Ischemic Transient Neurological Events Identified by Immune Response to Cerebral Ischemia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Deciphering whether a transient neurological event (TNE) is of ischemic or nonischemic etiology can be challenging. Ischemia of cerebral tissue elicits an immune response in stroke and transient ischemic attack (TIA). This response, as detected by RNA expressed in immune cells, could potentially distinguish ischemic from nonischemic TNE. METHODS: Analysis of 208 TIAs, ischemic strokes, controls, and TNE was performed. RNA from blood was processed on microarrays. TIAs (n=26) and ischemic strokes (n=94) were compared with controls (n=44) to identify differentially expressed genes (false discovery rate <0.05, fold change ≥1.2). Genes common to TIA and stroke were used predict ischemia in TIA diffusion-weighted imaging-positive/minor stroke (n=17), nonischemic TNE (n=13), and TNE of unclear etiology (n=14). RESULTS: Seventy-four genes expressed in TIA were common to those in ischemic stroke. Functional pathways common to TIA and stroke related to activation of innate and adaptive immune systems, involving granulocytes and B cells. A prediction model using 26 of the 74 ischemia genes distinguished TIA and stroke subjects from control subjects with 89% sensitivity and specificity. In the validation cohort, 17 of 17 TIA diffusion-weighted imaging-positive/minor strokes were predicted to be ischemic, and 10 of 13 nonischemic TNE were predicted to be nonischemic. In TNE of unclear etiology, 71% were predicted to be ischemic. These subjects had higher ABCD(2) scores. CONCLUSIONS: A common molecular response to ischemia in TIA and stroke was identified, relating to activation of innate and adaptive immune systems. TNE of ischemic etiology was identified based on gene profiles that may be of clinical use once validated.
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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 | 0.000 |
| 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".