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Cheng X, Meng X, Chen R, Song Z, Li S, Wei S, Lv H, Zhang S, Tang H, Jiang Y, Zhang R. The molecular subtypes of autoimmune diseases. Comput Struct Biotechnol J 2024; 23:1348-1363. [PMID: 38596313 PMCID: PMC11001648 DOI: 10.1016/j.csbj.2024.03.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024] Open
Abstract
Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.
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Affiliation(s)
| | | | | | - Zerun Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuai Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuhao Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hao Tang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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2
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Zhao J, Li L, Li J, Zhang L. Application of artificial intelligence in rheumatic disease: a bibliometric analysis. Clin Exp Med 2024; 24:196. [PMID: 39174664 PMCID: PMC11341591 DOI: 10.1007/s10238-024-01453-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024]
Abstract
The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. "Scientific Reports" emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.
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Affiliation(s)
- Junkang Zhao
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, No. 99 Longcheng Street, Taiyuan, 030032, China
- Shanxi Academy of Advanced Research and Innovation (SAARI), No.7, Xinhua Road, Xiaodian District, Taiyuan, Shanxi, China
| | - Linxin Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, No. 99 Longcheng Street, Taiyuan, 030032, China
| | - Jie Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, No. 99 Longcheng Street, Taiyuan, 030032, China
| | - Liyun Zhang
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, No. 99 Longcheng Street, Taiyuan, 030032, China.
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3
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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues. Front Immunol 2024; 15:1428773. [PMID: 39161769 PMCID: PMC11330812 DOI: 10.3389/fimmu.2024.1428773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/24/2024] [Indexed: 08/21/2024] Open
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- Institute of Computational Life Sciences, Zürich University of Applied Sciences (ZHAW), Wädenswil, Switzerland
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - María Rodríguez Martínez
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, United States
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4
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Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, Shan Y, Zheng Y, Zhao F, Lv X, Guo S, Wang F, He D. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol 2024; 15:1409555. [PMID: 38915408 PMCID: PMC11194317 DOI: 10.3389/fimmu.2024.1409555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
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Affiliation(s)
- Yiming Shi
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Mi Zhou
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yu Shan
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yixin Zheng
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Fuyu Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xinliang Lv
- Traditional Chinese Medicine Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, China
| | - Shicheng Guo
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fubo Wang
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Department of Urology, Affiliated Tumor Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
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5
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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573505. [PMID: 38234732 PMCID: PMC10793435 DOI: 10.1101/2023.12.28.573505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
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6
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Pelissier A, Laragione T, Harris C, Martínez MR, Gulko PS. Gene Network Analyses Identify Co-regulated Transcription Factors and BACH1 as a Key Driver in Rheumatoid Arthritis Fibroblast-like Synoviocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573506. [PMID: 38234777 PMCID: PMC10793426 DOI: 10.1101/2023.12.28.573506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying Rheumatoid Arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks (GRNs) to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This is the first time that BACH1 is shown to have a central role in the regulation of FLS phenotypes, and gene expression signatures, as well as in ferroptosis and fatty acid metabolism. These new discoveries have the potential to become new targets for treatments aimed at selectively targeting the RA FLS.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Carolyn Harris
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Ruschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
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7
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Siddiqui F, Aslam D, Tanveer K, Soudy M. The Role of Artificial Intelligence and Machine Learning in Autoimmune Disorders. STUDIES IN COMPUTATIONAL INTELLIGENCE 2024:61-75. [DOI: 10.1007/978-981-99-9029-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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8
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Kwon G, Wiedemann A, Steinheuer LM, Stefanski AL, Szelinski F, Racek T, Frei AP, Hatje K, Kam-Thong T, Schubert D, Schindler T, Dörner T, Thurley K. Transcriptional profiling upon T cell stimulation reveals down-regulation of inflammatory pathways in T and B cells in SLE versus Sjögren's syndrome. NPJ Syst Biol Appl 2023; 9:62. [PMID: 38102122 PMCID: PMC10724199 DOI: 10.1038/s41540-023-00319-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/30/2023] [Indexed: 12/17/2023] Open
Abstract
Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) share clinical as well as pathogenic similarities. Although previous studies suggest various abnormalities in different immune cell compartments, dedicated cell-type specific transcriptomic signatures are often masked by patient heterogeneity. Here, we performed transcriptional profiling of isolated CD4, CD8, CD16 and CD19 lymphocytes from pSS and SLE patients upon T cell stimulation, in addition to a steady-state condition directly after blood drawing, in total comprising 581 sequencing samples. T cell stimulation, which induced a pronounced inflammatory response in all four cell types, gave rise to substantial re-modulation of lymphocyte subsets in the two autoimmune diseases compared to healthy controls, far exceeding the transcriptomic differences detected at steady-state. In particular, we detected cell-type and disease-specific down-regulation of a range of pro-inflammatory cytokine and chemokine pathways. Such differences between SLE and pSS patients are instrumental for selective immune targeting by future therapies.
