Pulmonary Neuroendocrine Cells, Airway Innervation, and Smooth Muscle Are Altered in Cftr Null Mice
Bibliographic record
Abstract
The amine- and peptide-producing pulmonary neuroendocrine cells (PNEC) are widely distributed within the airway mucosa of mammalian lung as solitary cells and innervated clusters, neuroepithelial bodies (NEB), which function as airway O2 sensors. These cells express Cftr and hence could play a role in the pathophysiology of cystic fibrosis (CF) lung disease. We performed confocal microscopy and morphometric analysis on lung sections from Cftr-/- (null), Cftr+/+, and Cftr+/- (control) mice at developmental stages E20, P5, P9, and P30 to determine the distribution, frequency, and innervation of PNEC/NEB, innervation and cell mass of airway smooth muscle, and neuromuscular junctions using synaptic vesicle protein 2, smooth muscle actin, and synaptophysin markers, respectively. The mean number of PNEC/NEB in Cftr-/- mice was significantly reduced compared with control mice at E20, whereas comparable or increased numbers were observed postnatally. NEB cells in Cftr null mice showed a significant reduction in intracorpuscular nerve endings compared with control mice, which is consistent with an intrinsic abnormality of the PNEC system. The airways of Cftr-/- mice showed reduced density (approximately 20-30%) of smooth muscle innervation, decreased mean airway smooth muscle mass (approximately 35%), and reduced density (approximately 20%) of nerve endings compared with control mice. We conclude that the airways of Cftr-/- mice exhibit heretofore unappreciated structural alterations affecting cellular and neural components of the PNEC system and airway smooth muscle and its innervation resulting in blunted O2 sensing and reduced airway tonus. Cftr could play a role in the development of the PNEC system, lung innervation, and airway smooth muscle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".