Differential Expression of Vascular-Related MicroRNA in Circulating Endothelial Microvesicles in Adults With Spinal Cord Injury: A Pilot Study
Bibliographic record
Abstract
Background Spinal cord injury (SCI) is associated with an increased risk and prevalence of cardiopulmonary and cerebrovascular disease-related morbidity and mortality. The factors that initiate, promote, and accelerate vascular diseases and events in SCI are poorly understood. Clinical interest in circulating endothelial cell-derived microvesicles (EMVs) and their microRNA (miRNA) cargo has intensified due to their involvement in endothelial dysfunction, atherosclerosis, and cerebrovascular events. Objectives The aim of this study was to determine whether a subset of vascular-related miRNAs is differentially expressed in EMVs isolated from adults with SCI. Methods We assessed eight adults with tetraplegia (7 male/1 female; age: 46±4 years; time since injury: 26±5 years) and eight uninjured (6 male/2 female; age: 39±3 years). Circulating EMVs were isolated, enumerated, and collected from plasma by flow cytometry. The expression of vascular-related miRNAs in EMVs was assessed by RT-PCR. Results Circulating EMV levels were significantly higher (~130%) in adults with SCI compared with uninjured adults. The expression profile of miRNAs in EMVs from adults with SCI were significantly different than uninjured adults and were pathologic in nature. Expression of miR-126, miR-132, and miR-Let-7a were lower (~100–150%; p < .05), whereas miR-30a, miR-145, miR-155, and miR-216 were higher (~125–450%; p < .05) in EMVs from adults with SCI. Conclusion This study is the first examination of EMV miRNA cargo in adults with SCI. The cargo signature of vascular-related miRNAs studied reflects a pathogenic EMV phenotype prone to induce inflammation, atherosclerosis, and vascular dysfunction. EMVs and their miRNA cargo represent a novel biomarker of vascular risk and a potential target for intervention to alleviate vascular-related disease after SCI.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".