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Affiliation(s)
- Gino Kwon
- Systems Biology of Inflammation, German Rheumatism Research Center, a Leibniz-Institute, Berlin, Germany
| | - Annika Wiedemann
- Rheumatology and Clinical Immunology, Department of Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa M Steinheuer
- Biomathematics Division, Institute of Experimental Oncology, University Hospital Bonn, Bonn, Germany
| | - Ana-Luisa Stefanski
- Rheumatology and Clinical Immunology, Department of Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Franziska Szelinski
- Rheumatology and Clinical Immunology, Department of Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Tomas Racek
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Andreas Philipp Frei
- Roche Pharma Research and Early Development, Immunology, Infectious Diseases and Ophthalmology (I2O) Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Klas Hatje
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Tony Kam-Thong
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - David Schubert
- Roche Pharma Research and Early Development, Immunology, Infectious Diseases and Ophthalmology (I2O) Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Thomas Schindler
- Product Development Immunology, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Thomas Dörner
- Rheumatology and Clinical Immunology, Department of Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.
| | - Kevin Thurley
- Systems Biology of Inflammation, German Rheumatism Research Center, a Leibniz-Institute, Berlin, Germany.
- Biomathematics Division, Institute of Experimental Oncology, University Hospital Bonn, Bonn, Germany.
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Akal F, Batu ED, Sonmez HE, Karadağ ŞG, Demir F, Ayaz NA, Sözeri B. Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study. Med Biol Eng Comput 2022; 60:3601-3614. [DOI: 10.1007/s11517-022-02699-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 10/02/2022] [Indexed: 11/11/2022]
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10
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Momtazmanesh S, Nowroozi A, Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review. Rheumatol Ther 2022; 9:1249-1304. [PMID: 35849321 PMCID: PMC9510088 DOI: 10.1007/s40744-022-00475-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 06/24/2022] [Indexed: 11/23/2022] Open
Abstract
Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.
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Affiliation(s)
- Sara Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran
| | - Ali Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nima Rezaei
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
- Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran.
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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11
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Structural Strength Analyses for Low Brass Filler Biomaterial with Anti-Trauma Effects in Articular Cartilage Scaffold Design. MATERIALS 2022; 15:ma15134446. [PMID: 35806568 PMCID: PMC9267688 DOI: 10.3390/ma15134446] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/28/2022] [Accepted: 06/03/2022] [Indexed: 01/27/2023]
Abstract
The existing harder biomaterial does not protect the tissue cells with blunt-force trauma effects, making it a poor choice for the articular cartilage scaffold design. Despite the traditional mechanical strengths, this study aims to discover alternative structural strengths for the scaffold supports. The metallic filler polymer reinforced method was used to fabricate the test specimen, either low brass (Cu80Zn20) or titanium dioxide filler, with composition weight percentages (wt.%) of 0, 2, 5, 15, and 30 in polyester urethane adhesive. The specimens were investigated for tensile, flexural, field emission scanning electron microscopy (FESEM), and X-ray diffraction (XRD) tests. The tensile and flexural test results increased with wt.%, but there were higher values for low brass filler specimens. The tensile strength curves were extended to discover an additional tensile strength occurring before 83% wt.%. The higher flexural stress was because of the Cu solvent and Zn solute substituting each other randomly. The FESEM micrograph showed a cubo-octahedron shaped structure that was similar to the AuCu3 structure class. The XRD pattern showed two prominent peaks of 2θ of 42.6° (110) and 49.7° (200) with d-spacings of 1.138 Å and 1.010 Å, respectively, that indicated the typical face-centred cubic superlattice structure with Cu and Zn atoms. Compared to the copper, zinc, and cart brass, the low brass indicated these superlattice structures had ordered–disordered transitional states. As a result, this additional strength was created by the superlattice structure and ordered–disordered transitional states. This innovative strength has the potential to develop into an anti-trauma biomaterial for osteoarthritic patients.
