Using Peripheral Blood Mononuclear Cells to Determine a Gene Expression Profile of Acute Ischemic Stroke
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Bibliographic record
Abstract
BACKGROUND: Direct brain biopsy is rarely indicated during acute stroke. This study uses peripheral blood mononuclear cells (PBMCs) to determine whether a systemic gene expression profile could be demonstrated in patients with acute ischemic stroke. METHODS AND RESULTS: Using oligonucleotide microarrays, we compared the gene expression profile of an index cohort of 20 patients with confirmed ischemic stroke on neuroimaging studies with that of 20 referent subjects. Validation studies used quantitative real-time polymerase chain reaction to measure the levels of 9 upregulated genes in the index cohort, and an independent cohort of 9 patients and 10 referent subjects was prospectively studied to determine the accuracy of the Prediction Analysis for Microarrays list to classify stroke. After correction for multiple comparisons with the Bonferroni technique, 190 genes were significantly different between the stroke and referent groups. Broad classes of genes included white blood cell activation and differentiation (approximately 60%), genes associated with hypoxia and vascular repair, and genes potentially associated with an altered cerebral microenvironment. Real-time polymerase chain reaction confirmed increased mRNA expression in 9 of 9 upregulated stroke-associated genes in the index cohort. A panel of 22 genes derived from the Prediction Analysis for Microarrays algorithm in the index cohort classified stroke in the validation cohort with a sensitivity of 78% and a specificity of 80%. Control for the Framingham stroke risk score revealed only a partial dependence of the stroke gene expression profile in PBMCs on vascular risk. CONCLUSIONS: This study demonstrated an altered gene expression profile in PBMCs during acute ischemic stroke. Some genes with altered expression were consistent with an adaptive response to central nervous system ischemia.
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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 it