Pulmonary Neuroendocrine Cell System in Pediatric Lung Disease—Recent Advances
Bibliographic record
Abstract
The airway epithelium of human and animal lungs contains highly specialized pulmonary neuroendocrine cells (PNEC), distributed as solitary cells and as innervated clusters, neuroepithelial bodies (NEB). The designation "PNEC system" stems from the expression of both neural and endocrine cell phenotypes, including the synthesis and release of amine (serotonin, 5-HT) and a variety of neuropeptides (that is, bombesin). The role and function of PNEC in the lung have remained a subject of speculation for many years. During the last decade, studies using modern techniques of cellular and molecular biology revealed a complex functional role for PNEC, beginning during the early stages of lung development as modulators of fetal lung growth and differentiation and at the time of birth as airway O2 sensors involved in neonatal adaptation. Postnatally and beyond, PNEC/NEB are providers of a lung stem cell niche that is important in airway epithelial regeneration and lung carcinogenesis. The focus of this review is to present and discuss recent findings pertaining to the responses of PNEC to intrauterine environmental stimuli, ontogeny and molecular regulation of PNEC differentiation, innervation of NEB, and their role as airway chemoreceptors, including mechanisms of O2 sensing and chemotransmission of hypoxia stimulus. Abnormalities of PNEC/NEB have been reported in a variety of pediatric pulmonary disorders but the clinical significance or the mechanisms involved are unknown. The discussion on the possible role of PNEC/NEB in the pathogenesis and pathobiology of pediatric lung diseases includes congenital lung disorders, bronchopulmonary dysplasia, disorders of respiratory control, neuroendocrine hyperplasia of infancy, cystic fibrosis, bronchial asthma, and pulmonary hypertension.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".