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Yamamoto M, Nojima M, Kamekura R, Kuribara-Souta A, Uehara M, Yamazaki H, Yoshikawa N, Aochi S, Mizushima I, Watanabe T, Nishiwaki A, Komai T, Shoda H, Kitagori K, Yoshifuji H, Hamano H, Kawano M, Takano KI, Fujio K, Tanaka H. The differential diagnosis of IgG4-related disease based on machine learning. Arthritis Res Ther 2022; 24:71. [PMID: 35305690 PMCID: PMC8933663 DOI: 10.1186/s13075-022-02752-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/27/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION To eliminate the disparity and maldistribution of physicians and medical specialty services, the development of diagnostic support for rare diseases using artificial intelligence is being promoted. Immunoglobulin G4 (IgG4)-related disease (IgG4-RD) is a rare disorder often requiring special knowledge and experience to diagnose. In this study, we investigated the possibility of differential diagnosis of IgG4-RD based on basic patient characteristics and blood test findings using machine learning. METHODS Six hundred and two patients with IgG4-RD and 204 patients with non-IgG4-RD that needed to be differentiated who visited the participating institutions were included in the study. Ten percent of the subjects were randomly excluded as a validation sample. Among the remaining cases, 80% were used as training samples, and the remaining 20% were used as test samples. Finally, validation was performed on the validation sample. The analysis was performed using a decision tree and a random forest model. Furthermore, a comparison was made between conditions with and without the serum IgG4 concentration. Accuracy was evaluated using the area under the receiver-operating characteristic (AUROC) curve. RESULTS In diagnosing IgG4-RD, the AUROC curve values of the decision tree and the random forest method were 0.906 and 0.974, respectively, when serum IgG4 levels were included in the analysis. Excluding serum IgG4 levels, the AUROC curve value of the analysis by the random forest method was 0.925. CONCLUSION Based on machine learning in a multicenter collaboration, with or without serum IgG4 data, basic patient characteristics and blood test findings alone were sufficient to differentiate IgG4-RD from non-IgG4-RD.
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Affiliation(s)
- Motohisa Yamamoto
- Department of Rheumatology and Allergy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 1088639, Japan.
| | - Masanori Nojima
- Center for Translational Research, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Ryuta Kamekura
- Department of Otolaryngology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Akiko Kuribara-Souta
- Department of Rheumatology and Allergy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 1088639, Japan
| | - Masaaki Uehara
- Department of Rheumatology and Allergy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 1088639, Japan
| | - Hiroki Yamazaki
- Department of Rheumatology and Allergy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 1088639, Japan
| | - Noritada Yoshikawa
- Department of Rheumatology and Allergy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 1088639, Japan.,Division of Rheumatology, Center for Vaccine and Therapy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satsuki Aochi
- Department of Internal Medicine, Japan Self Defense Sapporo Hospital, Sapporo, Japan
| | - Ichiro Mizushima
- Department of Rheumatology, Kanazawa University Hospital, Kanazawa, Japan
| | - Takayuki Watanabe
- Second Department of Internal Medicine, Shinshu University School of Medicine, Matsumoto, Japan
| | - Aya Nishiwaki
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Toshihiko Komai
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hirofumi Shoda
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Kitagori
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hajime Yoshifuji
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Mitsuhiro Kawano
- Department of Rheumatology, Kanazawa University Hospital, Kanazawa, Japan
| | - Ken-Ichi Takano
- Division of Rheumatology, Center for Vaccine and Therapy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hirotoshi Tanaka
- Department of Rheumatology and Allergy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 1088639, Japan.,Division of Rheumatology, Center for Vaccine and Therapy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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13
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Wei M, Chu CQ. Prediction of treatment response: Personalized medicine in the management of rheumatoid arthritis. Best Pract Res Clin Rheumatol 2022; 36:101741. [DOI: 10.1016/j.berh.2021.101741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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José Alcaraz M. New potential therapeutic approaches targeting synovial fibroblasts in rheumatoid arthritis. Biochem Pharmacol 2021; 194:114815. [PMID: 34715065 DOI: 10.1016/j.bcp.2021.114815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 12/18/2022]
Abstract
Synovial cells play a key role in joint destruction during chronic inflammation. In particular, activated synovial fibroblasts (SFs) undergo intrinsic alterations leading to an aggressive phenotype mediating cartilage destruction and bone erosion in rheumatoid arthritis (RA). Recent research has revealed a number of targets to control arthritogenic changes in SFs. Therefore, identification of SF phenotypes, control of epigenetic changes, modulation of cellular functions, or regulation of the activity of cation channels and different signaling pathways has been investigated. Although many of these approaches have shown efficacy in vitro and in animal models of RA, further research is needed to select the most relevant targets for drug development. This review is focused on the role of SFs as a potential strategy to discover novel therapeutic targets in RA aimed at preserving joint architecture and function.
