Maternal exposure to endotoxin delays alveolarization during postnatal rat lung development
Bibliographic record
Abstract
Maternal bacterial infections adversely affect lung development by crossing the placental barrier and infecting the developing fetus. The underlying mechanism negatively affecting pulmonary development remains unknown. Herein, we investigated whether a systemic maternal infection affects postnatal inflammation and alveolar development. Pregnant rats were injected with 2.5 mg/kg LPS on day 20 and 21 (term = 22 days). Postnatal (PN0-21) mRNA and protein expression of cytokines (IL-1beta, IL-6, IL-10, CXCL1/2, TNFalpha) and genes implicated in alveologenesis [tropoelastin, lysyl oxidase (LOX), lysyl oxidase-like (LOXL)1, tenascin-C (TNC), fibulin 5, vascular endothelial growth factor (VEGF-A), VEGF receptor (VEGFR)2, VEGFR1, platelet-derived growth factor (PDGF)A, PDGFB, and PDGFRalpha] were quantified by real-time PCR and beadlyte technology. Lung transcript and protein levels of IL-1beta, IL-6, and CXCL1/2 were significantly greater in LPS-exposed pups than those of control pups at PN0, 2, 6, 10, and 14. Bronchoalveolar lavage fluid (BALF) of LPS-exposed animals contained significantly more macrophages at PN2 and 14 than BALF of control pups. Morphometric analysis revealed that LPS-exposed animals had fewer and larger alveoli, fewer secondary septa, and decreased peripheral vessel density when compared with control pups. This morphological delay in alveolar development disappeared after PN14. Tropoelastin, LOXL1, VEGF, VEGFR2, and PDGFRalpha mRNA expression of LPS-exposed animals was significantly greater than those of control animals in PN2-14 lungs. TNC, LOX, fibulin 5, VEGFR1, PDGFA, and PDGFB expression was not affected by maternal LPS exposure. Together, the data demonstrate that maternal exposure to endotoxin results in a prolonged pulmonary inflammation postnatally, altered gene expression of molecules implicated in alveologenesis, and delayed morphological maturation of the lung.
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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 | 0.001 |
| 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".