Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients
Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic...
Saved in:
| Published in | Journal of translational medicine Vol. 19; no. 1; pp. 500 - 10 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
07.12.2021
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1479-5876 1479-5876 |
| DOI | 10.1186/s12967-021-03169-7 |
Cover
| Abstract | Background
Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum.
Methods
We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals.
Results
Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity.
Conclusions
A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. |
|---|---|
| AbstractList | Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum.BACKGROUNDDiagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum.We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals.METHODSWe performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals.Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity.RESULTSTwenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity.A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.CONCLUSIONSA panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. Abstract Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. Keywords: Rheumatoid arthritis, Seronegative, Metabolomic, Lipidomic Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. |
| ArticleNumber | 500 |
| Audience | Academic |
| Author | Li, Hua Gu, Wanjian Xu, Jinyue Cai, Zongwei Liu, Shijia Zhang, Yi Lan, Zhangzhang Luan, Hemi Wang, Zi Lu, Lu Ke, Mengying Zhang, Wenyong Lu, Jiawei Xiao, Yanlin Chen, Wenjun |
| Author_xml | – sequence: 1 givenname: Hemi surname: Luan fullname: Luan, Hemi organization: School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology – sequence: 2 givenname: Wanjian surname: Gu fullname: Gu, Wanjian organization: Affiliated Hospital of Nanjing University of Chinese Medicine – sequence: 3 givenname: Hua surname: Li fullname: Li, Hua organization: Sustech Core Research Facilities, Southern University of Science and Technology – sequence: 4 givenname: Zi surname: Wang fullname: Wang, Zi organization: School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology – sequence: 5 givenname: Lu surname: Lu fullname: Lu, Lu organization: Affiliated Hospital of Nanjing University of Chinese Medicine – sequence: 6 givenname: Mengying surname: Ke fullname: Ke, Mengying organization: College of Pharmacy, Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine – sequence: 7 givenname: Jiawei surname: Lu fullname: Lu, Jiawei organization: State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University – sequence: 8 givenname: Wenjun surname: Chen fullname: Chen, Wenjun organization: Affiliated Hospital of Nanjing University of Chinese Medicine – sequence: 9 givenname: Zhangzhang surname: Lan fullname: Lan, Zhangzhang organization: School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology – sequence: 10 givenname: Yanlin surname: Xiao fullname: Xiao, Yanlin organization: School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology – sequence: 11 givenname: Jinyue surname: Xu fullname: Xu, Jinyue organization: School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology – sequence: 12 givenname: Yi surname: Zhang fullname: Zhang, Yi organization: School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology – sequence: 13 givenname: Zongwei surname: Cai fullname: Cai, Zongwei email: zwcai@hkbu.edu.hk organization: State Key Laboratory of Environmental and Biological Analysis (SKLEBA), Department