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Affiliation(s)
- María José Alcaraz
- Department of Pharmacology, University of Valencia, and Interuniversity Research Institute for Molecular Recognition and Technological Development (IDM), Polytechnic University of Valencia, University of Valencia, Av. Vicent A. Estellés s/n, 46100 Burjasot, Valencia, Spain.
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15
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Abstract
With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.
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16
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Jung SM, Park KS, Kim KJ. Deep phenotyping of synovial molecular signatures by integrative systems analysis in rheumatoid arthritis. Rheumatology (Oxford) 2021; 60:3420-3431. [PMID: 33230538 DOI: 10.1093/rheumatology/keaa751] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 09/29/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE RA encompasses a complex, heterogeneous and dynamic group of diseases arising from molecular and cellular perturbations of synovial tissues. The aim of this study was to decipher this complexity using an integrative systems approach and provide novel insights for designing stratified treatments. METHODS An RNA sequencing dataset of synovial tissues from 152 RA patients and 28 normal controls was imported and subjected to filtration of differentially expressed genes, functional enrichment and network analysis, non-negative matrix factorization, and key driver analysis. A naïve Bayes classifier was applied to the independent datasets to investigate the factors associated with treatment outcome. RESULTS A matrix of 1241 upregulated differentially expressed genes from RA samples was classified into three subtypes (C1-C3) with distinct molecular and cellular signatures. C3 with prominent immune cells and proinflammatory signatures had a stronger association with the presence of ACPA and showed a better therapeutic response than C1 and C2, which were enriched with neutrophil and fibroblast signatures, respectively. C2 was more occupied by synovial fibroblasts of destructive phenotype and carried highly expressed key effector molecules of invasion and osteoclastogenesis. CXCR2, JAK3, FYN and LYN were identified as key driver genes in C1 and C3. HDAC, JUN, NFKB1, TNF and TP53 were key regulators modulating fibroblast aggressiveness in C2. CONCLUSIONS Deep phenotyping of synovial heterogeneity captured comprehensive and discrete pathophysiological attributes of RA regarding clinical features and treatment response. This result could serve as a template for future studies to design stratified approaches for RA patients.
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Affiliation(s)
- Seung Min Jung
- Division of Rheumatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea
| | - Kyung-Su Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Meehan RT, Amigues IA, Knight V. Precision Medicine for Rheumatoid Arthritis: The Right Drug for the Right Patient-Companion Diagnostics. Diagnostics (Basel) 2021; 11:diagnostics11081362. [PMID: 34441297 PMCID: PMC8391624 DOI: 10.3390/diagnostics11081362] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 12/16/2022] Open
Abstract
Despite the growing number of biologic and JAK inhibitor therapeutic agents available to treat various systemic autoimmune illnesses, the lack of a validated companion diagnostic (CDx) to accurately predict drug responsiveness for an individual results in many patients being treated for years with expensive, ineffective, or toxic drugs. This review will focus primarily on rheumatoid arthritis (RA) therapeutics where the need is greatest due to poor patient outcomes if the optimum drug is delayed. We will review current FDA-approved biologic and small molecule drugs and why RA patients switch these medications. We will discuss the sampling of various tissues for potential CDx and review early results from studies investigating drug responsiveness utilizing advanced technologies including; multiplex testing of cytokines and proteins, autoantibody profiling, genomic analysis, proteomics, miRNA analysis, and metabolomics. By using these new technologies for CDx the goal is to improve RA patient outcomes and achieve similar successes like those seen in oncology using precision medicine guided therapeutics.
