P-018 Utilizing extracellular vesicle-associated cytokines to differentiating cerebrovascular disease pathologies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».