of Chemistry, Hong Kong Baptist University – sequence: 14 givenname: Shijia surname: Liu fullname: Liu, Shijia email: liushijia2011@163.com organization: Affiliated Hospital of Nanjing University of Chinese Medicine – sequence: 15 givenname: Wenyong orcidid: 0000-0002-8531-8274 surname: Zhang fullname: Zhang, Wenyong email: zhangwy@sustech.edu.cn organization: School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34876179$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUstu1TAQjVARfcAPsECR2LBJie3EdjZIqOJRqRILYG059jh1SexgJ0X9Bb6aufeWvoQqlEWSmXPOPM4cFnshBiiKl6Q-JkTyt5nQjouqpqSqGeFdJZ4UB6QRXdVKwffufO8Xhzlf1DVt2qZ7VuyzBoNEdAfF76-Q1qmcYNF9HOPkTamDLUc_e7v9m1N0fvRhKL2FsHjnIZfW6yHEvGC-93HS6QekXLqYygwpzjH7xV_CVmkTCDDobSCdwzrpJXpb6rScJ4TlcsYcKufnxVOnxwwvrt9HxfePH76dfK7Ovnw6PXl_Vhku2FJZ2lgniWaudZqbloq-460TltSMd6TvXS-pIEBAUJyfcKmdbYlupNQ9o8COitOdro36Qs3JY_9XKmqvtoGYBoXNeTOC0ob00nDLAUzDOi6NE8ApabmoJeEUtdhOaw2zvvqlx_FGkNRq45LauaTQJbV1SQlkvdux5rWfwBqcPunxXiv3M8GfqyFeKsnbuiENCry5Fkjx5wp5UZPPBsZRB4hrVpRje7Rt2k2t1w-gF3FNAReMKEIY6Zgkt6hB49g-uIh1zUZUveeSN6RrGEPU8T9Q-FjAU0Gb8VLgPuHV3UFvJvx7fwigO4BJMecE7v_WJx-QjF_wiOJmWX58nHrtV8Y6YYB0u41HWH8ApFMReQ |
| CitedBy_id | crossref_primary_10_1007_s11306_023_02004_y crossref_primary_10_1016_j_jep_2023_116782 crossref_primary_10_3389_fmed_2022_963540 crossref_primary_10_3389_fmed_2022_857135 crossref_primary_10_1039_D2VA00107A crossref_primary_10_1111_apm_13401 crossref_primary_10_1186_s13020_023_00750_8 crossref_primary_10_1186_s12967_024_05100_2 crossref_primary_10_3389_fimmu_2024_1409555 crossref_primary_10_3390_futurepharmacol2040038 crossref_primary_10_1016_j_jaut_2023_103001 crossref_primary_10_1002_rai2_12142 crossref_primary_10_3389_fimmu_2023_1161148 crossref_primary_10_1016_j_talanta_2024_126696 crossref_primary_10_1016_j_trac_2024_117852 crossref_primary_10_3390_jpm12060924 crossref_primary_10_1002_bmc_5736 crossref_primary_10_1111_1756_185X_70188 crossref_primary_10_1007_s00394_023_03257_y crossref_primary_10_1016_j_mtcomm_2024_110208 crossref_primary_10_1016_j_cclet_2022_03_020 crossref_primary_10_1016_j_jff_2024_106289 crossref_primary_10_1016_j_aca_2023_341028 crossref_primary_10_1080_13880209_2023_2241512 crossref_primary_10_1016_j_isci_2023_108387 crossref_primary_10_1186_s12884_025_07224_9 crossref_primary_10_1016_j_talanta_2022_123486 crossref_primary_10_3892_etm_2024_12717 crossref_primary_10_1016_j_heliyon_2024_e33085 crossref_primary_10_1007_s10753_024_01986_8 crossref_primary_10_1016_j_jbspin_2024_105841 crossref_primary_10_1093_rheumatology_kead619 crossref_primary_10_1021_acs_jproteome_3c00574 crossref_primary_10_1021_acsomega_2c02766 crossref_primary_10_1038_s41598_023_32428_4 crossref_primary_10_1002_art_42848 crossref_primary_10_3390_ijms231911269 crossref_primary_10_1016_j_arcmed_2023_102907 crossref_primary_10_3390_metabo15030205 crossref_primary_10_1007_s00216_022_04473_x crossref_primary_10_1136_rmdopen_2023_003560 crossref_primary_10_3389_fimmu_2023_1087925 crossref_primary_10_1155_2022_4258742 crossref_primary_10_1152_ajpcell_00630_2024 crossref_primary_10_3390_ijms25052483 crossref_primary_10_1021_acs_est_3c08033 crossref_primary_10_3389_fimmu_2024_1410365 |