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Affiliation(s)
- Richard Thomas Meehan
- Department of Medicine, Rheumatology Division, National Jewish Health, Denver, CO 80206, USA;
- Correspondence:
| | - Isabelle Anne Amigues
- Department of Medicine, Rheumatology Division, National Jewish Health, Denver, CO 80206, USA;
| | - Vijaya Knight
- Immunology Department, Children’s Hospital, Aurora, CO 80045, USA;
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18
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Mate GS, Kureshi AK, Singh BK. An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6712785. [PMID: 34221300 PMCID: PMC8219419 DOI: 10.1155/2021/6712785] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/19/2021] [Accepted: 05/25/2021] [Indexed: 12/31/2022]
Abstract
Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.
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Affiliation(s)
- Gitanjali S. Mate
- Department of Electronics and Telecommunication, JSPM's Rajarshi Shahu College of Engineering, Pune 411033, India
| | - Abdul K. Kureshi
- Department of Electronics, Maulana Mukhtar Ahmad Nadvi Technical Campus, Malegaon 423203, India
| | - Bhupesh Kumar Singh
- Arba Minch Institute of Technology, Arba Minch University, Arba Minch, Ethiopia
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19
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Zafari P, Golpour M, Hafezi N, Bashash D, Esmaeili SA, Tavakolinia N, Rafiei A. Tuberculosis comorbidity with rheumatoid arthritis: Gene signatures, associated biomarkers, and screening. IUBMB Life 2020; 73:26-39. [PMID: 33217772 DOI: 10.1002/iub.2413] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/01/2020] [Accepted: 11/04/2020] [Indexed: 12/19/2022]
Abstract
Rheumatoid arthritis (RA) is known to be related to an elevated risk of infections because of its pathobiology and the use of immunosuppressive therapies. Reactivation of latent tuberculosis (TB) infection is a serious issue in patients with RA, especially after receiving anti-TNFs therapy. TNF blocking reinforces the TB granuloma formation and maintenance and the growth of Mycobacterium tuberculosis (Mtb). After intercurrent of TB infection, the standard recommendation is that the treatment with TNF inhibitors to be withheld despite its impressive effect on suppression of inflammation until the infection has resolved. Knowing pathways and mechanisms that are common between two diseases might help to find the mechanistic basis of this comorbidity, as well as provide us a new approach to apply them as therapeutic targets or diagnostic biomarkers. Also, screening for latent TB before initiation of an anti-TNF therapy can minimize complications. This review summarizes the shared gene signature between TB and RA and discusses the biomarkers for early detection of this infection, and screening procedures as well.
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Affiliation(s)
- Parisa Zafari
- Department of Immunology, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran.,Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Monireh Golpour
- Molecular and Cellular Biology Research Center, Student Research Committee, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Nasim Hafezi
- Department of Immunology, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Davood Bashash
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed-Alireza Esmaeili
- Immunology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Immunology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Naeimeh Tavakolinia
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Rafiei
- Department of Immunology, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
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20
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Curtis JR, Weinblatt M, Saag K, Bykerk VP, Furst DE, Fiore S, St John G, Kimura T, Zheng S, Bingham CO, Wright G, Bergman M, Nola K, Charles-Schoeman C, Shadick N. Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry. Arthritis Care Res (Hoboken) 2020; 73:471-480. [PMID: 33002337 PMCID: PMC8048846 DOI: 10.1002/acr.24471] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 09/24/2020] [Indexed: 12/30/2022]
Abstract
