Using Peripheral Blood Mononuclear Cells to Determine a Gene Expression Profile of Acute Ischemic Stroke
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".