Acute respiratory distress vs healthy lung environments differently affect mesenchymal stromal cell extracellular vesicle miRNAs
Bibliographic record
Abstract
The acute respiratory distress syndrome (ARDS) inflammatory environment alters mesenchymal stromal cell (MSC) gene and protein expression but effects on microRNA (miRNA) content of MSC-extracellular vesicle (EVs) remain unknown. To assess this, sequencing analysis of EV-miRNAs prepared from human bone marrow-derived MSCs (hMSCs) exposed ex vivo to bronchoalveolar lavage fluid (BALF) from ARDS patients or healthy volunteers (HV) identified a number of differentially expressed miRNAs. Discriminant, differential expression, and functional enrichment analyses identified 14 miRNAs significantly changed following ARDS versus HV BALF exposure. Network analysis showed 4 (miR-760, miR-3175, miR-885-3p, and miR-766-3p) of the 14 EV-miRNAs formed a regulatory "hub", suggesting co-targeting of specific gene pathways. In silico prediction identified a number of pathways important in lung injury. Two miRNAs involved in regulation of the cystic fibrosis transmembrane conductance regulator (CFTR), miRNA-145-5p and miRNA-138-5p, were also significantly increased in ARDS BALF-exposed hMSCs EVs. Functionally, EVs from hMSCs exposed to either ARDS or HV BALF had differential effects on CFTR Cl - secretion by cultured primary human bronchial epithelial cells, an effect predicted to reduce mucociliary clearance. The potential clinical impact of these finding highlights the need for further studies assessing the role of hMSC-EV miRNAs in regulating lung inflammation and mucociliary clearance.
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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 | 0.000 | 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 | 0.001 | 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".