micro-RNA-486-5p protects against kidney ischemic injury and modifies the apoptotic transcriptome in proximal tubules
Bibliographic record
Abstract
Acute kidney injury (AKI) carries high morbidity and mortality, and effective treatments are lacking. Preclinical models support involvement of micro-RNAs (miRs) in AKI pathogenesis, although effects on the kidney transcriptome are unclear. We previously showed that injection of cord blood endothelial colony forming cell-derived exosomes, enriched in miR-486-5p, prevented ischemic AKI in mice. To further define this, we studied direct effects of miR-486-5p in mice with kidney ischemia-reperfusion injury. RNA-Seq was used to compare the impact of miR-486-5p and exosomes on the transcriptome of proximal tubules and kidney endothelial cells 24 hours after ischemia-reperfusion. In mice with AKI, injection of miR-486-5p mimic increased its levels in proximal tubules and endothelial cells, and improved plasma creatinine, histological injury, neutrophil infiltration, and apoptosis. Additionally, miR-486-5p inhibited expression of its target phosphatase and tensin homolog, and activated protein kinase B. In proximal tubules, miR-486-5p or exosomes reduced expression of genes associated with ischemic injury and the tumor necrosis factor (TNF) pathway, and altered distinct apoptotic genes. In endothelial cells, genes associated with metabolic processes were altered by miR-486-5p or exosomes, although TNF pathway genes were not affected. Thus, our results suggest that miR-486-5p may have therapeutic potential in AKI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".