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Porous PtCu Alloys Decode Plasma Metabolic Fingerprints for the Recognition of Severe Community-Acquired Pneumonia

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机构： [1]State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China. [2]Department of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Shanghai, 201399, China. [3]Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. [4]Shanghai Engineering Research Center of Internet of Things for Respiratory Medicine, Shanghai, 200032, China. [5]Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, 200032, China. [6]Center of Emergency and Critical Medicine in Jinshan Hospital of Fudan University, Fudan University, Shanghai, 200540, China. [7]Department of Critical Care, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. [8]School of Engineering, University of Warwick, Coventry, West Midlands, CV4 7AL, UK.

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关键词： alloy severe community-acquired pneumonia mass spectrometry metabolite diagnosis

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Rapid and accurate recognition of severe community-acquired pneumonia (CAP) would facilitate the optimal intervention. Currently, the diagnosis of severe CAP is commonly based on the criteria established by Infectious Disease Society of America (IDSA)/American Thoracic Society (ATS), which include 2 primary and 9 secondary criteria, making the process cumbersome and time-consuming. Here, a porous PtCu alloy-assisted laser desorption/ionization mass spectrometry (LDI MS) is designed for the extraction of plasma metabolic fingerprints (PMFs), coupling with machine learning for the diagnosis of severe CAP. The PtCu alloys with optimal particle size exhibit excellent sensitivity, reproducibility, and universality for metabolite detection, due to the porous structure, promising photoelectric effect, and improved melting surface structure. Further, the nanoplatform successfully records the PMFs within seconds, using only 0.5 µL native plasma. Machine learning of PMFs on 69 individuals produces a diagnostic model with an area under curve (AUC) of 0.832. Particularly, a three metabolic biomarker panel demonstrates enhanced diagnostic efficiency (AUC of 0.846), outperforming reported biomarkers (AUC of 0.560-0.770). Notably, the diagnosis can be completed in ≈35 min. The work affords a rapid and precise method for CAP management through metabolite analysis.© 2025 Wiley‐VCH GmbH.

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出版当年[2025]版：

大类 | 2 区

医学

小类 | 2 区工程：生物医学 2 区材料科学：生物材料 2 区纳米科技

最新[2025]版：

大类 | 2 区

医学

小类 | 2 区工程：生物医学 2 区材料科学：生物材料 2 区纳米科技

第一作者：

第一作者机构： [1]State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China.

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通讯作者：

通讯机构： [3]Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. [4]Shanghai Engineering Research Center of Internet of Things for Respiratory Medicine, Shanghai, 200032, China. [5]Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, 200032, China.

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Porous PtCu Alloys Decode Plasma Metabolic Fingerprints for the Recognition of Severe Community-Acquired Pneumonia

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