Innervation of Pulmonary Neuroendocrine Cells and Neuroepithelial Bodies in Developing Rabbit Lung
Bibliographic record
Abstract
We investigated the development of innervation of the pulmonary neuroendocrine cell (PNEC) system composed of single cells and organoid cell clusters, neuroepithelial bodies (NEB) in rabbit fetal and neonatal lungs. To visualize the nerve fibers and their contacts with PNECs/NEBs, we used confocal microscopy and multilabel immunohistochemistry (IHC) with pan-neural marker, synaptic vesicle protein 2 (SV2), and serotonin (5-HT) as markers for PNECs/NEBs, and smooth muscle actin or cytokeratin to identify airway landmarks. The numbers and distribution of PNEC/NEB at different stages of lung development (E16, 18, 21, 26, and P2) and the density of innervation were quantified. First PNECs immunoreactive for 5-HT were identified in primitive airway epithelium at E18 as single cells or as small cell clusters with or without early nerve contacts. At E21 a significant increase in the number of PNECs with formation of early innervated NEB corpuscules was observed. The overall numbers of PNECs/NEBs and the density of mucosal, submucosal, and intercorpuscular innervation increased with progressing gestation and peaked postnatally (P2). At term, the majority of NEBs and single PNECs within airway mucosa possessed neural contacts. Such an extensive and complex innervation of the PNEC system indicates a multifunctional role in developing lung and during neonatal adaptation.
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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".