Pulmonary feedback and gestational age-dependent regulation of fetal breathing movements
Bibliographic record
Abstract
Prenatal lung development requires fetal breathing movements (FBM). To investigate the dependence of FBM on feedback originating from the lung, we hypothesized that pneumonectomy stimulates FBM. Time-dated fetal sheep underwent bilateral pneumonectomy, unilateral pneumonectomy, or sham surgery at 125-130 days gestation. The incidence of FBM decreased in sham-operated fetuses at 142 days versus 130 days (p = 0.013), but was unchanged across all gestational ages in bilaterally pneumonectomized fetuses (p > or = 0.52). In unilaterally pneumonectomized fetuses, the incidence of FBM remained unchanged until 139 days and was higher than that of the bilaterally pneumonectomized fetuses at 130-136 days gestation (p < or = 0.03). The amplitude of integrated diaphragmatic electromyographic activity (integralEMG(di)) and total respiratory output (frequency of breathing x integralEMG(di)) were lower in pneumonectomized fetuses versus sham-operated fetuses at later gestational ages (p < 0.05). These decreases in integralEMG(di) and total respiratory output were most pronounced at 142 days in bilaterally pneumonectomized fetuses versus sham-operated fetuses (p = 0.006 and 0.016, respectively). Low-voltage electrocortical activity (ECoG) increased, and high-voltage ECoG decreased, in unilaterally pneumonectomized fetuses compared with sham-operated fetuses (p = 0.04). In conclusion, we provide new evidence that feedback from the fetal lung modulates the incidence and various components of phrenic nerve output, suggesting a positive feedback mechanism between FBM and lung development.
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 | 0.000 | 0.001 |
| 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".