A tracheal aspirate-derived airway basal cell model reveals a proinflammatory epithelial defect in congenital diaphragmatic hernia
Bibliographic record
Abstract
ABSTRACT Rationale Congenital diaphragmatic hernia (CDH) is characterized by incomplete closure of the diaphragm and lung hypoplasia. The pathophysiology of lung defects in CDH is poorly understood. Objectives To establish a translational model of human airway epithelium in CDH for pathogenic investigation and therapeutic testing. Methods We developed a robust methodology of epithelial progenitor derivation from tracheal aspirates of newborns. Basal stem cells (BSCs) from CDH patients and preterm and term, non-CDH controls were derived and analyzed by bulk RNA-sequencing, ATAC-sequencing, and air-liquidinterface differentiation. Lung sections from fetal human CDH samples and the nitrofen rat model of CDH were subjected to histological assessment of epithelial defects. Therapeutics to restore epithelial differentiation were evaluated in human epithelial cell culture and the nitrofen rat model of CDH. Measurements and Main Results Transcriptomic and epigenetic profiling of CDH and non-CDH basal stem cells reveals a disease-specific, proinflammatory signature independent of severity or hernia size. In addition, CDH basal stem cells exhibit defective epithelial differentiation in vitro that recapitulates epithelial phenotypes found in fetal human CDH lung samples and fetal tracheas of the nitrofen rat model of CDH. Furthermore, steroid treatment normalizes epithelial differentiation phenotypes of human CDH basal stem cells in vitro and in nitrofen rat tracheas in vivo . Conclusions Our findings have identified an underlying proinflammatory signature and BSC differentiation defects as a potential therapeutic target for airway epithelial defects in CDH.
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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 | 0.001 |
| 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".