| Cites_doi | 10.1016/j.aca.2018.08.002 10.1136/annrheumdis-2016-210997 10.1007/s11033-010-0545-9 10.4137/CIN.S20806 10.1017/nws.2015.20 10.1371/journal.pone.0097501 10.1017/S000711450769936X 10.1007/s10067-018-4021-6 10.1136/ard.2007.084459 10.1038/srep13888 10.1093/rheumatology/key302 10.1002/art.27584 10.1002/art.10241 10.1371/journal.pone.0219400 10.1017/S0007114514001056 10.1136/ard.2006.051672 10.1021/acs.jproteome.8b00439 10.1016/j.berh.2016.02.003 10.1038/cddis.2015.246 10.1016/j.jpba.2015.10.007 10.1016/j.cbi.2019.108903 10.1038/nrrheum.2014.121 10.1038/s41573-019-0032-5 10.1002/art.20720 10.1016/j.cbpa.2015.11.009 10.1136/annrheumdis-2019-216374 10.1186/s13075-019-1956-1 10.3389/fmed.2018.00339/full 10.1038/nrdp.2018.1 10.1016/j.jhazmat.2019.06.015 10.1021/pr401068k 10.1002/cpbi.86 10.1039/c4mb00131a 10.1186/1471-2105-12-77 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2021 2021. The Author(s). COPYRIGHT 2021 BioMed Central Ltd. 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2021 – notice: 2021. The Author(s). – notice: COPYRIGHT 2021 BioMed Central Ltd. – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7T5 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH H94 K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/s12967-021-03169-7 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Immunology Abstracts Health & Medical Collection (Proquest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Proquest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) AIDS and Cancer Research Abstracts ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Medical Database Proquest Central Premium ProQuest One Academic Publicly Available Content Database (Proquest) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall Openly Available Collection - DOAJ |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection AIDS and Cancer Research Abstracts ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Immunology Abstracts ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 6 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1479-5876 |
| EndPage | 10 |
| ExternalDocumentID | oai_doaj_org_article_ac1b8c6d6eec43968cf7e62156708162 10.1186/s12967-021-03169-7 PMC8650414 A686419433 34876179 10_1186_s12967_021_03169_7 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GeographicLocations | China United States--US |
| GeographicLocations_xml | – name: China – name: United States--US |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 21904058; 2190457; 81774096 funderid: http://dx.doi.org/10.13039/501100001809 – fundername: Priority Academic Program Development of Jiangsu Higher Education Institutions grantid: ZYX03KF031 funderid: http://dx.doi.org/10.13039/501100012246 – fundername: Young Elite Scientists Sponsorship Program by CAST grantid: QNRC2-B04 – fundername: National Natural Science Foundation of China grantid: 2190457 – fundername: Priority Academic Program Development of Jiangsu Higher Education Institutions grantid: ZYX03KF031 – fundername: National Natural Science Foundation of China grantid: 21904058 – fundername: National Natural Science Foundation of China grantid: 81774096 – fundername: ; grantid: QNRC2-B04 – fundername: ; grantid: ZYX03KF031 – fundername: ; grantid: 21904058; 2190457; 81774096 |
| GroupedDBID | --- 0R~ 29L 2WC 53G 5VS 6PF 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EBD EBLON EBS ESX F5P FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO IHR INH INR ITC KQ8 M1P M48 M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ TR2 TUS UKHRP WOQ WOW XSB ~8M AAYXX CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7T5 7XB 8FK AZQEC DWQXO H94 K9. PKEHL PQEST PQUKI PRINS 7X8 5PM 2VQ 4.4 ADRAZ ADTOC AHSBF EJD H13 IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c673t-d24df81a3f5fa6c527b965f7d103691bbfb8271e1e72479168afd51a488ab32e3 |
| IEDL.DBID | M48 |
| ISSN | 1479-5876 |
