: The Aging Immune Transcriptome is Linked to Ischemic Stroke Outcome
Bibliographic record
Abstract
Background: Advancing age is associated with changes to the immune system, which affect stroke outcome. We previously demonstrated an age-associated alteration in leukocyte gene expression in patients with ischemic stroke. The aim of this study is to validate these findings in an independent stroke cohort and assess the relationship to stroke outcome. Methods: Genes associated with age were identified in a cohort of 57 patients with acute ischemic stroke. Peripheral blood RNA was measured using whole genome microarrays and genes associated with advancing age identified (FDR-corrected p < 0.05, partial correlation coefficient r > |0.3|); age-associated gene expression differences in patients with poor 90-day outcome (mRS < 2) were also compared. Genes were functionally characterized by pathway and enrichment analysis and verified against age-associated genes from two additional stroke cohorts containing a total of 173 patients. Results: There were 536 genes associated with age in the new stroke cohort, of which 286 decreased and 253 increased with age. Thirty-nine (39) of the age-associated genes were present in previous stroke cohorts analyzed, including a decrease in CCR6, CXCR5, BLNK and NT5E. A decrease in CXCR5 and CD79B was also identified in patients with poor outcome. Pathways and enriched terms relating to the humoral adaptive system immune system, including B-cell and lymphocyte activation, were among those represented in age-associated genes. Conclusions: Age-related changes in leukocyte gene expression were validated in an independent cohort of patients with ischemic stroke. Verified changes include alterations to the humoral immune system and a relationship to stroke outcome. Further investigation of the aging immune system in stroke is warranted.
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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 | 0.001 |
| 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".