Predictive analytics in bronchopulmonary dysplasia: past, present, and future.

Bronchopulmonary dysplasia (BPD) remains a significant complication of prematurity, impacting approximately 18,000 infants annually in the United States. Advances in neonatal care have not reduced BPD, and its management is challenged by the rising survival of extremely premature infants and the variability in clinical practices. Leveraging statistical and machine learning techniques, predictive analytics can enhance BPD management by utilizing large clinical datasets to predict individual patient outcomes.

Blocking IL-17a Signaling Decreases Lung Inflammation and Improves Alveolarization in Experimental Bronchopulmonary Dysplasia.

Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease of preterm infants that is associated with life-long morbidities. Inflammatory insults contribute to BPD pathogenesis. Although the proinflammatory cytokine, IL-17a, plays a role in various neonatal inflammatory disorders, its role in BPD pathogenesis is unclear.

Leveraging Integrated RNA Sequencing to Decipher Adrenomedullin's Protective Mechanisms in Experimental Bronchopulmonary Dysplasia.

Bronchopulmonary dysplasia (BPD) is a chronic lung disease commonly affecting premature infants, with limited therapeutic options and increased long-term consequences. Adrenomedullin (), a proangiogenic peptide hormone, has been found to protect rodents against experimental BPD. This study aims to elucidate the molecular and cellular mechanisms through which influences BPD pathogenesis using a lipopolysaccharide (LPS)-induced model of experimental BPD in mice.