Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis.

ABSTRACT:

Background

Neonatal sepsis with meningoencephalitis is a common complication of sepsis, which is a leading cause of neonatal death and neurological dysfunction. Early identification of neonatal sepsis with meningoencephalitis is particularly important for reducing brain damage. We recruited 70 patients with neonatal sepsis, 42 of which were diagnosed as meningoencephalitis, and collected cerebrospinal fluid (CSF) and serum samples. The purpose of this study was to find neonatal sepsis with meningoencephalitis-related markers using unbiased metabolomics technology and artificial intelligence analysis based on machine learning methods.

Results

We found that the characteristics of neonatal sepsis with meningoencephalitis were manifested mainly as significant decreases in the co

SUBMITTER: Zhang P 

PROVIDER: S-EPMC8207169 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature