Application of Proteomics in Maternal and Neonatal Health: Advancements and Future Directions

Area of Science:

• Proteomics and biomarker discovery in maternal and neonatal health.
• Genomic and proteomic investigations for pregnancy complications.

Background:

• Maternal and neonatal health encompasses conditions like preterm birth, preeclampsia, intrauterine growth restriction, polycystic ovarian syndrome, and gestational diabetes mellitus.
• Genomic research has advanced understanding, but protein biomarker discovery is crucial for diagnosis, progression, and prognosis.
• Current biomarkers are often outdated or lack specificity, hindering clinical application.

Purpose of the Study:

• To review the current landscape of proteomics research in maternal and neonatal disorders.
• To evaluate the gap between biomarker discovery and clinical validation.
• To explore the role of Artificial Intelligence (AI) in addressing disparities and analyzing 'omics' data.

Main Methods:

• Comprehensive literature review of proteomics studies in maternal and neonatal health.
• Analysis of challenges in biomarker validation and clinical translation.
• Examination of AI applications in 'omics' data analysis and disparity mitigation.

Main Results:

• Identified significant challenges in the transition of discovered biomarkers to clinical validation.
• Highlighted ethnic disparities in maternal and neonatal health research.
• Demonstrated the potential of AI in analyzing large 'omics' datasets and reducing disparities.

Conclusions:

• There is a critical need to bridge the gap between biomarker discovery and clinical validation in maternal and neonatal health.
• AI offers promising solutions for improving data analysis and addressing socioeconomic and ethnic disparities.
• This review provides valuable insights for researchers, clinicians, and policymakers to enhance maternal and neonatal health outcomes.