Metabolic biomarkers of neonatal sepsis: identification using metabolomics combined with machine learning
Serum metabolite profiles were identified, and machine learning was applied to identify the key differentially abundant metabolites in septic infants with comorbidities, septic infants without comorbidities, and infants without infectious diseases. The findings obtained are expected to facilitate the early diagnosis of sepsis in infants and determine the severity of the disease.
Authors
- X Chen (ORCID: https://orcid.org/0009-0007-6788-0726)
- Zhanghua Yin
- Jihong Qian (ORCID: https://orcid.org/0000-0002-8974-115X)
- Zhongxiao Zhang (ORCID: https://orcid.org/0000-0002-5302-9515)
- Min Xu (ORCID: https://orcid.org/0000-0001-9581-8849)
- Yanru Chen (ORCID: https://orcid.org/0000-0002-9324-4076)
- Zhi Bian
- X. Q. Zha
Institutions
- XinHua Hospital (CN)
- Tongren Hospital (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2024-10-21
- DOI
- https://doi.org/10.3389/fcell.2024.1491065
- Citations
- 5
- Primary Topic
- Neonatal and Maternal Infections
- Type
- article
- Field-Weighted Citation Impact
- 2.71