| IngestDate | Fri Oct 03 12:50:54 EDT 2025 Sun Oct 26 04:15:56 EDT 2025 Sat Oct 11 07:03:06 EDT 2025 Thu Oct 02 11:27:08 EDT 2025 Sun Oct 19 00:03:29 EDT 2025 Mon Oct 20 22:14:53 EDT 2025 Mon Oct 20 16:48:22 EDT 2025 Mon Jul 21 06:08:13 EDT 2025 Thu Apr 24 22:52:13 EDT 2025 Wed Oct 01 03:39:44 EDT 2025 Sat Sep 06 07:28:44 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Metabolomic Rheumatoid arthritis Seronegative Lipidomic |
| Language | English |
| License | 2021. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c673t-d24df81a3f5fa6c527b965f7d103691bbfb8271e1e72479168afd51a488ab32e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-8531-8274 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://translational-medicine.biomedcentral.com/track/pdf/10.1186/s12967-021-03169-7 |
| PMID | 34876179 |
| PQID | 2611319381 |
| PQPubID | 43076 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_ac1b8c6d6eec43968cf7e62156708162 unpaywall_primary_10_1186_s12967_021_03169_7 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8650414 proquest_miscellaneous_2608125457 proquest_journals_2611319381 gale_infotracmisc_A686419433 gale_infotracacademiconefile_A686419433 pubmed_primary_34876179 crossref_primary_10_1186_s12967_021_03169_7 crossref_citationtrail_10_1186_s12967_021_03169_7 springer_journals_10_1186_s12967_021_03169_7 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-07 |
| PublicationDateYYYYMMDD | 2021-12-07 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-07 day: 07 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Journal of translational medicine |
| PublicationTitleAbbrev | J Transl Med |
| PublicationTitleAlternate | J Transl Med |
| PublicationYear | 2021 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | G Wells (3169_CR26) 2009; 68 RS Pinals (3169_CR33) 1977; 4 Y Qi (3169_CR31) 2014; 10 H Brouwers (3169_CR35) 2015; 29 I Navarro-Millán (3169_CR34) 2019; 58 CH Patel (3169_CR38) 2019; 18 VP van Halm (3169_CR7) 2006; 66 X Robin (3169_CR23) 2011; 12 T Tomizawa (3169_CR28) 2019; 21 S Kim (3169_CR29) 2014; 9 S Bugatti (3169_CR1) 2018; 5 P Hu (3169_CR36) 2011; 38 MS Chimenti (3169_CR5) 2015; 6 H Liao (3169_CR8) 2004; 50 A Zabek (3169_CR13) 2016; 117 G Kerekes (3169_CR6) 2014; 10 A Saraux (3169_CR2) 2002; 47 J Chong (3169_CR21) 2019; 68 NG Mahieu (3169_CR18) 2016; 30 H Luan (3169_CR19) 2020; 2 C Sasaki (3169_CR12) 2019; 14 X Sun (3169_CR32) 2014; 112 H Luan (3169_CR20) 2018; 1036 D Mehrle (3169_CR24) 2015; 3 D Aletaha (3169_CR14) 2010; 62 JS Smolen (3169_CR27) 2018; 4 V Moreira (3169_CR37) 2020; 317 M Souto-Carneiro (3169_CR4) 2020; 79 KJ Archer (3169_CR22) 2014; 13 A Wasserman (3169_CR3) 2018; 97 H Luan (3169_CR15) 2014; 13 EEA Arts (3169_CR25) 2017; 76 D Dubey (3169_CR10) 2018 F Ji (3169_CR17) 2019; 378 J Li (3169_CR11) 2018; 37 H Luan (3169_CR16) 2015; 5 P Li (3169_CR30) 2007; 98 AK Carlson (3169_CR9) 2019; 37 |
| References_xml | – volume: 1036 start-page: 66 year: 2018 ident: 3169_CR20 publication-title: Anal Chim Acta doi: 10.1016/j.aca.2018.08.002 – volume: 76 start-page: 1693 year: 2017 ident: 3169_CR25 publication-title: Ann Rheum Dis doi: 10.1136/annrheumdis-2016-210997 – volume: 2 start-page: 89 year: 2020 ident: 3169_CR19 publication-title: Bioinformatics – volume: 38 start-page: 4225 year: 2011 ident: 3169_CR36 publication-title: Mol Biol Rep doi: 10.1007/s11033-010-0545-9 – volume: 13 start-page: S20806 year: 2014 ident: 3169_CR22 publication-title: Cancer Inform doi: 10.4137/CIN.S20806 – volume: 3 start-page: 348 year: 2015 ident: 3169_CR24 publication-title: Netw Sci doi: 10.1017/nws.2015.20 – volume: 9 start-page: e97501 year: 2014 ident: 3169_CR29 publication-title: