P-018 Utilizing extracellular vesicle-associated cytokines to differentiating cerebrovascular disease pathologies
Bibliographic record
Abstract
Objective/Goals The ability to differentiate cerebrovascular disease (CVD) in plasma could be an invaluable theragnostic. CVD encompasses a spectrum of conditions (stenosis, thrombosis, embolism, hemorrhage) that alters intracranial blood flow. The University of Kentucky (UK) has two CVD biobanks called the ‘Blood And Clot Thrombectomy Registry And Collaboration’ (BACTRAC; NCT03153683) and ‘Moyamoya and Stroke Tissue Evaluation and Repository’ (MASTER). BACTRAC collects blood during diagnostic angiograms for aneurysms, carotid stenosis, arteriovenous malformations, arteriovenous fistula, and intracranial atherosclerotic disease. Additionally, during a mechanical thrombectomy for emergent large vessel occlusions (ELVOs), BACTRAC collects blood (intracranial and systemic) and clot from stroke subjects. MASTER collects blood from patients diagnosed with Moyamoya vasculopathy, an unique CVD that presents with internal carotid artery terminus stenosis and abnormal vascular collaterals. Key barriers to providing therapies for CVD are the spectrum of pathologies altering intracranial blood flow and the lack of blood biomarkers to identify cerebrovascular changes before overt pathological and cognitive changes are present. Therefore, the ability to detect cerebrovascular cellular alterations systemically within extracellular vesicles (EVs) could be an invaluable theragnostic. We have previously shown elevated levels of T cells and their associated cytokines in the intracranial blood of stroke subjects in BACTRAC. Since EVs can readily cross the blood-brain barrier, we hypothesize systemic EV expression of cytokines associated with T-cells (IL-17, Interferon gamma (IFN-γ), and IL-4) can be clinically relevant to differentiate types and stages of CVD. Methods/Study Population To test this hypothesis, EVs were isolated from plasma of CVD using an IZON AFCV2 Platform. Following size exclusion chromatography (qEV 70nM), EVs were concentrated and protein was analysed using an S-Plex MSD assay for IL-17A, IFN-γ, and IL-4. Results Our CVD subjects included Moyamoya (n=20, 66.7% female), carotid stenosis (n=4, 75% female), aneurysm (n=10, 70% female), and ELVO (n=12, 75% female) subjects with no significant difference in sex, body mass indexes, hypertension, smoking status, diabetes, or distance travelled (residing county to hospital indicating access to health care) between groups. Subjects with an ELVO were significantly older (72.75 ± 3.3 years) compared to subjects with Moyamoya (47.2.1 ±2.0, p<0.0001) and aneurysms (50.9 ± 5.5, p=0.01). Significantly higher EV IFN-γ was measured in Moyamoya (14.0 ± 1.0 (pg/mL)/total EV protein (mg/mL)) and aneurysm (18.89 ± 2.9) subjects compared to carotid stenosis (7.2 ± 2.7, p<0.5 and p<0.01, respectively) and ELVO (8.9 ± 0.9, p<0.05 and p<0.01, respectively). However, in ELVO subjects, EV IFN-γ correlated to edema volume (p<0.05, rs=-0.821), EV IL17-a correlated to admission NIHSS (p<0.05, r=0.578), and to PHQ-9 (p<0.01, r=0.573) scores. Interestingly, EV IL-4 plasma concentration, at the time of thrombectomy, correlated to a Montreal Cognitive Assessment score at discharge (p=0.052, r=0.876). In Moyamoya, EV IL-17 correlated to age and subjects with bilateral Moyamoya vasculopathies were younger (41.2 ± 1.6 years, p<0.05) compared to unilateral (49.0 ± 4.2). These data suggest that EV associated IFN-γ, IL17-a, and IL-4 are related to specific cerebrovascular pathologies, and could provide some insight into type and severity of cerebrovascular disease. Disclosured A. Trout: 1; C; Joe Niekro Award/SNIS, KL2/NIH NCATS. J. Roberts: None. C. O’Dell: None. C. Prince: None. L. Whitnel: None. N. Milson: None. M. Al-Kawaz: None. S. Pahwa: None. J. Fraser: None. A. Trout: None. K. Pennypacker: None. A. Stowe: None. J. Fraser: None.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".