Role of vagal innervation on pulmonary surfactant system during fetal development
Bibliographic record
Abstract
Vagally mediated afferent feedback and compliant lungs (surfactant system) play vital roles in the establishment of adequate alveolar ventilation and pulmonary gas exchange at birth. Although the significance of vagal innervation in the establishment of normal breathing patterns is well recognized, the precise role of lung innervation in the maturation of the surfactant system remains unclear. The specific aim of the present study was to investigate whether vagal denervation compromises the surfactant system during fetal development. Experiments were performed on 12 time-dated fetal sheep: 8 underwent cervical vagal denervation, and 4 were sham operated. Vagal denervation was performed at 110-113 days gestation. Fetal lambs were instrumented in utero to record arterial pH and blood-gas tensions. The animals were delivered by cesarean section under general anesthesia between 130 and 133 days gestation (term approximately 147 days). Lung samples were collected for wet-to-dry ratios, light and electron microscopy, and overall lung morphology. In addition, total proteins, total phospholipids, and surfactant proteins A and B were analyzed in both lung tissue and bronchoalveolar lavage fluid. Vagal denervation had no effect on alveolar architecture, including type II cells or the morphology of lamellar bodies within them. Furthermore, surfactant proteins A and B and total phospholipids were similar in lung tissue and bronchoalveolar lavage fluid between the two groups. A significant correlation was observed between circulating cortisol concentrations and surfactant proteins in the bronchoalveolar lavage fluid and lung tissue. We provide definitive evidence that vagal innervation at midgestation is not required for maturation of the pulmonary surfactant system during fetal development.
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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".