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Cited by in F6Publishing
For: Luan H, Gu W, Li H, Wang Z, Lu L, Ke M, Lu J, Chen W, Lan Z, Xiao Y, Xu J, Zhang Y, Cai Z, Liu S, Zhang W. Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients. J Transl Med 2021;19:500. [PMID: 34876179 DOI: 10.1186/s12967-021-03169-7] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Xu L, Niu X, Liu Y, Liu L. ST3GAL3 Promotes the Inflammatory Response of Fibroblast-Like Synoviocytes in Rheumatoid Arthritis by Activating the TLR9/MyD88 Pathway. Mediators of Inflammation 2022;2022:1-13. [DOI: 10.1155/2022/4258742] [Reference Citation Analysis]
2 Ruiz-romero C, Fernández-puente P, González L, Illiano A, Lourido L, Paz R, Quaranta P, Perez-pampín E, González A, Blanco FJ, Calamia V. Association of the serological status of rheumatoid arthritis patients with two circulating protein biomarkers: A useful tool for precision medicine strategies. Front Med 2022;9. [DOI: 10.3389/fmed.2022.963540] [Reference Citation Analysis]
3 Cunningham KY, Hur B, Gupta VK, Arment CA, Wright KA, Mason TG, Peterson LS, Bekele DI, Schaffer DE, Bailey ML, Delger KE, Crowson CS, Myasoedova E, Zeng H, Rodriguez M, Weyand CM, Davis JM, Sung J. Patients with ACPA-positive and ACPA-negative Rheumatoid Arthritis Show Different Serological Autoantibody Repertoires and Autoantibody Associations with Disease Activity.. [DOI: 10.1101/2022.10.09.22280063] [Reference Citation Analysis]
4 Barberis E, Khoso S, Sica A, Falasca M, Gennari A, Dondero F, Afantitis A, Manfredi M. Precision Medicine Approaches with Metabolomics and Artificial Intelligence. IJMS 2022;23:11269. [DOI: 10.3390/ijms231911269] [Reference Citation Analysis]
5 Lin W, Chen G, Mao Y, Ma X, Zhou J, Yu X, Wang C, Liu M. Imperatorin Inhibits Proliferation, Migration, and Inflammation via Blocking the NF-κB and MAPK Pathways in Rheumatoid Fibroblast-like Synoviocytes. ACS Omega. [DOI: 10.1021/acsomega.2c02766] [Reference Citation Analysis]
6 He L, Lu Y, Li C, Xie H, Zhao J, Wang Y, Wang L, Wang X, Wang W, Chen D, Gao Y, Li B, Li Y. Non-targeted metallomics through synchrotron radiation X-ray fluorescence with machine learning for cancer screening using blood samples. Talanta 2022;245:123486. [DOI: 10.1016/j.talanta.2022.123486] [Reference Citation Analysis]
7 Dorochow E, Köhm M, Hahnefeld L, Gurke R. Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity. JPM 2022;12:924. [DOI: 10.3390/jpm12060924] [Reference Citation Analysis]
8 Coras R, Murillo-saich JD, Singh AG, Kavanaugh A, Guma M. Lipidomic Profiling in Synovial Tissue. Front Med 2022;9:857135. [DOI: 10.3389/fmed.2022.857135] [Reference Citation Analysis]
9 Zeng T, Liang Y, Dai Q, Tian J, Chen J, Lei B, Yang Z, Cai Z. Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles. Chinese Chemical Letters 2022. [DOI: 10.1016/j.cclet.2022.03.020] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Luan H. Machine learning for screening active metabolites with metabolomics in environmental science. Environ Sci : Adv 2022. [DOI: 10.1039/d2va00107a] [Reference Citation Analysis]