Endothelial extracellular vesicle miR-423-5p regulates microvascular homeostasis and renal function after ischemia-reperfusion injury
Bibliographic record
Abstract
Microvascular rarefaction substantially contributes to renal dysfunction following ischemia-reperfusion injury (IRI). We characterized the microRNA signature of extracellular vesicles (EVs) released during endothelial apoptosis to identify biomarkers and regulators of microvascular rarefaction and renal dysfunction. Using in vitro models and RNA-Seq, we found miR-423-5p, let-7b-5p, and let-7c-5p enriched in small EVs from apoptotic endothelial cells. In mouse models of renal IRI and a cohort of 51 patients who have undergone renal transplant with delayed graft function, serum miR-423-5p correlated with circulating EVs, while let-7b-5p and let-7c-5p were also present in free form. Early acute kidney injury saw increased serum miR-423-5p levels linked to small EVs with endothelial markers. Over time, higher serum miR-423-5p levels were associated with large EVs and correlated with greater renal microvascular density and reduced fibrosis. Microvascular density and fibrosis predicted renal function 3 years after transplantation. We explored miR-423-5p's role in renal homeostasis, finding that its injection during renal IRI preserved microvascular density and inhibited fibrosis. Endothelial cells transfected with miR-423-5p showed enhanced resistance to apoptosis, increased migration, and angiogenesis. Localized miR-423-5p injection in hindlimb ischemia model accelerated revascularization. These findings position miR-423-5p as a predictor of renal microvascular rarefaction and fibrosis, highlighting potential strategies for preserving renal function.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".