Mesenchymal maintenance of distal epithelial cell phenotype during late fetal lung development
Bibliographic record
Abstract
Classical tissue recombination experiments have reported that at early gestation both tracheal and distal lung epithelium have the plasticity to respond to mesenchymal signals. Herein we examined the role of epithelial-mesenchymal interactions in maintaining epithelial differentiation at late (E19-E21, term = 22 days) fetal gestation in the rat. Isolated distal lung epithelial cells were recombined with mesenchymal cells from lung, skin, and intestine, and the homotypic or heterotypic recombinant cell aggregates were cultured for up to 5 days. Recombining lung epithelial cells with mesenchyme from various sources induced a morphological pattern that was specific to the type of inducing mesenchyme. In situ analysis of surfactant protein (SP)-C, SP-B, and Clara cell secretory protein (CCSP) expression, as well as SP-C and CCSP promoter transactivation experiments, revealed that distal lung epithelium requires lung mesenchyme to maintain the alveolar, but not bronchiolar, phenotype. Incubation of lung recombinants with an anti-FGF7 antibody resulted in a partial inhibition of mesenchyme-induced SP-C promoter transactivation. Immunoreactivity for Delta and Lunatic fringe, components of the Notch pathway that regulates cell differentiation, was downregulated in the heterotypic recombinants. In contrast, Hes1 mRNA expression was increased in these recombinants. Cumulatively, these results suggest that at late fetal gestation, distal lung epithelial cells are not fully committed to a specific phenotype and still have the plasticity to respond to various signals. Their alveolar phenotype is likely maintained by Notch/Notch ligand interactions and mesenchymal factors, including FGF7.
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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".