{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:35:43Z","timestamp":1787020543710,"version":"build-2736575974"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Transl Med"],"DOI":"10.1186\/s12967-025-07265-w","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T13:35:04Z","timestamp":1762781704000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development and multi-center validation of machine learning models based on targeted metabolomics for rheumatoid arthritis"],"prefix":"10.1186","volume":"23","author":[{"given":"Jifeng","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renquan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huali","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfang","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhui","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenge","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiming","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0704-1417","authenticated-orcid":false,"given":"Jinpiao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"issue":"4","key":"7265_CR1","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1093\/rheumatology\/ker370","volume":"51","author":"R Li","year":"2012","unstructured":"Li R, et al. Epidemiology of eight common rheumatic diseases in China: a large-scale cross-sectional survey in Beijing. Rheumatol (oxford). 2012;51(4):721\u201329","journal-title":"[J] Rheumatol (oxford)"},{"issue":"6","key":"7265_CR2","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1056\/NEJMra2103726","volume":"388","author":"EM Gravallese","year":"2023","unstructured":"Gravallese EM, Firestein GS. Rheumatoid arthritis - common origins, divergent mechanisms. N Engl J Med. 2023;388(6):529\u201342","journal-title":"[J] N Engl J Med"},{"issue":"13","key":"7265_CR3","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1001\/jama.2018.13103","volume":"320","author":"D Aletaha","year":"2018","unstructured":"Aletaha D, Smolen JS. Diagnosis and management of rheumatoid arthritis: a review. JAMA. 2018;320(13):1360\u201372","journal-title":"[J] JAMA"},{"issue":"8","key":"7265_CR4","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1038\/s41581-023-00720-1","volume":"19","author":"DS Pisetsky","year":"2023","unstructured":"Pisetsky DS. Pathogenesis of autoimmune disease. Nat Rev Nephrol. 2023;19(8):509\u201324","journal-title":"[J] Nat Rev Nephrol"},{"issue":"10055","key":"7265_CR5","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1016\/S0140-6736(16)30173-8","volume":"388","author":"JS Smolen","year":"2016","unstructured":"Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet. 2016;388(10055):2023\u201338","journal-title":"[J] Lancet"},{"key":"7265_CR6","unstructured":"Aletaha D, et al. Rheumatoid arthritis classification criteria: an American college of Rheumatology\/European league against rheumatism collaborative initiative. Arthritis Rheum. 2010, 2010;62(9):2569\u201381"},{"issue":"3","key":"7265_CR7","first-page":"554","volume":"41","author":"L De Stefano","year":"2023","unstructured":"De Stefano L, et al. Seronegative rheumatoid arthritis: one year in review 2023. Clin Exp Rheumatol. 2023;41(3):554\u201364","journal-title":"[J] Clin Exp Rheumatol"},{"key":"7265_CR8","doi-asserted-by":"crossref","unstructured":"Yang Z, et al. Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: a multidimensional perspective. Pharmacol Ther. 2024,. 254:108591","DOI":"10.1016\/j.pharmthera.2024.108591"},{"key":"7265_CR9","doi-asserted-by":"publisher","first-page":"104001","DOI":"10.1016\/j.ebiom.2022.104001","volume":"79","author":"Y Xiao","year":"2022","unstructured":"Xiao Y, et al. Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis. EBioMedicine. 2022;79:104001","journal-title":"[J] EBioMedicine"},{"issue":"1","key":"7265_CR10","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.annonc.2023.10.125","volume":"35","author":"A Prelaj","year":"2024","unstructured":"Prelaj A, et al. