Multi-0mics and Machine Learning-Based Profiling of Severity signatures in Mycoplasma Pneumoniae Infection in children

ABSTRACT:

Mycoplasma pneumoniae pneumonia (MPP) is a common respiratory infection in children. Yet the mechanisms driving its progression to severe disease remain poorly understood. This study employs a comprehensive proteomic and metabolomic approach to elucidate severity-related pathways and identify potential biomarkers for improved diagnosis and targeted therapy. By analyzing blood proteomes from 57 pediatric patients with varying MPP severities alongside 10 healthy controls, and integrating multi-omics data from bronchoalveolar lavage fluid (BALF).

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse-phase, Liquid Chromatography MS - positive - reverse-phase

PROVIDER: MTBLS13511 | MetaboLights | 2026-01-04

REPOSITORIES: MetaboLights