Acute ischemic stroke and reperfusion drive molecular immune-vascular activations detectable in peripheral blood
Bibliographic record
Abstract
BACKGROUND: Inflammation drives damage in acute ischemic stroke (AIS). Here, we map temporal and molecular mechanisms of immune-vascular response in patients with AIS treated with endovascular thrombectomy (EVT) for anterior circulation large-vessel occlusion. METHODS: In this prospective cohort, 52 patients underwent serial peripheral blood sampling at groin puncture (Pre), catheter withdrawal (T0), and 6, 24, and 48 hours post-reperfusion. Thirteen immune and vascular players were quantified by mesoscale multiplex assays. Clinical outcomes were the modified Rankin Scale (mRS) score at 3 months and the National Institutes of Health Stroke Scale (NIHSS) at 24 hours. RESULTS: Adjusted by age, baseline Alberta Stroke Program Early CT Score (ASPECTS) and NIHSS scores, higher pre-EVT peripheral blood levels of interleukin (IL)-1β, IL-4, IL-10, and IL-13 were associated with poorer 24-hours NIHSS. Post-EVT reperfusion, IL-6 and its downstream effectors vascular cell adhesion molecule- (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1) levels rose in peripheral blood over time, suggesting cerebrovascular inflammation, accompanied by the increased levels of acute-phase reactants C-reactive protein (CRP) and serum amyloid A (SAA), indicative of a systemic inflammatory engagement. In the same timeframe, interferon-gamma (IFN-γ) blood levels decreased. Adjusted by age, baseline ASPECT and NIHSS scores, and pre-thrombectomy biomarker levels, higher post-EVT levels of IL-6, VCAM-1, ICAM-1, and SAA were associated with poorer 24-hours NIHSS and unfavorable mRS 3 month outcomes, supporting an evolving immune dysregulation following AIS. CONCLUSION: This exploratory study points to immune and vascular activation mechanisms from pre- to post-EVT, representing possible disease indicators and targets.
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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 | 0.001 | 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".