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol. 2024;35(1):29\u201365","journal-title":"[J] Ann Oncol"},{"issue":"6","key":"7265_CR11","doi-asserted-by":"publisher","first-page":"2349","DOI":"10.1002\/mas.21785","volume":"42","author":"CJ Chen","year":"2022","unstructured":"Chen CJ, et al. Recent advances in LC-MS-based metabolomics for clinical biomarker discovery. Mass Spectrom Rev. 2022;42(6):2349\u201378","journal-title":"[J] Mass Spectrom Rev"},{"issue":"4","key":"7265_CR12","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1002\/med.21883","volume":"42","author":"PR Pereira","year":"2022","unstructured":"Pereira PR, et al. Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease. Med Res Rev. 2022;42(4):1518\u201344","journal-title":"[J] Med Res Rev"},{"key":"7265_CR13","doi-asserted-by":"publisher","first-page":"1381272","DOI":"10.3389\/fimmu.2024.1381272","volume":"15","author":"X Gong","year":"2024","unstructured":"Gong X, et al. An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery. Front Immunol. 2024;15:1381272","journal-title":"[J] Front Immunol"},{"issue":"1","key":"7265_CR14","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.cmet.2016.09.018","volume":"25","author":"CB Newgard","year":"2017","unstructured":"Newgard CB. Metabolomics and metabolic diseases: where do we stand? Cell Metab. 2017;25(1):43\u201356","journal-title":"[J] Cell Metab"},{"issue":"6","key":"7265_CR15","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/s41580-019-0108-4","volume":"20","author":"MM Rinschen","year":"2019","unstructured":"Rinschen MM, et al. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019;20(6):353\u201367","journal-title":"[J] Nat Rev Mol Cell Biol"},{"issue":"4","key":"7265_CR16","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1152\/physrev.00035.2018","volume":"99","author":"DS Wishart","year":"2019","unstructured":"Wishart DS. Metabolomics for investigating physiological and pathophysiological processes. Physiol Rev. 2019;99(4):1819\u201375","journal-title":"[J] Physiol Rev"},{"issue":"5","key":"7265_CR17","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s11306-024-02155-6","volume":"20","author":"RD Beger","year":"2024","unstructured":"Beger RD, et al. Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial. Metabolomics. 2024;20(5):95","journal-title":"[J] Metabolomics"},{"issue":"9","key":"7265_CR18","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1002\/art.41733","volume":"73","author":"X Shen","year":"2021","unstructured":"Shen X, et al. Serum metabolomics identifies dysregulated pathways and potential metabolic biomarkers for hyperuricemia and gout. Arthritis Rheumatol. 2021;73(9):1738\u201348","journal-title":"[J] Arthritis Rheumatol"},{"issue":"1","key":"7265_CR19","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.1038\/s41467-023-37875-1","volume":"14","author":"Y Yao","year":"2023","unstructured":"Yao Y, et al. Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera. Nat Commun. 2023;14(1):2339","journal-title":"[J] Nat Commun"},{"issue":"12","key":"7265_CR20","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.1002\/art.42635","volume":"75","author":"M Wang","year":"2023","unstructured":"Wang M, et al. Metabolomics and machine learning identify metabolic differences and potential biomarkers for frequent versus infrequent gout flares. Arthritis Rheumatol. 2023;75(12):2252\u201364","journal-title":"[J] Arthritis Rheumatol"},{"issue":"6","key":"7265_CR21","doi-asserted-by":"publisher","first-page":"e2114186","DOI":"10.1001\/jamanetworkopen.2021.14186","volume":"4","author":"S Huang","year":"2021","unstructured":"Huang S, et al. Identification and validation of plasma Metabolomic signatures in precancerous gastric lesions that progress to cancer. JAMA Netw Open. 2021;4(6):e2114186","journal-title":"[J] JAMA Netw Open"},{"issue":"4","key":"7265_CR22","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1038\/s41596-022-00801-8","volume":"18","author":"S Sarmad","year":"2023","unstructured":"Sarmad S, et al. A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK consortium on metabolic phenotyping (MAP\/UK). Nat Protocol. 