Sildenafil Improves Alveolar Growth and Pulmonary Hypertension in Hyperoxia-induced Lung Injury
Bibliographic record
Abstract
RATIONALE: Bronchopulmonary dysplasia (BPD), the chronic lung disease of preterm infants, and pulmonary emphysema, both significant global health problems, are characterized by an arrest in alveolar growth/loss of alveoli structures. Mechanisms that inhibit distal lung growth are poorly understood, but recent studies suggest that impaired vascular endothelial growth factor signaling and reduced nitric oxide (NO) production decreases alveolar and vessel growth in the developing lung, features observed in experimental oxygen-induced BPD. NO exerts its biological activity by stimulating guanosine 3',5'-cyclic monophosphate (cGMP) production. OBJECTIVES: Because cGMP is inactivated by phosphodiesterase (PDE) enzymes, we hypothesized that the cGMP-specific PDE5 inhibitor sildenafil would promote angiogenesis and attenuate oxygen-induced lung injury in newborn rats. METHODS, MEASUREMENTS, AND MAIN RESULTS: In vitro, sildenafil (10(-4) M) increased endothelial capillary network formation of human pulmonary endothelial cells exposed to hyperoxia. In vivo, rat pups were randomly exposed from birth to normoxia, hyperoxia (95% O(2), BPD model), and hyperoxia+sildenafil (100 mg/kg/day subcutaneously). Rat pups exposed to hyperoxia showed fewer and enlarged air spaces as well as decreased capillary density, mimicking pathologic features seen in human BPD. These structural anomalies were associated with echographic (decreased pulmonary acceleration time) and structural (right ventricular hypertrophy and increased medial wall thickness) signs of pulmonary hypertension. Sildenafil preserved alveolar growth and lung angiogenesis, and decreased pulmonary vascular resistance, right ventricular hypertrophy and medial wall thickness. CONCLUSIONS: Our findings suggest a role for the NO/cGMP pathway during alveolar development. Sildenafil may have therapeutic potential in diseases associated with impaired alveolar structures.
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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".