Amniotic fluid stem cell extracellular vesicles as a novel fetal therapy for pulmonary hypoplasia: a review on mechanisms and translational potential
Bibliographic record
Abstract
Disruption of developmental processes affecting the fetal lung leads to pulmonary hypoplasia. Pulmonary hypoplasia results from several conditions including congenital diaphragmatic hernia (CDH) and oligohydramnios. Both entities have high morbidity and mortality, and no effective therapy that fully restores normal lung development. Hypoplastic lungs have impaired growth (arrested branching morphogenesis), maturation (decreased epithelial/mesenchymal differentiation), and vascularization (endothelial dysfunction and vascular remodeling leading to postnatal pulmonary hypertension). Herein, we discuss the pathogenesis of pulmonary hypoplasia and the role of microRNAs (miRNAs) during normal and pathological lung development. Since multiple cells and pathways are altered, the ideal strategy for hypoplastic lungs is to deliver a therapy that addresses all aspects of abnormal lung development. In this review, we report on a novel regenerative approach based on the administration of extracellular vesicles derived from amniotic fluid stem cells (AFSC-EVs). Specifically, we describe the effects of AFSC-EVs in rodent and human models of pulmonary hypoplasia, their mechanism of action via release of their cargo, including miRNAs, and their anti-inflammatory properties. We also compare cargo contents and regenerative effects of EVs from AFSCs and mesenchymal stromal cells (MSCs). Overall, there is compelling evidence that antenatal administration of AFSC-EVs rescues multiple features of fetal lung development in experimental models of pulmonary hypoplasia. Lastly, we discuss the steps that need to be taken to translate this promising EV-based therapy from the bench to the bedside. These include strategies to overcome barriers commonly associated with EV therapeutics and specific challenges related to stem cell-based therapies in fetal medicine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".