Unique Tracheal Fluid MicroRNA Signature Predicts Response to FETO in Patients With Congenital Diaphragmatic Hernia
Bibliographic record
Abstract
OBJECTIVE AND BACKGROUND: Our objective was to determine the fetal in vivo microRNA signature in hypoplastic lungs of human fetuses with severe isolated congenital diaphragmatic hernia (CDH) and changes in tracheal and amniotic fluid of fetuses undergoing fetoscopic endoluminal tracheal occlusion (FETO) to reverse severe lung hypoplasia due to CDH. METHODS: We profiled microRNA expression in prenatal human lungs by microarray analysis. We then validated this signature with real-time quantitative polymerase chain reaction in tracheal and amniotic fluid of CDH patients undergoing FETO. We further explored the role of miR-200b using semiquantitative in situ hybridization and immunohistochemistry for TGF-β2 in postnatal lung sections. We investigated miR-200b effects on TGF-β signaling using a SMAD-luciferase reporter assay and Western blotting for phospho-SMAD2/3 and ZEB-2 in cultures of human bronchial epithelial cells. RESULTS: CDH lungs display an increased expression of 2 microRNAs: miR-200b and miR-10a as compared to control lungs. Fetuses undergoing FETO display increased miR-200 expression in their tracheal fluid at the time of balloon removal. Future survivors of FETO display significantly higher miR-200 expression than those with a limited response. miR-200b was expressed in bronchial epithelial cells and vascular endothelial cells. TGF-β2 expression was lower in CDH lungs. miR-200b inhibited TGF-β-induced SMAD signaling in cultures of human bronchial epithelial cells. CONCLUSIONS: Human fetal hypoplastic CDH lungs have a specific miR-200/miR-10a signature. Survival after FETO is associated with increased miR-200 family expression. miR-200b overexpression in CDH lungs results in decreased TGF-β/SMAD signaling.
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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 | 0.001 |
| 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 | 0.000 |
| 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".