A confocal microscopic study of solitary pulmonary neuroendocrine cells in human airway epithelium
Bibliographic record
Abstract
BACKGROUND: Pulmonary neuroendocrine cells (PNEC) are specialized epithelial cells that are thought to play important roles in lung development and airway function. PNEC occur either singly or in clusters called neuroepithelial bodies. Our aim was to characterize the three dimensional morphology of PNEC, their distribution, and their relationship to the epithelial nerves in whole mounts of adult human bronchi using confocal microscopy. METHODS: Bronchi were resected from non-diseased portions of a lobe of human lung obtained from 8 thoracotomy patients (Table 1) undergoing surgery for the removal of lung tumors. Whole mounts were stained with antibodies to reveal all nerves (PGP 9.5), sensory nerves (calcitonin gene related peptide, CGRP), and PNEC (PGP 9.5, CGRP and gastrin releasing peptide, GRP). The analysis and rendition of the resulting three-dimensional data sets, including side-projections, was performed using NIH-Image software. Images were colorized and super-imposed using Adobe Photoshop. RESULTS: PNEC were abundant but not homogenously distributed within the epithelium, with densities ranging from 65/mm2 to denser patches of 250/mm2, depending on the individual wholemount. Rotation of 3-D images revealed a complex morphology; flask-like with the cell body near the basement membrane and a thick stem extending to the lumen. Long processes issued laterally from its base, some lumenal and others with feet-like processes. Calcitonin gene-related peptide (CGRP) was present in about 20% of PNEC, mainly in the processes. CGRP-positive nerves were sparse, with some associated with the apical part of the PNEC. CONCLUSION: Our 3D-data demonstrates that PNEC are numerous and exhibit a heterogeneous peptide content suggesting an active and diverse PNEC population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".