Pulmonary Neuroendocrine Cells and Neuroepithelial Bodies in Sudden Infant Death Syndrome: Potential Markers of Airway Chemoreceptor Dysfunction
Bibliographic record
Abstract
Pulmonary neuroendocrine cells (PNEC), including neuroepithelial bodies (NEB), are amine- and peptide (for example, bombesin)-producing cells that function as hypoxia/hypercapnia-sensitive chemoreceptors that could be involved in the pathophysiology of sudden infant death syndrome (SIDS). We assessed morphometrically the frequency and size of PNEC/NEB in lungs of infants who died of SIDS (n = 21) and compared them to an equal number PNEC/NEB in lungs of age-matched control infants who died of accidental death or homicide, with all cases obtained from the San Diego SIDS/SUDC Research Project database. As a marker for PNEC/NEB we used an antibody against chromogranin A (CGA), and computer-assisted morphometric analysis was employed to determine the relative frequency of PNEC per airway epithelial area (% immunostained area, %IMS), the size of NEB, the number of nuclei/NEB, and the size of the NEB cells. The lungs of SIDS infants showed significantly greater %IMS of airway epithelium (2.72 +/- 0.28 [standard error of the mean, SEM] versus 1.88 +/- 0.24; P < 0.05) and larger NEB (1557 +/- 153 microm(2) versus 1151 +/- 106 microm(2); P < 0.05) compared to control infants. The size of NEB cells was also significantly increased in SIDS cases compared to the controls (180 +/- 6.39 microm(2) versus 157 +/- 8.0 microm(2); P < 0.05), indicating the presence of hypertrophy in addition to hyperplasia. Our findings support previous studies demonstrating hyperplasia of PNEC/NEB in lungs of infants who died of SIDS. These changes could be secondary to chronic hypoxia and/or could be attributable to maturational delay. Morphometric assessment and/or measurement of the secretory products of these cells (for example, CGA, bombesin) could provide a potential biological marker for SIDS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".