Fetal lung underdevelopment is rescued by administration of amniotic fluid stem cell extracellular vesicles in rodents
Bibliographic record
Abstract
Fetal lung underdevelopment, also known as pulmonary hypoplasia, is characterized by decreased lung growth and maturation. The most common birth defect found in babies with pulmonary hypoplasia is congenital diaphragmatic hernia (CDH). Despite research and clinical advances, babies with CDH still have high morbidity and mortality rates, which are directly related to the severity of lung underdevelopment. To date, there is no effective treatment that promotes fetal lung growth and maturation. Here, we describe a stem cell-based approach in rodents that enhances fetal lung development via the administration of extracellular vesicles (EVs) derived from amniotic fluid stem cells (AFSCs). Using fetal rodent models of pulmonary hypoplasia (primary epithelial cells, organoids, explants, and in vivo), we demonstrated that AFSC-EV administration promoted branching morphogenesis and alveolarization, rescued tissue homeostasis, and stimulated epithelial cell and fibroblast differentiation. We confirmed this regenerative ability in in vitro models of lung injury using human material, where human AFSC-EVs obtained following good manufacturing practices restored pulmonary epithelial homeostasis. Investigating EV mechanism of action, we found that AFSC-EV beneficial effects were exerted via the release of RNA cargo. MicroRNAs regulating the expression of genes involved in lung development, such as the miR17-92 cluster and its paralogs, were highly enriched in AFSC-EVs and were increased in AFSC-EV-treated primary lung epithelial cells compared to untreated cells. Our findings suggest that AFSC-EVs hold regenerative ability for underdeveloped fetal lungs, demonstrating potential for therapeutic application in patients with pulmonary hypoplasia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".