Common and distinct circulating microRNAs in four neurovascular disorders
Bibliographic record
Abstract
Background Familial cerebral cavernous malformations (FCCM), Sturge-Weber Syndrome (SWS), and hereditary hemorrhagic telangiectasia (HHT) are driven by genetic mutations causing varying vascular dysmorphism and risk of brain bleeding. Cerebral microbleeds (CMBs) are associated with the aging process with less characterized genetic drivers. This study hypothesizes that common and distinct circulating microRNAs (miRNAs) can reflect mechanisms of vascular dysmorphism and bleeding, which can serve as potential biomarkers in clinical contexts. Methods Differentially expressed (DE) plasma miRNAs ( p < 0.05, FDR corrected, absolute fold change [|FC|]>1.5]) were identified between patients with FCCM, SWS, HHT, and CMB, compared to age and sex matched healthy patients. Ingenuity Pathway Analysis as well as transcriptome integration analyses were performed to identify gene targets of the DE miRNAs and their associated pathways. Preselected miRNAs were validated using ddPCR. Results Eleven circulating DE miRNAs were identified in FCCM, 40 in SWS, 41 in HHT, and 26 in CMB ( p < 0.05, FDR-corrected, [|FC|]>1.5]). Further analyses showed that 18 DE miRNAs were commonly dysregulated in any two of the studied neurovascular disorders. The PI3K-Akt and ROBO SLIT signaling pathways were identified across all four disorders. The plasma levels of four miRNAs were further validated ( p < 0.05) using ddPCR. Conclusion The common dysregulated miRNAs across neurovascular disorders reflect shared mechanistic pathways underlying vascular dysmorphism and bleeding. These findings pave the way for further mechanistic exploration of these miRNAs, and their potential clinical application for disease monitoring and therapeutic intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".