mRNA Expression Profiles from Whole Blood Associated with Vasospasm in Patients with Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Though there are many biomarker studies of plasma and serum in patients with aneurysmal subarachnoid hemorrhage (SAH), few have examined blood cells that might contribute to vasospasm. In this study, we evaluated inflammatory and prothrombotic pathways by examining mRNA expression in whole blood of SAH patients with and without vasospasm. METHODS: Adult SAH patients with vasospasm (n = 29) and without vasospasm (n = 21) were matched for sex, race/ethnicity, and aneurysm treatment method. Diagnosis of vasospasm was made by angiography. mRNA expression was measured by Affymetrix Human Exon 1.0 ST Arrays. SAH patients with vasospasm were compared to those without vasospasm by ANCOVA to identify differential gene, exon, and alternatively spliced transcript expression. Analyses were adjusted for age, batch, and time of blood draw after SAH. RESULTS: At the gene level, there were 259 differentially expressed genes between SAH patients with vasospasm compared to patients without (false discovery rate < 0.05, |fold change| ≥ 1.2). At the exon level, 1210 exons representing 1093 genes were differentially regulated between the two groups (P < 0.005, ≥ 1.2 |fold change|). Principal components analysis segregated SAH patients with and without vasospasm. Signaling pathways for the 1093 vasospasm-related genes included adrenergic, P2Y, ET-1, NO, sildenafil, renin-angiotensin, thrombin, CCR3, CXCR4, MIF, fMLP, PKA, PKC, CRH, PPARα/RXRα, and calcium. Genes predicted to be alternatively spliced included IL23A, RSU1, PAQR6, and TRIP6. CONCLUSIONS: This is the first study to demonstrate that mRNA expression in whole blood distinguishes SAH patients with vasospasm from those without vasospasm and supports a role of coagulation and immune systems in vasospasm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".