| Literature DB >> 31829520 |
Jing Yang1, Ran Wang2, Lin Huang3, Mengji Zhang1, Jingyang Niu1, Chunde Bao2, Nan Shen2, Min Dai2, Qiang Guo2, Qian Wang1, Qin Wang2, Qiong Fu2, Kun Qian1.
Abstract
Metabolic fingerprints of biofluids encode diverse diseases and particularly urine detection offers complete non-invasiveness for diagnostics of the future. Present urine detection affords unsatisfactory performance and requires advanced materials to extract molecular information, due to the limited biomarkers and high sample complexity. Herein, we report plasmonic polymer@Ag for laser desorption/ionization mass spectrometry (LDI-MS) and sparse-learning-based metabolic diagnosis of kidney diseases. Using only 1 μL of urine without enrichment or purification, polymer@Ag afforded urine metabolic fingerprints (UMFs) by LDI-MS in seconds. Analysis by sparse learning discriminated lupus nephritis from various other non-lupus nephropathies and controls. We combined UMFs with urine protein levels (UPLs) and constructed a new diagnostic model to characterize subtypes of kidney diseases. Our work guides urine-based diagnosis and leads to new personalized analytical tools for other diseases.Entities:
Keywords: analytical chemistry; disease diagnosis; mass spectrometry; metabolism; nanoparticles
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Year: 2019 PMID： 31829520 DOI： 10.1002/anie.201913065
Source DB: PubMed Journal: Angew Chem Int Ed Engl ISSN： 1433-7851 Impact factor: 15.336