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Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.
Hu, Xin; Wang, Jie; Ju, Yingjiao; Zhang, Xiuli; Qimanguli, Wushou'er; Li, Cuidan; Yue, Liya; Tuohetaerbaike, Bahetibieke; Li, Ying; Wen, Hao; Zhang, Wenbao; Chen, Changbin; Yang, Yefeng; Wang, Jing; Chen, Fei.
Afiliação
  • Hu X; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Respiratory Medicine, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, Xinjiang, China.
  • Wang J; Department of Respiratory Medicine, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, China.
  • Ju Y; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
  • Zhang X; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Qimanguli W; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
  • Li C; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Yue L; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
  • Tuohetaerbaike B; Department of Respiratory Medicine, Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063, Xinjiang, China.
  • Li Y; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
  • Wen H; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
  • Zhang W; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, 830011, Xinjiang, China.
  • Chen C; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, 830011, Xinjiang, China.
  • Yang Y; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, 830011, Xinjiang, China.
  • Wang J; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, 830011, Xinjiang, China.
  • Chen F; Key Laboratory of Molecular Virology and Immunology, Unit of Pathogenic Fungal Infection and Host Immunity, Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, 20003, China.
BMC Infect Dis ; 22(1): 707, 2022 Aug 25.
Article em En | MEDLINE | ID: mdl-36008772
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Tuberculose Pulmonar / COVID-19 / Mycobacterium tuberculosis Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose / Tuberculose Pulmonar / COVID-19 / Mycobacterium tuberculosis Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article