Recombinant adiponectin protects the newborn rat lung from lipopolysaccharide‐induced inflammatory injury
Bibliographic record
Abstract
Preterm infants are at high risk for developing bronchopulmonary dysplasia and pulmonary hypertension from inflammatory lung injury. In adult models, adiponectin (APN)-an adipocyte-derived hormone-protects the lung from inflammatory injury and pulmonary vascular remodeling. Cord blood APN levels in premature infants born < 26 weeks gestation are 5% of the level in infants born at term. We previously reported the expression profile of APN and its receptors in neonatal rat lung homogenates during the first 3 weeks of postnatal development. Here, we characterize the expression profile of APN and its receptors in specific lung cells and the effects of exogenous recombinant APN (rAPN) on lipopolysaccharide-(LPS)-induced cytokine and chemokine production in total lung homogenates and specific lung cells. In vitro, rAPN added to primary cultures of pulmonary artery smooth muscle cells attenuated the expression of LPS-induced pro-inflammatory cytokines while increasing the expression of anti-inflammatory cytokines. In vivo, intraperitoneal rAPN (2 mg/kg), given 4 hr prior to intrapharyngeal administration of LPS (5 mg/kg) to newborn rats at postnatal day 4, significantly reduced gene and protein expression of the pro-inflammatory cytokine IL-1ß and reduced protein expression of the chemokines monocyte chemoattractant protein (MCP-1) and macrophage inflammatory protein-1 alpha (MIP-1α) in the lung. LPS-induced histopathological changes in the lung were also decreased. Moreover, rAPN given 20 hr after intrapharyngeal LPS had a similar effect on lung inflammation. These findings suggest a role for APN in protecting the lung from inflammation during early stages of lung 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".