{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T17:16:40Z","timestamp":1794071800363,"version":"4.8.0"},"reference-count":36,"publisher":"BMJ","funder":[{"name":"The 2024 High-quality Development Research Project of Shenzhen Bao\u2019an Public Hospital","award":["BAGZL2024040"],"award-info":[{"award-number":["BAGZL2024040"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82372323"],"award-info":[{"award-number":["82372323"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82430074"],"award-info":[{"award-number":["82430074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Medical Research Special Fund Project","award":["A2304001"],"award-info":[{"award-number":["A2304001"]}]},{"name":"Guangdong Provincial Science and Technology Program","award":["2019B030301009"],"award-info":[{"award-number":["2019B030301009"]}]},{"name":"Shenzhen Science and Technology Innovation Foundation","award":["2023B005"],"award-info":[{"award-number":["2023B005"]}]},{"name":"Shenzhen Science and Technology Innovation Foundation","award":["CJGJZD20220517141404010\/2023B005"],"award-info":[{"award-number":["CJGJZD20220517141404010\/2023B005"]}]},{"name":"Shenzhen Science and Technology Innovation Foundation","award":["JCY20220530142801003"],"award-info":[{"award-number":["JCY20220530142801003"]}]},{"name":"Shenzhen Science and Technology Innovation Foundation","award":["JCYJ20220530160207015"],"award-info":[{"award-number":["JCYJ20220530160207015"]}]},{"name":"Shenzhen Science and Technology Innovation Foundation","award":["JSGGZD20220822095200001"],"award-info":[{"award-number":["JSGGZD20220822095200001"]}]},{"name":"Prevention and Control of Emerging and Major Infectious Diseases-National Science and Technology Major Project","award":["2025ZD01907201"],"award-info":[{"award-number":["2025ZD01907201"]}]},{"name":"Provincial Natural Science Foundation of Guangdong","award":["2022A1515012438"],"award-info":[{"award-number":["2022A1515012438"]}]},{"name":"Provincial Natural Science Foundation of Guangdong","award":["2022A1515220034"],"award-info":[{"award-number":["2022A1515220034"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Thorax"],"accepted":{"date-parts":[[2026,5,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The prompt and precise diagnosis of active pulmonary tuberculosis (TB) is crucial for controlling this disease and yet it remains a global challenge. The objective of this study was to identify a set of microRNAs (miRNAs) whose expression in plasma could be used as a triage test for diagnosing TB.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 879 plasma samples were collected in seven clinical centres from healthy individuals and patients displaying TB-like symptoms and\/or radiological features consistent with TB. The samples were classified as TB, pneumonia, lung cancer and HC subgroups based on subsequent diagnostic assessments.<\/jats:p>\n                    <jats:p>We performed quantitative profiling of 264 plasma miRNAs in a training cohort (n=410) and an independent external test cohort (n=469). After dimensionality reduction and feature selection analysis, we identified nine discriminative miRNAs and used them to train an ensemble model in a training cohort using the scikit-learn library, which was subsequently evaluated in the external test cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ensemble model showed notable accuracy in discriminating TB from non-TB patients, yielding areas under the curve (AUC) of 0.84 (95% CI 0.80 to 0.88) for the training cohort and 0.86 (95% CI 0.82 to 0.90) for the external test cohort. When tested against subgroups of laboratory confirmed and clinically diagnosed but unconfirmed TB, the AUC values were 0.89 (95% CI 0.85 to 0.93) and 0.83 (95% CI 0.79 to 0.88), respectively. In smear-negative confirmed TB patients, the AUC exceeded 0.83, with a sensitivity and specificity of 0.75.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our miRNA ensemble model, based on detecting a nine-miRNA expression biosignature in plasma, demonstrated promising ability to diagnose TB and distinguish it from other common lung diseases but further studies are needed to assess its clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration number<\/jats:title>\n                    <jats:p>ChiCTR2000039734.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/thorax-2024-222136","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:20:46Z","timestamp":1782408046000},"page":"thorax-2024-222136","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Plasma miRNA signature for the diagnosis of pulmonary tuberculosis in symptomatic patients"],"prefix":"10.1136","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4837-3941","authenticated-orcid":false,"given":"Yunlong","family":"Hu","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Infection Immunity and Inflammation, Department of Pathogen Biology, School of Medicine, Shenzhen University, Shenzhen, People's Republic of China"},{"name":"Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3187-4291","authenticated-orcid":false,"given":"Huihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Infectious Diseases Center, Guangzhou Eighth People\u2019s Hospital, Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Yu","sequence":"additional","affiliation":[{"name":"Infectious Diseases Center, Guangzhou Eighth People\u2019s 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