Abstract

Metabolic analysis in biofluids interprets the end products in the bioprocess, emerging as an irreplaceable disease diagnosis and monitoring platform. Laser desorption/ionization mass spectrometry (LDI MS)-based metabolic analysis holds great potential for clinical applications in terms of high throughput, rapid signal readout, and minimal sample preparation. There are two essential elements to construct the LDI MS-based metabolic analysis: 1) well-designed nanomaterials as matrices; 2) machine learning algorithms for data analysis. This review highlights the development of various inorganic matrices to comprehend the advantages of LDI MS in metabolite detection and the recent diagnostic applications based on target metabolite detection and untargeted metabolic fingerprints in biological fluids.

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