2023;18(4):1017\u201327","journal-title":"[J] Nat Protocol"},{"issue":"8","key":"7265_CR23","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1002\/art.1780290816","volume":"29","author":"R Altman","year":"1986","unstructured":"Altman R, et al. Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and Therapeutic criteria committee of the American rheumatism association. Arthritis Rheum. 1986;29(8):1039\u201349","journal-title":"[J] Arthritis Rheum"},{"issue":"1","key":"7265_CR24","doi-asserted-by":"publisher","first-page":"6656","DOI":"10.1038\/s41467-022-34537-6","volume":"13","author":"Z Zhou","year":"2022","unstructured":"Zhou Z, et al. Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nat Commun. 2022;13(1):6656","journal-title":"[J] Nat Commun"},{"issue":"3","key":"7265_CR25","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11306-007-0082-2","volume":"3","author":"LW Sumner","year":"2007","unstructured":"Sumner LW, et al. Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) Metabolomics standards initiative (MSI). Metabolomics. 2007;3(3):211\u201321","journal-title":"[J] Metabolomics"},{"issue":"1","key":"7265_CR26","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","volume":"23","author":"JG Greener","year":"2022","unstructured":"Greener JG, et al. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40\u201355","journal-title":"[J] Nat Rev Mol Cell Biol"},{"issue":"10","key":"7265_CR27","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1093\/rheumatology\/keu215","volume":"53","author":"EM Ball","year":"2014","unstructured":"Ball EM, et al. A study of erosive phenotypes in lupus arthritis using magnetic resonance imaging and anti-citrullinated protein antibody, anti-RA33 and RF autoantibody status. Rheumatol (oxford). 2014;53(10):1835\u201343","journal-title":"[J] Rheumatol (oxford)"},{"key":"7265_CR28","doi-asserted-by":"publisher","first-page":"151947","DOI":"10.1016\/j.semarthrit.2021.11.013","volume":"52","author":"A Riccardi","year":"2022","unstructured":"Riccardi A, et al. Erosive arthritis autoantibodies in systemic sclerosis. Semin Arthritis Rheum. 2022;52:151947","journal-title":"[J] Semin Arthritis Rheum"},{"key":"7265_CR29","doi-asserted-by":"crossref","unstructured":"Perera J, et al. Clinical phenotypes, serological biomarkers, and synovial features defining seropositive and seronegative rheumatoid arthritis: a literature review. Cells. 2024;13(9)","DOI":"10.3390\/cells13090743"},{"key":"7265_CR30","doi-asserted-by":"publisher","first-page":"961708","DOI":"10.3389\/fimmu.2022.961708","volume":"13","author":"L Xu","year":"2022","unstructured":"Xu L, et al. Metabolomics in rheumatoid arthritis: advances and review. Front Immunol. 2022;13:961708","journal-title":"[J] Front Immunol"},{"issue":"1","key":"7265_CR31","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1038\/s41584-022-00867-4","volume":"19","author":"T Kissel","year":"2023","unstructured":"Kissel T, et al. Glycobiology of rheumatic diseases. Nat Rev Rheumatol. 2023;19(1):28\u201343","journal-title":"[J] Nat Rev Rheumatol"},{"issue":"7","key":"7265_CR32","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/s41592-021-01197-1","volume":"18","author":"S Alseekh","year":"2021","unstructured":"Alseekh S, et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat Methods. 2021;18(7):747\u201356","journal-title":"[J] Nat Methods"},{"issue":"3","key":"7265_CR33","first-page":"393","volume":"37","author":"AK Carlson","year":"2019","unstructured":"Carlson AK, et al. Global metabolomic profiling of human synovial fluid for rheumatoid arthritis biomarkers. Clin Exp Rheumatol. 