CFTR Regulates Early Pathogenesis of Chronic Obstructive Lung Disease in βENaC-Overexpressing Mice
Bibliographic record
Abstract
BACKGROUND: Factors determining the onset and severity of chronic obstructive pulmonary disease remain poorly understood. Previous studies demonstrated that airway surface dehydration in βENaC-overexpressing (βENaC-Tg) mice on a mixed genetic background caused either neonatal mortality or chronic obstructive lung disease suggesting that the onset of lung disease was modulated by the genetic background. METHODS: To test this hypothesis, we backcrossed βENaC-Tg mice onto two inbred strains (C57BL/6 and BALB/c) and studied effects of the genetic background on neonatal mortality, airway ion transport and airway morphology. Further, we crossed βENaC-Tg mice with CFTR-deficient mice to validate the role of CFTR in early lung disease. RESULTS: We demonstrate that the C57BL/6 background conferred increased CFTR-mediated Cl(-) secretion, which was associated with decreased mucus plugging and mortality in neonatal βENaC-Tg C57BL/6 compared to βENaC-Tg BALB/c mice. Conversely, genetic deletion of CFTR increased early mucus obstruction and mortality in βENaC-Tg mice. CONCLUSIONS: We conclude that a decrease or absence of CFTR function in airway epithelia aggravates the severity of early airway mucus obstruction and related mortality in βENaC-Tg mice. These results suggest that genetic or environmental factors that reduce CFTR activity may contribute to the onset and severity of chronic obstructive pulmonary disease and that CFTR may serve as a novel therapeutic target.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".