The intracerebral hemorrhage blood transcriptome in humans differs from the ischemic stroke and vascular risk factor control blood transcriptomes
Bibliographic record
Abstract
Understanding how the blood transcriptome of human intracerebral hemorrhage (ICH) differs from ischemic stroke (IS) and matched controls (CTRL) will improve understanding of immune and coagulation pathways in both disorders. This study examined RNA from 99 human whole-blood samples using GeneChip® HTA 2.0 arrays to assess differentially expressed transcripts of alternatively spliced genes between ICH, IS and CTRL. We used a mixed regression model with FDR-corrected p(Dx) < 0.2 and p < 0.005 and |FC| > 1.2 for individual comparisons. For time-dependent analyses, subjects were divided into four time-points: 0(CTRL), <24 h, 24–48 h, >48 h; 489 transcripts were differentially expressed between ICH and CTRL, and 63 between IS and CTRL. ICH had differentially expressed T-cell receptor and CD36 genes, and iNOS, TLR, macrophage, and T-helper pathways. IS had more non-coding RNA. ICH and IS both had angiogenesis, CTLA4 in T lymphocytes, CD28 in T helper cells, NFAT regulation of immune response, and glucocorticoid receptor signaling pathways. Self-organizing maps revealed 4357 transcripts changing expression over time in ICH, and 1136 in IS. Understanding ICH and IS transcriptomes will be useful for biomarker development, treatment and prevention strategies, and for evaluating how well animal models recapitulate human ICH and IS.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".