Objective To use unbiased, data‐driven, principal component (PC) and cluster analysis to identify patient phenotypes of rheumatoid arthritis (RA) that might exhibit distinct trajectories of disease progression, response to treatment, and risk for adverse events. Methods Patient demographic, socioeconomic, health, and disease characteristics recorded at entry into a large, single‐center, prospective observational registry cohort, the Brigham and Women’s Rheumatoid Arthritis Sequential Study (BRASS), were harmonized using PC analysis to reduce dimensionality and collinearity. The number of PCs was established by eigenvalue >1, cumulative variance, and interpretability. The resulting PCs were used to cluster patients using a K‐means approach. Longitudinal clinical outcomes were compared between the clusters over 2 years. Results Analysis of 142 variables from 1,443 patients identified 41 PCs that accounted for 77% of the cumulative variance in the data set. Cluster analysis distinguished 5 patient clusters: 1) less RA disease activity/multimorbidity, shorter RA duration, lower incidence of comorbidities; 2) less RA disease activity/multimorbidity, longer RA duration, more infections, psychiatric comorbidities, health care utilization; 3) moderate RA disease activity/multimorbidity, more neurologic comorbidity; 4) more RA disease activity/multimorbidity, shorter RA duration, more metabolic comorbidity, higher body mass index; 5) more RA disease activity/multimorbidity, longer RA duration, more hepatic, orthopedic comorbidity and RA‐related surgeries. The clusters exhibited differences in clinical outcomes over 2 years of follow‐up. Conclusion Data‐driven analysis of the BRASS registry identified 5 distinct phenotypes of RA. These results illustrate the potential of data‐driven patient profiling as a tool to support personalized medicine in RA. Validation in an independent data set is ongoing.
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Affiliation(s)
| | | | | | | | - Daniel E Furst
- University of California, Los Angeles, University of Washington, Seattle, and University of Florence, Florence, Italy
| | | | | | | | | | | | | | - Martin Bergman
- Drexel University College of Medicine, Philadelphia, Pennsylvania
| | - Kamala Nola
- Lipscomb University College of Pharmacy and Health Sciences, Nashville, Tennessee
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21
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Pandit A, Radstake TRDJ. Machine learning in rheumatology approaches the clinic. Nat Rev Rheumatol 2020; 16:69-70. [PMID: 31908355 DOI: 10.1038/s41584-019-0361-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Aridaman Pandit
- Division Internal Medicine and Dermatology and Centre for Translational Immunology, University Medical Center Utrecht, Utrecht, Netherlands.
| | - Timothy R D J Radstake
- Division Internal Medicine and Dermatology and Centre for Translational Immunology, University Medical Center Utrecht, Utrecht, Netherlands.
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22
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Min HK, Moon SJ, Park KS, Kim KJ. Integrated systems analysis of salivary gland transcriptomics reveals key molecular networks in Sjögren's syndrome. Arthritis Res Ther 2019; 21:294. [PMID: 31856901 PMCID: PMC6921432 DOI: 10.1186/s13075-019-2082-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 12/04/2019] [Indexed: 02/08/2023] Open
Abstract
Background Treatment of patients with Sjögren’s syndrome (SjS) is a clinical challenge with high unmet needs. Gene expression profiling and integrative network-based approaches to complex disease can offer an insight on molecular characteristics in the context of clinical setting. Methods An integrated dataset was created from salivary gland samples of 30 SjS patients. Pathway-driven enrichment profiles made by gene set enrichment analysis were categorized using hierarchical clustering. Differentially expressed genes (DEGs) were subjected to functional network analysis, where the elements of the core subnetwork were used for key driver analysis. Results We identified 310 upregulated DEGs, including nine known genetic risk factors and two potential biomarkers. The core subnetwork was enriched with the processes associated with B cell hyperactivity. Pathway-based subgrouping revealed two clusters with distinct molecular signatures for the relevant pathways and cell subsets. Cluster 2, with low-grade inflammation, showed a better response to rituximab therapy than cluster 1, with high-grade inflammation. Fourteen key driver genes appeared to be essential signaling mediators downstream of the B cell receptor (BCR) signaling pathway and to have a positive relationship with histopathology scores. Conclusion Integrative network-based approaches provide deep insights into the modules and pathways causally related to SjS and allow identification of key targets for disease. Intervention adjusted to the molecular traits of the disease would allow the achievement of better outcomes, and the BCR signaling pathway and its leading players are promising therapeutic targets.
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Affiliation(s)
- Hong Ki Min
- Division of Rheumatology, Department of Internal Medicine, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Su-Jin Moon
- Division of Rheumatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kyung-Su Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
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