Collagen biomaterial stimulates the production of extracellular vesicles containing microRNA‐21 and enhances the proangiogenic function of CD34 <sup>+</sup> cells
Bibliographic record
Abstract
ABSTRACT CD34 + cells are promising for revascularization therapy, but their clinical use is limited by low cell counts, poor engraftment, and reduced function after transplantation. In this study, a collagen type I biomaterial was used to expand and enhance the function of human peripheral blood CD34 + cells, and potential underlying mechanisms were examined. Compared to the fibronectin control substrate, biomaterial‐cultured CD34 + cells from healthy donors had enhanced proliferation, migration toward VEGF, angiogenic potential, and increased secretion of CD63 + CD81 + extracellular vesicles (EVs). In the biomaterial‐derived EVs, greater levels of the angiogenic microRNAs (miRs), miR‐21 and ‐210, were detected. Notably, biomaterial‐cultured CD34 + cells had reduced mRNA and protein levels of Sprouty (Spry)1, which is an miR‐21 target and negative regulator of endothelial cell proliferation and angiogenesis. Similar to the results of healthy donor cells, biomaterial culture increased miR‐21 and ‐210 expression in CD34 + cells from patients who underwent coronary artery bypass surgery, which also exhibited improved VEGF‐mediated migration and angiogenic capacity. Therefore, collagen biomaterial culture may be useful for expanding the number and enhancing the function of CD34 + cells in patients, possibly mediated through suppression of Spryl activity by EV‐derived miR‐21. These results may provide a strategy to enhance the therapeutic potency of CD34 + cells for vascular regeneration.—McNeill, B., Ostojic, A., Rayner, K. J., Ruel, M., Suuronen, E. J. Collagen biomaterial stimulates the production of extracellular vesicles containing microRNA‐21 and enhances the proangiogenic function of CD34 + cells. FASEB J. 33, 4166–4177 (2019). www.fasebj.org
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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 | 0.001 | 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 | 0.001 |
| 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".