Fetal lung vascular development is disrupted by mechanical compression and rescued by administration of amniotic fluid stem cell extracellular vesicles via regulation of the Hippo signaling pathway
Bibliographic record
Abstract
Abstract Postnatal pulmonary hypertension is the biggest treatment challenge and major determinant for poor outcome in infants with congenital diaphragmatic hernia (CDH). CDH lungs are hypoplastic and exhibit vascular remodeling, whose pathogenesis remains poorly understood. Using a novel micro-static compression system, herein we found that mechanical compression induces vascular remodeling and downregulation of key angiogenic markers in rat and human fetal lung models, with similar features observed in CDH fetal lung autopsy samples. These fetal lung vascular changes are reversed back to normal upon administration of extracellular vesicles derived from amniotic fluid stem cells (AFSC-EVs), a regenerative approach previously shown to restore lung branching morphogenesis and epithelial differentiation in CDH models. Exploring pathways that are dysregulated in CDH lungs and involved in mechanotransduction, we found that compressed fetal lungs had altered expression of Hippo signaling factors that was restored upon AFSC-EV administration. We found that AFSC-EV cargo contains some miRNAs involved in lung vascular development and Hippo pathway, indicating that AFSC-EV regenerative effects is associated with the delivery of specific miRNAs. This study uncovers the role of mechanical compression that herniated organs exert on CDH fetal lungs and proposes a new cell-free strategy to restore normal fetal lung vascular development.
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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) | 0.000 | 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".