Abstract TP210: Small Extracellular Vesicle-derived Micrornas Differentiate Ischemic Stroke And Intracerebral Hemorrhage: A Pilot Study
Bibliographic record
Abstract
Investigating the short and long-term signaling mechanisms via small extracellular vesicles (sEVs) in peripheral blood following human Ischemic Stroke (IS) and Intracerebral Hemorrhage (ICH) is of great interest. The sEV’s cargo can induce distant responses which can be used to derive potential novel therapeutic targets and biomarkers to differentiate the two brain pathologies. Thus, we sequenced the miRNA cargo derived from peripheral blood sEVs in IS (n=3), ICH (n=3), and Vascular Risk Factor-matched Control (VRFC; n=3) subjects. Subjects were divided into contrast groups (IS vs VRFC, ICH vs VRFC, ICH vs IS), and log2 transformed expression underwent Kruskal-Wallis tests to identify differentially expressed (DE; p<0.05) miRNAs. We found 55 DE miRNAs in IS vs VRFC, 38 in ICH vs VRFC, and 45 in ICH vs IS ( Fig. 1A ). The combination of these miRNAs differentiated the three groups on Principal Components Analysis ( Fig. 1B ). IS associated miRNA included miR-30a, miR-30b, and miR-144. miR-30a is involved in hematopoietic stem cell self-renewal and can impair B cell differentiation. miR-30b may be an immune suppressor via Notch1. In male mice, miR-144 is protective against atherosclerosis. ICH associated miRNA included miR-195, miR-1-3p, and miR-20b-5p. miR-195 can inhibit the pro-inflammatory roles of macrophages. miR-1-3p is involved in cardiomyocyte development, can target TLR1 (Toll-Like Receptor 1), and may regulate autophagy. miR-20b-5p reduces Amyloid Precursor Protein (APP) mRNA and protein levels; vascular accumulation of APP is one cause of Lobar ICH. We show differential expression of sEV-derived miRNAs in peripheral blood of human IS and ICH patients that are involved in relevant signaling processes. sEV cargo profiles pose a largely underexplored intercellular signaling mechanism in IS and ICH with the potential to better characterize long distance signaling from injured brain to peripheral blood leukocytes in these brain disorders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".