Transfer of microRNA-486-5p from human endothelial colony forming cell–derived exosomes reduces ischemic kidney injury
Bibliographic record
Abstract
Administration of human cord blood endothelial colony-forming cells (ECFCs) or their exosomes protects mice against kidney ischemia/reperfusion injury. Here we studied the microRNA (miRNA) content of ECFC exosomes and the role of miRNA transfer in kidney and endothelial cell protection. ECFC exosomes were enriched in miR-486-5p, which targets the phosphatase and tensin homolog (PTEN) and the Akt pathway. In cultured endothelial cells exposed to hypoxia, incubation with ECFC exosomes increased miR-486-5p, decreased PTEN, and stimulated Akt phosphorylation. Exposure of hypoxic endothelial cells to conditioned medium from ECFCs pretreated with anti-miR-486-5p blocked increases in miR-486-5p and phosphorylated Akt, restored expression of PTEN, and enhanced apoptosis. Coculture of endothelial cells with ECFCs enhanced endothelial miR-486-5p levels. Targeting of PTEN by miR-486-5p was observed in endothelial cells, and PTEN knockdown blocked apoptosis. In mice with ischemic kidney injury, infusion of ECFC exosomes induced potent functional and histologic protection, associated with increased kidney miR-486-5p levels, decreased PTEN, and activation of Akt. Infusion of exosomes from ECFCs transfected with anti-miR-486-5p had no protective effect. Thus, delivery of ECFC exosomes reduces ischemic kidney injury via transfer of miR-486-5p targeting PTEN. Exosomes enriched in miR-486-5p could represent a therapeutic tool in acute kidney injury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".