P-026 Extracellular vesicle neurotrophin expression in plasma collected during mechanical thrombectomy from patients with emergent large vessel stroke
Bibliographic record
Abstract
Introduction Mechanical thrombectomy (MT) and tissue plasminogen activator are ischemic stroke treatments that assist in restoring blood flow to the brain tissue, but do not guarantee good outcomes. Detection of cellular alterations systemically in extracellular vesicles (EVs) could be invaluable to theragnostic. EVs are nanoparticles released from cells that carrying stimuli specific cargo (e.g. lipids, proteins, and nucleic acids) from one cell to another. We hypothesize that the ratio of pro brain derived neurotrophic factor (proBDNF) to BDNF expression (proBDNF/BDNF) can be clinically relevant and used to predict stroke outcomes. Methods Human ischemic stroke plasma, collected during MT, and cardiovascular disease (CVD) control plasma, collected during diagnostic angiograms, were unbanked from the ‘Blood And Clot Thrombectomy Registry And Collaboration’ (BACTRAC; NCT03153683). EVs were isolated (Exoquick) then measured (Zetaview-NTA) before quantification of proBDNF and BDNF. Results Stroke subjects (n=29) were significantly older (67 vs. 56 years; p=0.03) than the controls (n=18), though there was no difference in the representation of sex (p>0.9), body mass (p=0.64), or the presence of hypertension (p=0.50). Baseline EV characteristics also showed no significant difference in size (124.1 nm vs. 125.6 nm; p=0.9) or concentration (1.88 X109 vs. 1.89 X109; p=0.9). Stroke subjects exhibited increased EV proBDNF/BDNF compared to the controls (10.56 ± 1.8 vs. 4.13 ± 0.78; p=0.0008). This was primarily driven by significantly higher EV BDNF in stroke patients compared to controls (59 pg/mg vs. 14 pg/mg; p=0.027) and a lower variation of EV proBDNF expression in stroke (211.8 ± 26.6 vs.175.8 ± 42.6 pg/mL) compared to controls. EV proBDNF/BDNF positively correlated longer infarct time (p=0.0946, r2= 0.12) and decreased cognition (i.e., Montreal Cognitive Assessment (MoCA), p=0.03, r2= 0.487). Discussion These data suggest that EV proBDNF/BDNF levels can reflect vascular changes that lead to decreased cognition and should be explored further for translational applications. Disclosures A. Trout: None. D. Britsch: None. W. Naberhaus: None. J. Turchan-Cholewo: None. C. McLouth: None. L. Whitnel-Smith: None. J. Frank: None. J. Roberts: None. P. Shivani: None. D. Dornboss III: None. J. Harp: 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 | 0.000 | 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) | 0.003 | 0.001 |
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".