Administration of amniotic fluid stem cell extracellular vesicles promotes development of fetal hypoplastic lungs by immunomodulating lung macrophages
Bibliographic record
Abstract
Abstract Congenital diaphragmatic hernia (CDH) is a devastating condition characterized by incomplete closure of the diaphragm and herniation of abdominal organs into the chest. As a result, fetuses have pulmonary hypoplasia, whose severity is the main determinant of poor outcome. The pathogenesis of pulmonary hypoplasia secondary to CDH is at least in part explained by lack or dysregulation of miRNAs that are known to regulate lung developmental processes. Herein, we report that intra-amniotic administration of extracellular vesicles derived from amniotic fluid stem cells (AFSC-EVs) rescues lung growth and maturation in a fetal rat model of CDH. To understand which fetal lung cells and biological pathways are affected by AFSC-EVs, we conducted whole lung single nucleus RNA-sequencing. We discovered that CDH lungs have a multilineage inflammatory signature with macrophage enrichment, and confirmed these findings in autopsy samples of lungs from human fetuses with CDH. Transcriptomic analysis of CDH fetal rat lungs also showed that AFSC-EV treatment reduced macrophage density and inflammation to normal levels. Analyzing the miRNAs contained in the AFSC-EV cargo with validated mRNA targets, we found that the downregulated genes in AFSC-EV treated CDH lungs were involved in inflammatory response and immune system processes. This study reports a single cell atlas of normal and hypoplastic CDH fetal rat lungs and provides evidence that AFSC-EVs restore lung development by addressing multiple pathophysiological aspects of CDH. One Sentence Summary Amniotic fluid stem cell extracellular vesicle treatment for fetal lung macrophage modulation
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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) | 0.002 | 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".