Peripheral Blood Gene Expression at 3 to 24 Hours Correlates with and Predicts 90-Day Outcome Following Human Ischemic Stroke
Bibliographic record
Abstract
Abstract This study identified early immune gene responses in peripheral blood associated with 90-day ischemic stroke (IS) outcomes and an early gene profile that predicted 90-day outcomes. Peripheral blood from the CLEAR trial IS patients was compared to vascular risk factor matched controls. Whole-transcriptome analyses identified genes and networks associated with 90-day IS outcome (NIHSS-NIH Stroke Scale, mRS-modified Rankin Scale). The expression of 467, 526, and 571 genes measured at ≤3, 5 and 24 hours after IS, respectively, were associated with poor 90-day mRS outcome (mRS=3-6), while 49, 100 and 35 associated with good mRS 90-day outcome (mRS=0-2). Poor outcomes were associated with up-regulated MMP9 , S100A12 , interleukin-related and STAT3 pathways. Weighted Gene Co-Expression Network Analysis (WGCNA) revealed modules significantly associated with 90-day outcome. Poor outcome modules were enriched in down-regulated T cell and monocyte-specific genes plus up-regulated neutrophil genes and good outcome modules were associated with erythroblasts and megakaryocytes. Using the difference in gene expression between 3 and 24 hours, 10 genes correctly predicted 100% of patients with Good 90-day mRS outcome and 67% with Poor mRS outcome (AUC=0.88) in a validation set. The predictors included AVPR1A , which mediates platelet aggregation, release of coagulation factors and exacerbates the brain inflammatory response; and KCNK1 ( TWIK-1 ), a member of a two-pore potassium channel family, which like other potassium channels likely modulates stroke outcomes. This study suggests the immune response after stroke impacts long-term functional outcomes. Furthermore, early post-stroke gene expression may predict stroke outcomes and outcome-associated genes could be targets for improving outcomes.
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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) | 0.001 | 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".