PLoS ONE doi: 10.1371/journal.pone.0097501 – volume: 98 start-page: 237 year: 2007 ident: 3169_CR30 publication-title: Br J Nutr doi: 10.1017/S000711450769936X – volume: 37 start-page: 1493 year: 2018 ident: 3169_CR11 publication-title: Clin Rheumatol doi: 10.1007/s10067-018-4021-6 – volume: 68 start-page: 954 year: 2009 ident: 3169_CR26 publication-title: Ann Rheum Dis doi: 10.1136/ard.2007.084459 – volume: 37 start-page: 393 year: 2019 ident: 3169_CR9 publication-title: Clin Exp Rheumatol – volume: 5 start-page: 13888 year: 2015 ident: 3169_CR16 publication-title: Sci Rep doi: 10.1038/srep13888 – volume: 97 start-page: 455 year: 2018 ident: 3169_CR3 publication-title: Am Fam Physician – volume: 58 start-page: 933 year: 2019 ident: 3169_CR34 publication-title: Rheumatology doi: 10.1093/rheumatology/key302 – volume: 62 start-page: 2569 year: 2010 ident: 3169_CR14 publication-title: Arthritis Rheum doi: 10.1002/art.27584 – volume: 47 start-page: 155 year: 2002 ident: 3169_CR2 publication-title: Arthritis Care Res (Hoboken). doi: 10.1002/art.10241 – volume: 14 start-page: e0219400 year: 2019 ident: 3169_CR12 publication-title: PLoS ONE doi: 10.1371/journal.pone.0219400 – volume: 4 start-page: 414 year: 1977 ident: 3169_CR33 publication-title: J Rheumatol – volume: 112 start-page: 477 year: 2014 ident: 3169_CR32 publication-title: Br J Nutr doi: 10.1017/S0007114514001056 – volume: 66 start-page: 184 year: 2006 ident: 3169_CR7 publication-title: Ann Rheum Dis doi: 10.1136/ard.2006.051672 – year: 2018 ident: 3169_CR10 publication-title: J Proteome Res doi: 10.1021/acs.jproteome.8b00439 – volume: 29 start-page: 741 year: 2015 ident: 3169_CR35 publication-title: Best Pract Res Clin Rheumatol doi: 10.1016/j.berh.2016.02.003 – volume: 6 start-page: e1887 year: 2015 ident: 3169_CR5 publication-title: Cell Death Dis doi: 10.1038/cddis.2015.246 – volume: 117 start-page: 544 year: 2016 ident: 3169_CR13 publication-title: J Pharm Biomed Anal doi: 10.1016/j.jpba.2015.10.007 – volume: 317 start-page: 108903 year: 2020 ident: 3169_CR37 publication-title: Chem Biol Interact doi: 10.1016/j.cbi.2019.108903 – volume: 10 start-page: 691 year: 2014 ident: 3169_CR6 publication-title: Nat Rev Rheumatol doi: 10.1038/nrrheum.2014.121 – volume: 18 start-page: 669 year: 2019 ident: 3169_CR38 publication-title: Nat Rev Drug Discov doi: 10.1038/s41573-019-0032-5 – volume: 50 start-page: 3792 year: 2004 ident: 3169_CR8 publication-title: Arthritis Rheum doi: 10.1002/art.20720 – volume: 30 start-page: 87 year: 2016 ident: 3169_CR18 publication-title: Curr Opin Chem Biol doi: 10.1016/j.cbpa.2015.11.009 – volume: 79 start-page: 499 year: 2020 ident: 3169_CR4 publication-title: Ann Rheum Dis doi: 10.1136/annrheumdis-2019-216374 – volume: 21 start-page: 174 year: 2019 ident: 3169_CR28 publication-title: Arthritis Res Ther doi: 10.1186/s13075-019-1956-1 – volume: 5 start-page: 1 year: 2018 ident: 3169_CR1 publication-title: Front Med doi: 10.3389/fmed.2018.00339/full – volume: 4 start-page: 18001 year: 2018 ident: 3169_CR27 publication-title: Nat Rev Dis Prim doi: 10.1038/nrdp.2018.1 – volume: 378 start-page: 120738 year: 2019 ident: 3169_CR17 publication-title: J Hazard Mater doi: 10.1016/j.jhazmat.2019.06.015 – volume: 13 start-page: 1527 year: 2014 ident: 3169_CR15 publication-title: J Proteome Res doi: 10.1021/pr401068k – volume: 68 start-page: 1 year: 2019 ident: 3169_CR21 publication-title: Curr Protoc Bioinforma doi: 10.1002/cpbi.86 – volume: 10 start-page: 2617 year: 2014 ident: 3169_CR31 publication-title: Mol Biosyst doi: 10.1039/c4mb00131a – volume: 12 start-page: 77 year: 2011 ident: 3169_CR23 publication-title: BMC Bioinform doi: 10.1186/1471-2105-12-77 |
| SSID | ssj0024549 |
| Score | 2.52966 |
| Snippet | Background
Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers... Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for... Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers... Abstract Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 500 |
| SubjectTerms | Amino acids Arthritis, Rheumatoid Biological markers Biomarkers Biomedical and Life Sciences Biomedicine Blood lipids Chromatography Chronic illnesses Development and progression Diagnosis Discriminant analysis Disease Disease Biomarkers Energy metabolism Health aspects Histidine Humans Identification and classification Learning algorithms Lipid metabolism Lipidomic Lipidomics Lipids Medicine/Public Health Metabolism Metabolites Metabolomic Metabolomics Phenylalanine Phosphatidic acid Phosphatidylethanolamine Physiological aspects Prediction models Rheumatoid arthritis Seronegative Serum |
| SummonAdditionalLinks | – databaseName: Openly Available Collection - DOAJ dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQDzwOiHcDBRkJiQONiuPEdo4FUVVI5USl3qzYHrORttnVZkPVv8CvZuw82IBUOHBM7CS25_M8lPE3hLzhTngOuL89AE9DqftUGYxSCueV4Rw45OFw8tkXcXqef74oLnZKfYWcsJ4euF-4o8oyo6xwAsCi8RTKegkCDZWQoWZE1L7vVTkGUyPLHoY94xEZJY5atGqoEEI6AoJYlKmcmaHI1v-nTt4xSr8nTE5_Te-RO12zrq6vquVyxzCdPCD3B4-SHvczeUhuQfOI3D4b_pk_Jj9QHXSX9BK2KO9lOIRMq8bRZb2uXbzqy3bjd2jt-twhaKnrU_DwnTSc0A9JPJuWoodLEbOrPtXrO8Q3hRsNfIsM4nSzgA6d4FXtKK7tIlIm0YG8tX1Czk8-ff14mg4VGFIrJN-mLstRZqzivvCVsEUmTSkKLx1Dw1cyY7xRmWTAQGa5RE9TVd4VrEKtUBmeAX9K9hocwj6h3qAkAb0nnpV5Xip0E5ziglvU1M6DTQgbBaLtQE8eqmQsdQxTlNC9EDUKUUchapmQd9Mz656c48beH4Kcp56BWDveQLjpAW76b3BLyNuAEh22Pw7PVsMpBpxkINLSx0KJnJU55wk5mPXEbWvnzSPO9KA2Wo3hLEOdiF5UQl5PzeHJkArXwKoLfXAgGNYXOKFnPSynKXEMP9ElLRMiZ4CdzXne0tSLSCqu0FXPWZ6QwxHav4Z105oeTvD_BxE8_x8ieEHuZmEnh5wieUD2tpsOXqJnuDWvohL4CcRbX3U priority: 102 providerName: Directory of Open Access Journals – databaseName: Proquest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1baxQxFA51C14exLujVSIIPtihZDKTZB5EWmkpQhcRC30LmVy6C9vZdS-Kf8Ff7TmZy3YUio87k5lN9nw5-bI55zuEvOVOBO5hfgfveYql7lNVwS6lcEFVnHvuc0xOPhuL0_P880VxsUPGXS4MhlV2PjE6aje3-B_5ATB9BnCBBebj4nuKVaPwdLUroWHa0gruQ5QYu0V2M1TGGpHdo-Pxl69b9T3YDnWpM0ocrGC1A0eBYQoAblGmcrA8RRX_f331tcXq70DK_jT1HrmzqRfm108zm11bsE4ekPst06SHDTQekh1fPyK3z9qz9MfkN7iJzRW98mvAwQyTk6mpHZ1NF1MXPzXlvOF76NQ1MUV-RV0TmgfvpJi5j8E9yxUF5ksBy_MmBOyHj2_CC7W_jMridDnxGyDH86mjANZJlFKirajr6gk5Pzn-9uk0bSszpFZIvk5dloMtmeGhCEbYIpNVKYogHYMFsWRVFSqVSeaZl1kugYEqE1zBDHgLU_HM86dkVEMXnhMaKmOZB1bFszLPSwX0wSkuuAUP7oK3CWGdQbRtZcuxesZMx-2LEroxogYj6mhELRPyvn9m0Yh23Nj6CO3ct0TB7XhhvrzU7fzV0MlKWeGE9xY4nFA2SC-ALwmJpUuyhLxDlGh0C9A9a9rsBhgkCmzpQ6FEzsqc84TsDVrCdLbD2x3OdOtOVnoL_oS86W_jkxgiV_v5BttAR2C7X8CAnjWw7IfEYVsKVLVMiBwAdjDm4Z16Ooli4woofM7yhOx30N5266bfdL-H_3-Y4MXNg35J7mY4RzGKSO6R0Xq58a-AC66r1-0E_wPgFV2Y priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELagSDwOiDeBgoyExIFGlePEdo6loqqQyolKvVmOPWYjpdnVZgPiL_CrGTvZdAOogmPiR-zM6xt5ZkzIW-6E54Dy7QF4Gq66T1WFXkrhvKo4Bw55SE4--yxOz_NPF8XFWCYn5MLsnt8zJQ47tEcoyiGQANlPlKm8SW6hkRLxYFYcX9XVQ0dnmxTz13EzwxPr8_-phXfM0O8hktM56T1yp29X5sd30zQ7pujkAbk_Ykh6NBD9IbkB7SNy-2w8JX9MfqIC6C_pJWyQwk1IO6amdbSpV7WLT8NF3fgdWrshWgg66oagO5yThpz8ELaz7ihiWopcuhyCu75BnCm8aOFrrBlO1wvoEfYua0eRDRexSBIdy7V2T8j5yccvx6fpeOdCaoXkm9RlOVKJGe4Lb4QtMlmVovDSMTR1JasqX6lMMmAgs1witlTGu4IZ1AOm4hnwp2SvxSU8J9RXxjJAvMSzMs9LhcDAKS64Rd3sPNiEsC1BtB0Lkod7MRodHRMl9EBEjUTUkYhaJuT9NGY1lOO4tveHQOepZyilHV8gh-lRMjUuslJWOAFgEZ0JZb0EgUhIyHApSZaQd4FLdBB4XJ41Y94CbjKUztJHQomclTnnCdmf9URBtfPmLZ_pUVF0Gh1YhloQcVNC3kzNYWQIfmth2Yc-uBB05Avc0LOBLactcXQ4EYSWCZEzhp3ted7S1otYRlwhOM9ZnpCDLWtfLeu6f3owsf8_kODF_83-ktzNgsyGeCG5T_Y26x5eIerbVK-juP8CnAdQCA priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6VrcTjwLNAoCAjIXGg2cpxYjvHBVFVSK04sFI5WYkf3VXT7Gp3UwQ_gV_NOC-agiqQuO3GduRxZr75nMyMAV4zwx2zaN_OWhb6o-5DmeMuJTFO5oxZZmOfnHx0zA-n8ceT5GQLpl0uzMYjdNG-Bwu778vjy7noRQ3g-EOf7S-Na-xe8v01ui60eh9zgJrK01DcgG2eIEUfwfb0-NPkS51pJNIwQQjoEmj-OHDgpOpa_r8j9iWXdTWcsv-megduVeUy-_Y1K4pLbuvgHlx0AjfRKmfjapOP9fcrtSD_-4rch7st0SWTRjMfwJYtH8LNo_bGj-AHolR1Ts7tBtWw8LnRJCsNKebLuan_NaeJo4BkbpqQJrsmpokMxHsSPzUfW7RaEyTeBE1p0USgXdj6Tv5CaU_rwuZkNbMVcvPF3BC0lVldyYm0NWXXOzA9-PD5_WHYHgwRai7YJjRRjKpEM-YSl3GdRCJPeeKEoeiPU5rnLpeRoJZaEeHzplxmziQ0Q7DKchZZ9hhGJU7hKRCXZ5paJHUsSuM4lchejGScaXQgxlkdAO00Qem2aro_vKNQ9e5JctUsssJFVvUiKxHA237MsqkZcm3vd17B-p6-3nd9YbE6VS18KJxkLjU33FqNFJJL7YTlSNe48CenRAG88eqpPCp5Pcja5AoU0tf3UhMueUzTmLEAdgc9EU30sLlTcNWi2VrhLpsiVCO5C-BV3-xH-gi90i4q3wcnEiEfR4GeNPbQi8RwV4xMOQ1ADCxlIPOwpZzP6lrnEncQMY0D2Ots6te0rlvTvd7u_uIRPPu37s_hduRtygc1iV0YbVaVfYHUdJO_bIHmJzI9iow priority: 102 providerName: Unpaywall |
| Title | Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients |
| URI | https://link.springer.com/article/10.1186/s12967-021-03169-7 https://www.ncbi.nlm.nih.gov/pubmed/34876179 https://www.proquest.com/docview/2611319381 https://www.proquest.com/docview/2608125457 https://pubmed.ncbi.nlm.nih.gov/PMC8650414 https://translational-medicine.biomedcentral.com/track/pdf/10.1186/s12967-021-03169-7 https://doaj.org/article/ac1b8c6d6eec43968cf7e62156708162 |
| UnpaywallVersion | publishedVersion |
| Volume | 19 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central_OA刊 customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: RBZ dateStart: 20030101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: KQ8 dateStart: 20030701 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: KQ8 dateStart: 20030101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: DOA dateStart: 20030101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: ABDBF dateStart: 20030101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: DIK dateStart: 20030101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: M~E dateStart: 20030101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: RPM dateStart: 20030101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection (Proquest) customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal - Open Access customDbUrl: eissn: 1479-5876 dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: M48 dateStart: 20031201 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: AAJSJ dateStart: 20030601 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1479-5876 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0024549 issn: 1479-5876 databaseCode: C6C dateStart: 20030106 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1ba9swFD70Ars8jN3nrQsaDPawukOWLckPYyShpQwSSlkg24uwLbkJpE6Wy7b-hf3qHcmX1lsJewnYkmVJ5_ad-OgcgLdM85wZlO_cGObbUve-TNFLiXQuU8YMM6E9nDwY8tNR-HkcjXegLndUbeDqVtfO1pMaLWdHv75ffUKB_-gEXvIPK7RZKO422ABZlMe-2IV9tFSxLeUwCOV17r3IwWEaitiPUA3Uh2huHaNlqFw-_3-19g2z9XdIZfNd9T7c3RSL5OpnMpvdMF0nD-FBhTlJt2SSR7BjisdwZ1B9VX8Cv1FhbC7JpVkjR8zsMWWSFJrMpoupdldlYW98D5nqMrrIrIgug_RwTGLP8Nswn-WKIAYmyNXzMhjsh3Ej2RuFuXA5xslyYjYIk-dTTZBtJy6pEqnSu66ewujk-Ev_1K9qNPgZF2zt6yBEqtKE5VGe8CwKRBrzKBeaommMaZrmqQwENdSIALedcpnkOqIJ6o0kZYFhz2CvwCm8AJKnSUYN4isWxGEYSwQSWjLOMtTlOjeZB7QmiMqqBOa2jsZMOUdGclUSUSERlSOiEh68b55ZlOk7tvbuWTo3PW3qbXdjvrxQlSQrnGQqM665MRmiOS6zXBiOyIkLW8Qk8OCd5RJlWRanlyXVOQdcpE21pbpc8pDGIWMeHLR6omBn7eaaz1QtFwodXopaE3GWB2-aZvukDZYrzHxj--BE0PGPcEHPS7ZslsTQQUXQGnsgWgzbWnO7pZhOXNpxiWA-pKEHhzVrX09r254eNuz_HyR4uX1PXsG9wMqojScSB7C3Xm7Ma0SF67QDu2IsOrDfOx6eneNVn_c77h-WjlMC-Hve-4bto-FZ9-sfJVNjCw |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELfGJjF4QHwTGGAkEA8smhI7tvMwoQ02bWytENqkvZnEdtZKXVqalmn_An8Ufxt3-eoCUsXLHhs77rn39bv6fEfIW2ZFxhzod-Yc87HVva9SiFIim6mUMcccx8vJvb44OOVfzqKzFfK7uQuDaZWNTSwNtR0b_I98C5B-AOICDubj5IePXaPwdLVpoZHUrRXsdllirL7YceSuLiGEK7YPPwO_34Xh_t7JpwO_7jLgGyHZzLchB7qChGVRlggThTKNRZRJG4Bxj4M0zVIVysAFToZcAppSSWajIAHJT1IWOgbr3iJrnPEYgr-13b3-12-Lan8QfjVXdZTYKsC7gmHCtAhQJhH7suMOy64B__qGa87x78TN9vT2Llmf55Pk6jIZja45yP375F6NbOlOJYoPyIrLH5Lbvfrs_hH5BWZpfkEv3AzkboSXoWmSWzoaToa2_FS1D4fvoUNb5TC5gtoqFRDWpFgpAJOJpgUFpE1Bd8ZVytlPV66ED3J3XlYyp9OBmwMYHw8tBeUYlKWbaF1EtnhMTm-ER0_Iag4kPCM0SxMTOEBxLIw5jxXAFauYYAY8hs2c8UjQMESbukw6dusY6TJcUkJXTNTARF0yUUuPfGjfmVRFQpbO3kU-tzOxwHf5YDw917W90EBkqoywwjkDmFEok0knAJ8Jia1SQo-8RynRaIaAPJPUtylgk1jQS-8IJXgQc8Y8stGZCebDdIcbOdO1-Sr0Qtk88qYdxjcxJS934znOAUJCAOCwoaeVWLZbYhAGAzSOPSI7AtvZc3ckHw7K4uYKQgYecI9sNqK9IGvZb7rZiv9_sOD58k2_JusHJ71jfXzYP3pB7oSor5jBJDfI6mw6dy8Bh87SV7WyU_L9pu3LH0gUmm4 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Jb9QwFLagSIUeEFshUMBISBxoVDl2bOdYBkZlacWBSr1ZiZfOSNPMaBYq_gK_mvecTDoBVMEx8RI7b_ssv4WQ19zJwD3Id_Cep1jqPtUVnFJyF3TFuedeYHDy8Yk8OhWfzvKzjSj-6O2-vpJsYhowS1O9PJi50Ii4lgcLsFIg4OheAEwpi1TdJLcEWDesYTCQg6tse3D8WYfK_HVczxzFrP1_6uYN4_S742R3e7pDbq_qWfnjspxMNgzU8B652yJLetiwwn1yw9cPyPZxe3f-kPwEtbC6oBd-CXSfYDAyLWtHJ-PZ2MWnpnw3fIeOXeND5BfUNa54MCfFSH105pkvKCBdCrw7bVy-vvs4E76o_XnMJE7nI78CMDwdOwrMOYqpk2ibxHXxiJwOP3wbHKVtJYbUSsWXqcsE0I6VPOShlDbPVFXIPCjHwAAWrKpCpTPFPPMqEwoQpy6Dy1kJ2qGseOb5LtmqYQlPCA1VaZkHFMWzQohCA1xwmktuQWO74G1C2JogxrZpyrFaxsTE44qWpiGiASKaSESjEvK2GzNrknRc2_sd0rnriQm244vp_Ny08mpgkZW20knvLWA2qW1QXgI-kgpLlWQJeYNcYlANwPJs2UYzwCYxoZY5lFoKVgjOE7LX6wnia_vNaz4zrfpYGDjWMtCNgKYS8qprxpHoElf76Qr7IL8DAIYNPW7YstsSh2MoQNMiIarHsL0991vq8SgmF9cA2QUTCdlfs_bVsq77p_sd-_8DCZ7-3-wvyfbX90Pz5ePJ52fkTobiiw5Fao9sLecr_xxg4bJ6ESX_F_FLWz4 |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6VrcTjwLNAoCAjIXGg2cpxYjvHBVFVSK04sFI5WYkf3VXT7Gp3UwQ_gV_NOC-agiqQuO3GduRxZr75nMyMAV4zwx2zaN_OWhb6o-5DmeMuJTFO5oxZZmOfnHx0zA-n8ceT5GQLpl0uzMYjdNG-Bwu778vjy7noRQ3g-EOf7S-Na-xe8v01ui60eh9zgJrK01DcgG2eIEUfwfb0-NPkS51pJNIwQQjoEmj-OHDgpOpa_r8j9iWXdTWcsv-megduVeUy-_Y1K4pLbuvgHlx0AjfRKmfjapOP9fcrtSD_-4rch7st0SWTRjMfwJYtH8LNo_bGj-AHolR1Ts7tBtWw8LnRJCsNKebLuan_NaeJo4BkbpqQJrsmpokMxHsSPzUfW7RaEyTeBE1p0USgXdj6Tv5CaU_rwuZkNbMVcvPF3BC0lVldyYm0NWXXOzA9-PD5_WHYHgwRai7YJjRRjKpEM-YSl3GdRCJPeeKEoeiPU5rnLpeRoJZaEeHzplxmziQ0Q7DKchZZ9hhGJU7hKRCXZ5paJHUsSuM4lchejGScaXQgxlkdAO00Qem2aro_vKNQ9e5JctUsssJFVvUiKxHA237MsqkZcm3vd17B-p6-3nd9YbE6VS18KJxkLjU33FqNFJJL7YTlSNe48CenRAG88eqpPCp5Pcja5AoU0tf3UhMueUzTmLEAdgc9EU30sLlTcNWi2VrhLpsiVCO5C-BV3-xH-gi90i4q3wcnEiEfR4GeNPbQi8RwV4xMOQ1ADCxlIPOwpZzP6lrnEncQMY0D2Ots6te0rlvTvd7u_uIRPPu37s_hduRtygc1iV0YbVaVfYHUdJO_bIHmJzI9iow |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Serum+metabolomic+and+lipidomic+profiling+identifies+diagnostic+biomarkers+for+seropositive+and+seronegative+rheumatoid+arthritis+patients&rft.jtitle=Journal+of+translational+medicine&rft.au=Luan%2C+Hemi&rft.au=Gu%2C+Wanjian&rft.au=Li%2C+Hua&rft.au=Wang%2C+Zi&rft.date=2021-12-07&rft.pub=BioMed+Central+Ltd&rft.issn=1479-5876&rft.eissn=1479-5876&rft.volume=19&rft.issue=1&rft_id=info:doi/10.1186%2Fs12967-021-03169-7&rft.externalDocID=A686419433 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1479-5876&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1479-5876&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1479-5876&client=summon |