Fetal lung regeneration using stem cell‐derived extracellular vesicles: A new frontier for pulmonary hypoplasia secondary to congenital diaphragmatic hernia
Bibliographic record
Abstract
The poor outcomes of babies with congenital diaphragmatic hernia (CDH) are directly related to pulmonary hypoplasia, a condition characterized by impaired lung development. Although the pathogenesis of pulmonary hypoplasia is not fully elucidated, there is now evidence that CDH patients have missing or dysregulated microRNAs (miRNAs) that regulate lung development. A prenatal therapy that supplements these missing/dysregulated miRNAs could be a strategy to rescue normal lung development. Extracellular vesicles (EVs), also known as exosomes when of small dimensions, are lipid-bound nanoparticles that can transfer their heterogeneous cargo (proteins, lipids, small RNAs) to target cells to induce biological responses. Herein, we review all studies that show evidence for stem cell-derived EVs as a regenerative therapy to rescue normal development in CDH fetal lungs. Particularly, we report studies showing that administration of EVs derived from amniotic fluid stem cells (AFSC-EVs) to models of pulmonary hypoplasia promotes fetal lung growth and maturation via transfer of miRNAs that are known to regulate lung developmental processes. We also describe that stem cell-derived EVs exert effects on vascular remodeling, thus possibly preventing postnatal pulmonary hypertension. Finally, we discuss future perspectives and challenges to translate this promising stem cell EV-based therapy to clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".