PPARγ activation in late gestation does not promote surfactant maturation in the fetal sheep lung
Bibliographic record
Abstract
Respiratory distress syndrome results from inadequate functional pulmonary surfactant and is a significant cause of mortality in preterm infants. Surfactant is essential for regulating alveolar interfacial surface tension, and its synthesis by Type II alveolar epithelial cells is stimulated by leptin produced by pulmonary lipofibroblasts upon activation by peroxisome proliferator-activated receptor γ (PPARγ). As it is unknown whether PPARγ stimulation or direct leptin administration can stimulate surfactant synthesis before birth, we examined the effect of continuous fetal administration of either the PPARγ agonist, rosiglitazone (RGZ; Study 1) or leptin (Study 2) on surfactant protein maturation in the late gestation fetal sheep lung. We measured mRNA expression of genes involved in surfactant maturation and showed that RGZ treatment reduced mRNA expression of LPCAT1 (surfactant phospholipid synthesis) and LAMP3 (marker for lamellar bodies), but did not alter mRNA expression of PPARγ, surfactant proteins (SFTP-A, -B, -C, and -D), PCYT1A (surfactant phospholipid synthesis), ABCA3 (phospholipid transportation), or the PPARγ target genes SPHK-1 and PAI-1. Leptin infusion significantly increased the expression of PPARγ and IGF2 and decreased the expression of SFTP-B. However, mRNA expression of the majority of genes involved in surfactant synthesis was not affected. These results suggest a potential decreased capacity for surfactant phospholipid and protein production in the fetal lung after RGZ and leptin administration, respectively. Therefore, targeting PPARγ may not be a feasible mechanistic approach to promote lung maturation.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".