Effects of Gender on Gene Expression in the Blood of Ischemic Stroke Patients
Bibliographic record
Abstract
This study examined the effects of gender on RNA expression after ischemic stroke (IS). RNA obtained from blood of IS patients (n=51; 153 samples at < or =3, 5, and 24 hours) and from matched controls (n=52) were processed on Affymetrix microarrays. Analyses of covariance for stroke versus control samples were performed separately for both genders and the regulated genes for females compared with males. In all, 242, 227, and 338 male-specific genes were regulated at < or =3, 5, and 24 hours after IS, respectively, of which 59 were regulated at all time points. Overall, 774, 3,437, and 571 female-specific stroke genes were regulated at < or =3, 5, and 24 hours, respectively, of which 152 were regulated at all time points. Male-specific stroke genes were associated with integrin, integrin-liked kinase, actin, tight junction, Wnt/β-catenin, RhoA, fibroblast growth factors (FGF), granzyme, and tumor necrosis factor receptor (TNFR)2 signaling. Female-specific stroke genes were associated with p53, high-mobility group box-1, hypoxia inducible factor (HIF)1α, interleukin (IL)1, IL6, IL12, IL18, acute-phase response, T-helper, macrophage, and estrogen signaling. Cell death signaling was overrepresented in both genders, although the molecules and pathways differed. Gender affects gene expression in the blood of IS patients, which likely implies gender differences in immune, inflammatory, and cell death responses to stroke.
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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 | 0.000 | 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) | 0.002 | 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".