Abstract WP535: Differential Immune Activation in Patients With Acute Ischemic Stroke and Admission Blood Pressure Greater Than 185/110 mm Hg
Bibliographic record
Abstract
Background: A blood pressure > 185/110 mm Hg is associated with increased risk of tPA related hemorrhagic transformation (HT). Stroke guidelines recommend blood pressure >185/110 mm Hg be lowered before tPA treatment. How high blood pressure increases blood-brain barrier disruption and risk of hemorrhagic transformation remains poorly understood. We evaluated peripheral leukocyte activation in stroke patients in relation to elevated blood pressure and their potential contribution to blood-brain barrier disruption. Methods: Blood samples from acute ischemic stroke patients were collected within 3 hours of stroke onset, prior to treatment with thrombolytic. Patients were grouped by BP >185/110 mm Hg (n=19) and BP <185/110 mm Hg (n=46). Total blood RNA was assessed by whole genome microarray and differential gene expression analyzed by ANCOVA. Functional analysis of identified genes was performed. Correlation analysis was conducted to identify genes correlated with systolic blood pressure. Results: Strokes with admission BP >185/110 mm Hg had 231 genes differentially expressed compared to strokes with BP <185/110 mm Hg (p <0.05, fold change ≥|1.2|). Key genes and pathways associated with BP>185/110 mm Hg included downregulation of caveolin-1 and upregulation of matrix metalloproteinases (MMPs). Several of these genes, including MMP-21, linearly correlated with increasing systolic blood pressure (r=0.25, p = 0.02). Conclusions: A blood pressure >185/110 mm Hg is associated with differential immune activation in patients with acute ischemic stroke, including caveolin-1 and matrix metalloproteinases. These differences may contribute to blood-brain barrier disruption and risk of hemorrhagic transformation in acute stroke patients with blood pressure >185/110 mm Hg. Whether modulating immune activation could reduce blood-brain barrier disruption and risk of HT requires further study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".