In one paragraphArticle in Lupus science & medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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0citing papers in PubMed
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What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
25 authors.
Zhuochao Zhou *Department of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0003-1109-5781 Yuhong Liu *School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID 0009-0001-6075-4694 Yue SunDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0003-2682-3542 Honglei LiuDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0003-3858-0373 Xiaobing ChengDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0002-1243-5170 Yutong SuDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0002-0358-8933 Hui ShiDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0002-3574-8807 Qiongyi HuDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0002-4825-5227 Huihui ChiDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0001-9587-1180 Jianfen MengDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0001-8660-1170 Jinchao JiaDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0003-1507-8540 Tingting LiuDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0001-6043-3340 Mengyan WangDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0002-8052-0828 Cui LuDepartment of Rheumatology and Immunology, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0001-7008-296X Yunping CaiDepartment of Rheumatology and Immunology, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-2102-1498 Yijun YouDepartment of Infectious Diseases, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0000-0002-3667-1258 Dehao ZhuDepartment of Critical Care Medicine, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.ORCID 0009-0007-2229-1004 Shifang RenDepartment of Biochemistry and Molecular Biology, Fudan University School of Basic Medical Sciences, Shanghai, China.ORCID 0000-0001-8931-9293 Jialin TengDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China yjn0912@qq.com tengteng8151@sina.com 2806200215@qq.com yangchengde@sina.com.ORCID 0000-0003-1016-9064 Jingyi WuDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China yjn0912@qq.com tengteng8151@sina.com 2806200215@qq.com yangchengde@sina.com.ORCID 0009-0002-1074-7020 Chengde YangDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China yjn0912@qq.com tengteng8151@sina.com 2806200215@qq.com yangchengde@sina.com.ORCID 0000-0002-3720-634X Junna YeDepartment of Rheumatology and Immunology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China yjn0912@qq.com tengteng8151@sina.com 2806200215@qq.com yangchengde@sina.com.ORCID 0000-0002-2823-4349 Funding
No grant is acknowledged in the PubMed record.
6 · The paper itselfAbstract
objectiveThis study aimed to leverage machine learning algorithms to explore the relationship between anti-double-stranded DNA (anti-dsDNA) immunoglobulin G (IgG) glycosylation and the degree of organ involvement in patients with SLE. METHODS AND ANALYSIS: We enrolled 86 consecutive treatment-naïve patients with SLE positive for anti-dsDNA antibodies from the Department of Rheumatology and Immunology at Ruijin Hospital, Shanghai, between 2017 and 2019. We quantified and classified the degree of organ involvement in patients with SLE and analysed each glycoform and a combination of glycoforms of purified anti-dsDNA IgG. A random forest classifier and artificial neural network were applied to evaluate the correlation between the levels of glycoform pairs and the degree of organ involvement.
resultsPearson's correlation analysis revealed a strong connection between the involved and uninvolved organs in patients with SLE. Random forest analysis showed that the combination of IgG1Gal and IgG3/4Bis had the highest accuracy (0.7692) and area under the curve (0.8187). In terms of predicting the degree of involvement using an artificial neural network, IgG3/4Bis and IgG1Gal showed the lowest mean squared error (0.0244).
conclusionsOur study showed the effectiveness of combining glycoforms to classify and predict the degree of SLE organ involvement. Different glycoforms were correlated with the involvement degree to various extents, and the combination of anti-dsDNA IgG3/4Bis and IgG1Gal exhibited the best correlation with organ involvement.
Indexed as
Antibodies, AntinuclearImmunoglobulin GLupus Erythematosus, SystemicAdultChinaDNAFemaleGlycoproteinsGlycosylationHumansMachine LearningMaleMiddle AgedNeural Networks, ComputerYoung AdultAntibodies, Antinuclearanti-dsDNA autoantibodyDNAGlycoproteinsglycosylated IgGImmunoglobulin GAntibodiesLupus Erythematosus, SystemicSeverity of Illness Index
Identifiers
PMID40890016
PMCPMC12414148
What OpenQuestion holds
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LicenceCC BY-NC
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