Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants
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 Inter...
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| Format: | Article |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1521668/full |
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| author | Hanieh Talebi Seyed Alireza Dastgheib Maryam Vafapour Reza Bahrami Mohammad Golshan-Tafti Mahsa Danaei Sepideh Azizi Amirhossein Shahbazi Melina Pourkazemi Maryam Yeganegi Amirmasoud Shiri Ali Masoudi Heewa Rashnavadi Hossein Neamatzadeh |
| author_facet | Hanieh Talebi Seyed Alireza Dastgheib Maryam Vafapour Reza Bahrami Mohammad Golshan-Tafti Mahsa Danaei Sepideh Azizi Amirhossein Shahbazi Melina Pourkazemi Maryam Yeganegi Amirmasoud Shiri Ali Masoudi Heewa Rashnavadi Hossein Neamatzadeh |
| author_sort | Hanieh Talebi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-467ba37efed34ed08af0601e738b789a |
| institution | OA Journals |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-467ba37efed34ed08af0601e738b789a2025-08-20T02:24:38ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-04-011310.3389/fped.2025.15216681521668Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infantsHanieh Talebi0Seyed Alireza Dastgheib1Maryam Vafapour2Reza Bahrami3Mohammad Golshan-Tafti4Mahsa Danaei5Sepideh Azizi6Amirhossein Shahbazi7Melina Pourkazemi8Maryam Yeganegi9Amirmasoud Shiri10Ali Masoudi11Heewa Rashnavadi12Hossein Neamatzadeh13Clinical Research Development Unit, Fatemieh Hospital, Hamadan University of Medical Sciences, Hamadan, IranDepartment of Medical Genetics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, IranFiroozabadi Clinical Research Development Unit, Department of Pediatrics, Iran University of Medical Sciences, Tehran, IranNeonatal Research Center, Shiraz University of Medical Sciences, Shiraz, IranDepartment of Pediatrics, Islamic Azad University of Yazd, Yazd, IranDepartment of Obstetrics and Gynecology, School of Medicine, Iran University of Medical Sciences, Tehran, IranShahid Akbarabadi Clinical Research Development Unit, Iran University of Medical Sciences, Tehran, IranSchool of Medicine, Ilam University of Medical Sciences, Ilam, IranSchool of Medicine, Iran University of Medical Sciences, Tehran, Iran0Department of Obstetrics and Gynecology, School of Medicine, Iranshahr University of Medical Sciences, Iranshahr, Iran1School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran2School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran3School of Medicine, Tehran University of Medical Sciences, Tehran, Iran4Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, IranRecent 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.https://www.frontiersin.org/articles/10.3389/fped.2025.1521668/fullbiomarkersbronchopulmonary dysplasianeonatal respiratory distress syndromemachine learningpredictive modelspreterm infants |
| spellingShingle | Hanieh Talebi Seyed Alireza Dastgheib Maryam Vafapour Reza Bahrami Mohammad Golshan-Tafti Mahsa Danaei Sepideh Azizi Amirhossein Shahbazi Melina Pourkazemi Maryam Yeganegi Amirmasoud Shiri Ali Masoudi Heewa Rashnavadi Hossein Neamatzadeh Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants Frontiers in Pediatrics biomarkers bronchopulmonary dysplasia neonatal respiratory distress syndrome machine learning predictive models preterm infants |
| title | Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants |
| title_full | Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants |
| title_fullStr | Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants |
| title_full_unstemmed | Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants |
| title_short | Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants |
| title_sort | advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants |
| topic | biomarkers bronchopulmonary dysplasia neonatal respiratory distress syndrome machine learning predictive models preterm infants |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1521668/full |
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