Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Respiratory Assessment: Purpose and Indications01:19

Respiratory Assessment: Purpose and Indications

Respiratory assessment is a cornerstone of nursing assessments, crucial for the early detection of patient deterioration. This evaluation transcends routine procedures, representing a critical skill nurses must master to ensure optimal patient care.
Objectives and Importance:
The primary goal of respiratory assessment is to evaluate patients at early risk of clinical deterioration. Since respiratory distress often precedes other signs of declining health, breathing patterns and sounds become a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dynamic Remodeling of the Human Milk Serum Proteome Across Lactation: A Paired Two-Stage DIA Proteomic Study in Term and Preterm Mothers.

Nutrients·2026
Same author

Multisite Real-World Validation of an Electronic Health Record-Integrated Generative Artificial Intelligence Tool for Venous Thromboembolism Risk Stratification.

medRxiv : the preprint server for health sciences·2026
Same author

Common data elements for maternal health research: Expert consensus emerging from a modified Delphi study.

Pregnancy (Hoboken, N.J.)·2026
Same author

Efficacy of Upadacitinib in Patients with Atopic Dermatitis of the Head and Neck Region.

Dermatology and therapy·2026
Same author

Urine Metabolomics to Predict Bronchopulmonary Dysplasia: A Single-Center Prospective Study.

Pediatric pulmonology·2026
Same author

Impact of Race, Ethnicity, and Insurance Type on In-hospital Mortality in Children with Traumatic Injury.

Journal of racial and ethnic health disparities·2026

Related Experiment Video

Updated: May 9, 2026

Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung
11:26

Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung

Published on: July 18, 2016

8.8K

Predicting Future Respiratory Hospitalizations in Extremely Premature Neonates Using Transcriptomic Data and Machine

Bryan G McOmber1, Lois Randolph1, Patrick Lang1

  • 1Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA.

Children (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

Gene expression profiles in extremely preterm neonates can predict future respiratory hospitalizations. This finding may help identify high-risk infants for early intervention, improving long-term respiratory health outcomes.

Keywords:
bioinformaticsbronchopulmonary dysplasiamachine learningpreterm infantsrespiratory morbiditytranscriptomics

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405

Related Experiment Videos

Last Updated: May 9, 2026

Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung
11:26

Real-time X-ray Imaging of Lung Fluid Volumes in Neonatal Mouse Lung

Published on: July 18, 2016

8.8K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405

Area of Science:

  • Neonatal Medicine
  • Genomics
  • Computational Biology

Background:

  • Extremely premature neonates face high risks of respiratory complications and hospitalizations.
  • Early identification of high-risk infants is crucial for targeted preventive strategies.
  • Transcriptomic data may enhance the prediction of respiratory outcomes beyond clinical factors.

Purpose of the Study:

  • To investigate the predictive capability of early-life gene expression for respiratory hospitalizations in extremely preterm neonates within their first four years.
  • To determine if transcriptomic profiles can identify infants at higher risk for respiratory morbidity.

Main Methods:

  • Retrospective cohort study of 58 neonates born before 32 weeks' gestational age.
  • Analysis of peripheral blood transcriptomic data collected on days 5, 14, and 28 of life.
  • Development of random forest models to predict respiratory readmissions, with performance assessed by AUC, sensitivity, and specificity.

Main Results:

  • Machine learning models using transcriptomic data achieved strong predictive performance (AUC = 0.90).
  • Differential expression analysis identified 31 genes and 8 biological pathways associated with respiratory readmissions.
  • Despite a small sample size, results indicate significant predictive power.

Conclusions:

  • Early-life transcriptomic data and machine learning accurately predict respiratory rehospitalizations in extremely preterm infants.
  • Identified gene signatures provide insights into biological mechanisms underlying chronic respiratory morbidity.
  • Further validation in larger cohorts is necessary for clinical application.