Advancements in Biomarkers and Machine Learning for Predicting of Bronchopulmonary Dysplasia and Neonatal Respiratory Distress Syndrome in Preterm Infants

Seyedehelham Shams¹Reza Bahrami^2*Seyed Alireza Dastgheib²Maryam Yeganegi³Sepideh Azizi⁴Mahsa Danaie⁴Mohammad Golshan-Tafti⁵Ali Masoudi⁶Amirhossein Shahbazi⁷Amirmasoud Shiri²Kazem Aghili⁶Mahmood Noorishadkam⁶Hossein Neamatzadeh⁶

• ¹Hamadan University of Medical Sciences, Hamedan, Hamadan, Iran
• ²Shiraz University of Medical Sciences, Shiraz, Iran
• ³Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
• ⁴Iran University of Medical Sciences, Tehran, Tehran, Iran
• ⁵Islamic Azad University, Yazd, Yazd, Yazd, Iran
• ⁶Shahid Sadoughi University of Medical Sciences and Health Services, Yazd, Yazd, Iran
• ⁷Medical University of Ilam, Ilam, Ilam, Iran

Recent advancements in biomarker identification and machine learning have significantly enhanced the prediction and diagnosis of Bronchopulmonary Dysplasia (BPD) and neonatal respiratory distress syndrome (nRDS) in preterm infants. Key predictors of BPD severity include elevated cytokines like Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), as well as inflammatory markers such as the Neutrophil-to-Lymphocyte Ratio (NLR) and soluble gp130. Research into endoplasmic reticulum stress-related genes, differentially expressed genes, and ferroptosis-related genes provides valuable insights into BPD's pathophysiology. Machine learning models like XGBoost and Random Forest have identified important biomarkers, including CYYR1, GALNT14, and OLAH, improving diagnostic accuracy. Additionally, a five-gene transcriptomic signature shows promise for early identification of at-risk neonates, underscoring the significance of immune response factors in BPD. For nRDS, biomarkers such as the lecithin/sphingomyelin (L/S) ratio and oxidative stress indicators have been effectively used in innovative diagnostic methods, including attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and high-content screening for ABCA3 modulation. Machine learning algorithms like Partial Least Squares Regression (PLSR) and C5.0 have shown potential in accurately identifying critical health indicators. Furthermore, advanced feature extraction methods for analyzing neonatal cry signals offer a non-invasive means to differentiate between conditions like sepsis and nRDS. Overall, these findings emphasize the importance of combining biomarker analysis with advanced computational techniques to improve clinical decision-making and intervention strategies for managing BPD and nRDS in vulnerable preterm infants.