2019;37(3):393\u201399","journal-title":"[J] Clin Exp Rheumatol"},{"issue":"3","key":"7265_CR34","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1007\/s10753-021-01604-x","volume":"45","author":"P Nieminen","year":"2022","unstructured":"Nieminen P, et al. Metabolomics of synovial fluid and infrapatellar Fat Pad in patients with osteoarthritis or rheumatoid arthritis. Inflammation. 2022;45(3):1101\u201317","journal-title":"[J] Inflammation"},{"issue":"8","key":"7265_CR35","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1093\/rheumatology\/kez545","volume":"59","author":"G Miltenberger-Miltenyi","year":"2020","unstructured":"Miltenberger-Miltenyi G, et al. Increased monohexosylceramide levels in the serum of established rheumatoid arthritis patients. Rheumatol (oxford). 2020;59(8):2085\u201389","journal-title":"[J] Rheumatol (oxford)"},{"issue":"4","key":"7265_CR36","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1136\/annrheumdis-2019-216374","volume":"79","author":"M Souto-Carneiro","year":"2020","unstructured":"Souto-Carneiro M, et al. Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis. Ann Rheum Dis. 2020;79(4):499\u2013506","journal-title":"[J] Ann Rheum Dis"},{"issue":"6","key":"7265_CR37","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1007\/s10067-018-4021-6","volume":"37","author":"J Li","year":"2018","unstructured":"Li J, et al. LC-MS-based serum metabolomics reveals a distinctive signature in patients with rheumatoid arthritis. Clin Rheumatol. 2018;37(6):1493\u2013502","journal-title":"[J] Clin Rheumatol"},{"issue":"10","key":"7265_CR38","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1111\/1756-185X.13935","volume":"23","author":"YJ Lee","year":"2020","unstructured":"Lee YJ, et al. A discovery of screening markers for rheumatoid arthritis by liquid chromatography mass spectrometry: a metabolomic approach. Int J Rheum Dis. 2020;23(10):1353\u201362","journal-title":"[J] Int J Rheum Dis"},{"key":"7265_CR39","doi-asserted-by":"crossref","unstructured":"Roca M, et al. Reviewing the metabolome coverage provided by LC-MS: focus on sample preparation and chromatography-A tutorial. Anal Chim Acta. 2021;1147:38\u201355","DOI":"10.1016\/j.aca.2020.12.025"},{"key":"7265_CR40","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Dom\u00ednguez R, et al. Recommendations and best practices for standardizing the pre-analytical processing of blood and urine samples in Metabolomics. Metabolites. 2020;10(6)","DOI":"10.3390\/metabo10060229"},{"issue":"1","key":"7265_CR41","doi-asserted-by":"publisher","first-page":"21005","DOI":"10.1038\/s41598-024-68477-6","volume":"14","author":"J Sigaux","year":"2024","unstructured":"Sigaux J, et al. Red blood cell metabolomics identify ergothioneine as a key metabolite in DMARD-na\u00efve rheumatoid arthritis and response to methotrexate. Sci Rep. 2024;14(1):21005","journal-title":"[J] Sci Rep"},{"key":"7265_CR42","doi-asserted-by":"crossref","unstructured":"Sakata S, Kunimatsu R, Tanimoto K. Protective effect of Ergothioneine against oxidative stress-induced chondrocyte death. Antioxidants (Basel). 2024;13(7)","DOI":"10.3390\/antiox13070800"},{"key":"7265_CR43","doi-asserted-by":"crossref","unstructured":"Malik S, et al. Unveiling the nexus: cellular metabolomics unravels the impact of estrogen on nicotinamide metabolism in mitigating rheumatoid arthritis pathogenesis. Metabolites. 2024;14(4)","DOI":"10.3390\/metabo14040214"},{"issue":"2","key":"7265_CR44","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1136\/ard.2003.019844","volume":"64","author":"R Imrich","year":"2005","unstructured":"Imrich R, et al. Low levels of dehydroepiandrosterone sulphate in plasma, and reduced sympathoadrenal response to hypoglycaemia in premenopausal women with rheumatoid arthritis. Ann Rheum Dis. 2005;64(2):202\u201306","journal-title":"[J] Ann Rheum Dis"},{"issue":"1","key":"7265_CR45","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3109\/s10165-005-0453-3","volume":"16","author":"M Yukioka","year":"2006","unstructured":"Yukioka M, et al. Adrenocorticotropic hormone and dehydroepiandrosterone sulfate levels of rheumatoid arthritis patients treated with glucocorticoids. Mod Rheumatol. 