{"doi":"10.1002/advs.202406233","title":"Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints","abstract":"<jats:title>Abstract</jats:title><jats:p>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<jats:sup>−1</jats:sup> features is identified as biomarkers for TB diagnosis and a panel of 4 m z<jats:sup>−1</jats:sup> 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.</jats:p>","journal":"Advanced Science","year":2024,"id":608822,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":344223,"name":"Ruimin Wang","orcid":"0000-0002-1842-6291","position":1,"is_corresponding":false},{"id":1254585,"name":"Chao Zhang","orcid":"0009-0007-8631-2476","position":2,"is_corresponding":false},{"id":1142986,"name":"Lin Huang","orcid":"0000-0003-2062-5772","position":3,"is_corresponding":false},{"id":1564060,"name":"Jifan Chen","orcid":null,"position":4,"is_corresponding":false},{"id":1564061,"name":"Yiqing Zeng","orcid":null,"position":5,"is_corresponding":false},{"id":1564062,"name":"Hongjian Chen","orcid":null,"position":6,"is_corresponding":false},{"id":324175,"name":"Guowei Wang","orcid":"0000-0001-7110-3865","position":7,"is_corresponding":false},{"id":385141,"name":"Kun Qian","orcid":"0000-0002-3196-9258","position":8,"is_corresponding":false},{"id":1564063,"name":"Pintong Huang","orcid":"0000-0003-0747-5765","position":9,"is_corresponding":false},{"id":1180911,"name":"Yajing Liu","orcid":"0000-0002-9819-4303","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints","abstract":"<jats:title>Abstract</jats:title><jats:p>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<jats:sup>−1</jats:sup> features is identified as biomarkers for TB diagnosis and a panel of 4 m z<jats:sup>−1</jats:sup> 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.</jats:p>","is_dataset_classified":null,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39159075","pmcid":"PMC11497029","openalex_id":"https://openalex.org/W4401694832","authors":[],"funders":[{"funder_name":"National Natural Science Foundation of China","grant_id":"82001818","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82230069","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82030048","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82102191","title":null},{"funder_name":"Science and Technology Department of Sichuan Province","grant_id":"2024YFHZ0176","title":null},{"funder_name":"Key Research and Development Program of Zhejiang Province","grant_id":"2019C03077","title":null},{"funder_name":"Science and Technology Commission of Shanghai Municipality","grant_id":"20DZ2220400","title":null},{"funder_name":"Science and Technology Commission of Shanghai Municipality","grant_id":"2021SHZDZX","title":null},{"funder_name":"Shanghai Municipal Education Commission","grant_id":"ZXWF082101","title":null},{"funder_name":"Shanghai Municipal Health Commission","grant_id":"2019CXJQ03","title":null},{"funder_name":"Innovative Research Team of High-level Local University in Shanghai","grant_id":"SHSMU‐ZDCX20210700","title":null},{"funder_name":"Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning","grant_id":"2021‐01‐07‐00‐02‐E00083","title":null},{"funder_name":"Zhejiang Science and Technology Project","grant_id":"LQ21H180007","title":null},{"funder_name":"Innovative Research Team of High-level Local University in Shanghai","grant_id":"SHSMU-ZDCX20210700","title":null},{"funder_name":"Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning","grant_id":"2021-01-07-00-02-E00083","title":null}],"total_grants":15,"fwci":1.7991,"citation_percentile":0.85212804,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":10}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202406233","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202406233","host_type":"publisher"},{"url":"https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202406233","host_type":"publisher"},{"url":"https://doi.org/10.1002/advs.202406233","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39159075","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11497029","host_type":"repository"},{"url":"https://doaj.org/article/ac95559ca4274e97bc8163d55676c0d7","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11497029/pdf/ADVS-11-2406233.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11497029","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11497029?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Metabolomics and Mass Spectrometry Studies","Tuberculosis Research and Epidemiology","Cell Image Analysis Techniques"],"mesh_terms":["Machine Learning","COVID-19","Adult","Female","Humans","Male","Middle Aged","Phenotype","Tuberculosis","Biomarkers","Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization","Metabolomics"],"keywords":["Tuberculosis","Computational biology","Biology","Medicine","Pathology","Drug Resistant Tuberculosis","Serum Metabolic Fingerprints","Diagnosis And Phenotyping","Nanoparticle Enhanced Laser Desorption/ionization Mass Spectrometry"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"nct"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T22:43:05.495603Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}