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Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification.
Du, Qiuyao; Wang, Xiao; Chen, Junyu; Xiong, Caiqiao; Liu, Wenlan; Liu, Jianfeng; Liu, Huihui; Jiang, Lixia; Nie, Zongxiu.
Afiliación
  • Du Q; Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
  • Wang X; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Chen J; Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
  • Xiong C; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Liu W; Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
  • Liu J; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Liu H; Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
  • Jiang L; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Nie Z; The Center for Medical Genetics & Molecular Diagnosis, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Sciences Center, Shenzhen 518035, China.
Chem Commun (Camb) ; 59(65): 9852-9855, 2023 Aug 10.
Article en En | MEDLINE | ID: mdl-37490058
Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Metabolómica / Aprendizaje Automático Tipo de estudio: Prognostic_studies Idioma: En Revista: Chem Commun (Camb) Asunto de la revista: QUIMICA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Metabolómica / Aprendizaje Automático Tipo de estudio: Prognostic_studies Idioma: En Revista: Chem Commun (Camb) Asunto de la revista: QUIMICA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido