Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

Yang, Chenjie; Zhou, Da; Yu, Hailong; Chen, Yijie; Lin, Hairu; Wu, Hao; Deng, Chunhui

doi:10.1021/acs.analchem.4c02369

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Anal. Chem.

2024

DOI: 10.1021/acs.analchem.4c02369

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Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

Chenjie Yang,

Da Zhou,

Hailong Yu

et al.

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References 46 publications

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Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

Liu,

Wang,

Zhang

et al. 2024

Advanced Science

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Tuberculosis (TB) stands as the second most fatal infectious disease after COVID‐19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics‐based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle‐enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost‐effectiveness (approximately $3). A panel of 14 m z−1 features is identified as biomarkers for TB diagnosis and a panel of 4 m z−1 features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964‐0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806‐0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end‐TB strategy.

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Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

Liu,

Wang,

Zhang

et al. 2024

Advanced Science

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Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

Cited by 1 publication

References 46 publications

Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

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