2006;16(1):30\u201335","journal-title":"[J] Mod Rheumatol"},{"issue":"3","key":"7265_CR46","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1136\/ard.52.3.211","volume":"52","author":"GM Hall","year":"1993","unstructured":"Hall GM, Perry LA, Spector TD. Depressed levels of dehydroepiandrosterone sulphate in postmenopausal women with rheumatoid arthritis but no relation with axial bone density. Ann Rheum Dis. 1993;52(3):211\u201314","journal-title":"[J] Ann Rheum Dis"},{"key":"7265_CR47","doi-asserted-by":"crossref","unstructured":"Comas-Bast\u00e9 O, et al. Histamine intolerance: the current state of the art. Biomolecules. 2020;10(8)","DOI":"10.3390\/biom10081181"},{"issue":"1","key":"7265_CR48","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3904\/kjim.2019.271","volume":"35","author":"HK Min","year":"2020","unstructured":"Min HK, et al. Roles of mast cells in rheumatoid arthritis. Korean J Intern Med. 2020;35(1):12\u201324","journal-title":"[J] Korean J Intern Med"},{"key":"7265_CR49","first-page":"266","volume":"6","author":"Y Kouno","year":"2016","unstructured":"Kouno Y, et al. N-acetyl-L-methionine is a superior protectant of human serum albumin against post-translational oxidation as compared to N-acetyl-L-tryptophan. Biochem Biophys Rep. 2016;6:266\u201374","journal-title":"[J] Biochem Biophys Rep"},{"key":"7265_CR50","doi-asserted-by":"crossref","unstructured":"Nishiyama R, Inoue A, Ojima T. Identification of 2-keto-3-deoxy-d-gluconate kinase and 2-keto-3-deoxy-d-phosphogluconate aldolase in an alginate-assimilating bacterium, Flavobacterium sp. Strain UMI-01. Mar Drugs, 2017;15(2)","DOI":"10.3390\/md15020037"},{"issue":"19","key":"7265_CR51","doi-asserted-by":"publisher","first-page":"5441","DOI":"10.1073\/pnas.1521916113","volume":"113","author":"X Chen","year":"2016","unstructured":"Chen X, et al. The Entner-Doudoroff pathway is an overlooked glycolytic route in cyanobacteria and plants. Proc Natl Acad Sci U S A. 2016;113(19):5441\u201346","journal-title":"[J] Proc Natl Acad Sci U S A"},{"issue":"5","key":"7265_CR52","doi-asserted-by":"publisher","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","volume":"20","author":"KY Ngiam","year":"2019","unstructured":"Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262\u201373","journal-title":"[J] Lancet Oncol"}],"container-title":["Journal of Translational Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12967-025-07265-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12967-025-07265-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12967-025-07265-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:02:59Z","timestamp":1762783379000},"score":1,"resource":{"primary":{"URL":"https:\/\/translational-medicine.biomedcentral.com\/articles\/10.1186\/s12967-025-07265-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["7265"],"URL":"https:\/\/doi.org\/10.1186\/s12967-025-07265-w","relation":{},"ISSN":["1479-5876"],"issn-type":[{"value":"1479-5876","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"16 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Fujian Medical University, Fuzhou, China (Approval No. [2022]082), and by the Medical Ethics Committee of Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Approval No. AF\/SC-11\/04.0). All other participating institutions were included under the ethics approval of Tongren Hospital through formal institutional collaboration agreements. All procedures involving human participants complied with the Declaration of Helsinki, and informed consent was obtained from all subjects.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing financial or non-financial interests related to this work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1257"}}