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Multi-Omics 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):

ORGANISM(S): Mycoplasma Pneumoniae (strain Atcc 29342 / M129)

TISSUE(S): Blood

SUBMITTER: xiulan lai

LAB HEAD: Xiulan Lai

PROVIDER: PXD071661 | Pride | 2025-12-12

REPOSITORIES: Pride

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			Action	DRS
	Results.zip	Other
	XB00885B5DA_CCH_BALF.raw	Raw
	XB00885B5DA_CJC_BALF.raw	Raw
	XB00885B5DA_CLE_BALF.raw	Raw
	XB00885B5DA_DSH_BALF.raw	Raw

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