Pub Date : 2024-03-07DOI: 10.1038/s41584-024-01100-0
Sarah Onuora
Results of a new study indicate that eosinophils have a role in maintaining bone homeostasis through their inhibitory effects on bone-resorbing osteoclasts.
一项新研究的结果表明，嗜酸性粒细胞通过抑制骨吸收破骨细胞，在维持骨平衡方面发挥作用。
{"title":"Eosinophils regulate bone remodelling","authors":"Sarah Onuora","doi":"10.1038/s41584-024-01100-0","DOIUrl":"10.1038/s41584-024-01100-0","url":null,"abstract":"Results of a new study indicate that eosinophils have a role in maintaining bone homeostasis through their inhibitory effects on bone-resorbing osteoclasts.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140053533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-07DOI: 10.1038/s41584-024-01099-4
Robert Phillips
New research has identified apolipoprotein E expressed by fibroblasts and macrophages in the infrapatellar fat pad and synovium as a pathogenetic mediator and potential therapeutic target in knee osteoarthritis.
{"title":"APOE in fat pad and synovium contributes to knee OA","authors":"Robert Phillips","doi":"10.1038/s41584-024-01099-4","DOIUrl":"10.1038/s41584-024-01099-4","url":null,"abstract":"New research has identified apolipoprotein E expressed by fibroblasts and macrophages in the infrapatellar fat pad and synovium as a pathogenetic mediator and potential therapeutic target in knee osteoarthritis.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140059935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-07DOI: 10.1038/s41584-024-01101-z
Jessica McHugh
Ribonucleoprotein complexes containing Xist, a long non-coding RNA involved in X chromosome inactivation, are immunogenic and promote autoimmune responses.
核糖核蛋白复合物含有 Xist（一种参与 X 染色体失活的长非编码 RNA），具有免疫原性，可促进自身免疫反应。
{"title":"Immunogenic Xist ribonucleoproteins drive sex-biased autoimmunity","authors":"Jessica McHugh","doi":"10.1038/s41584-024-01101-z","DOIUrl":"10.1038/s41584-024-01101-z","url":null,"abstract":"Ribonucleoprotein complexes containing Xist, a long non-coding RNA involved in X chromosome inactivation, are immunogenic and promote autoimmune responses.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140059936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.1038/s41584-024-01086-9
Timothy B. Niewold, Ivona Aksentijevich, Peter D. Gorevic, Greg Gibson, Qingping Yao
In genomic medicine, the concept of genetically transitional disease (GTD) refers to cases in which gene mutation is necessary but not sufficient to cause disease. In this Perspective, we apply this novel concept to rheumatic diseases, which have been linked to hundreds of genetic variants via association studies. These variants are in the ‘grey zone’ between monogenic variants with large effect sizes and common susceptibility alleles with small effect sizes. Among genes associated with rare autoinflammatory diseases, many low-frequency and/or low-penetrance variants are known to increase susceptibility to systemic inflammation. In autoimmune diseases, hundreds of HLA and non-HLA genetic variants have been revealed to be modest- to moderate-risk alleles. These diseases can be reclassified as GTDs. The same concept could apply to many other human diseases. GTD could improve the reporting of genetic testing results, diagnostic yields, genetic counselling and selection of therapy, as well as facilitating research using a novel approach to human genetic diseases. Beyond the traditional classification of monogenic or complex, many genetic diseases can be considered genetically transitional disease. In this Perspective, the authors consider the application of the genetically transitional disease model to rheumatic diseases and the potential implications for patient care, genetic counselling and research.
{"title":"Genetically transitional disease: conceptual understanding and applicability to rheumatic disease","authors":"Timothy B. Niewold, Ivona Aksentijevich, Peter D. Gorevic, Greg Gibson, Qingping Yao","doi":"10.1038/s41584-024-01086-9","DOIUrl":"10.1038/s41584-024-01086-9","url":null,"abstract":"In genomic medicine, the concept of genetically transitional disease (GTD) refers to cases in which gene mutation is necessary but not sufficient to cause disease. In this Perspective, we apply this novel concept to rheumatic diseases, which have been linked to hundreds of genetic variants via association studies. These variants are in the ‘grey zone’ between monogenic variants with large effect sizes and common susceptibility alleles with small effect sizes. Among genes associated with rare autoinflammatory diseases, many low-frequency and/or low-penetrance variants are known to increase susceptibility to systemic inflammation. In autoimmune diseases, hundreds of HLA and non-HLA genetic variants have been revealed to be modest- to moderate-risk alleles. These diseases can be reclassified as GTDs. The same concept could apply to many other human diseases. GTD could improve the reporting of genetic testing results, diagnostic yields, genetic counselling and selection of therapy, as well as facilitating research using a novel approach to human genetic diseases. Beyond the traditional classification of monogenic or complex, many genetic diseases can be considered genetically transitional disease. In this Perspective, the authors consider the application of the genetically transitional disease model to rheumatic diseases and the potential implications for patient care, genetic counselling and research.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139990642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1038/s41584-024-01084-x
David A. McBride, Ryan M. Jones, Nunzio Bottini, Nisarg J. Shah
Disease-modifying drugs have transformed the treatment options for many systemic autoimmune diseases. However, an evolving understanding of disease mechanisms, which might vary between individuals, is paving the way for the development of novel agents that operate in a patient-tailored manner through immunophenotypic regulation of disease-relevant cells and the microenvironment of affected tissue domains. Immunoengineering is a field that is focused on the application of engineering principles to the modulation of the immune system, and it could enable future personalized and immunoregulatory therapies for rheumatic diseases. An important aspect of immunoengineering is the harnessing of material chemistries to design technologies that span immunologically relevant length scales, to enhance or suppress immune responses by re-balancing effector and regulatory mechanisms in innate or adaptive immunity and rescue abnormalities underlying pathogenic inflammation. These materials are endowed with physicochemical properties that enable features such as localization in immune cells and organs, sustained delivery of immunoregulatory agents, and mimicry of key functions of lymphoid tissue. Immunoengineering applications already exist for disease management, and there is potential for this new discipline to improve disease modification in rheumatology. Immunoengineering involves the design of materials with specific properties relating to the immune system. In this Review the authors consider the application of immunoengineering to systemic autoimmune diseases via site-specific and antigen-specific immunoregulation, the facilitation of immune cell therapy, novel approaches to immunodiagnostics and the generation of models to study autoimmunity.
{"title":"The therapeutic potential of immunoengineering for systemic autoimmunity","authors":"David A. McBride, Ryan M. Jones, Nunzio Bottini, Nisarg J. Shah","doi":"10.1038/s41584-024-01084-x","DOIUrl":"10.1038/s41584-024-01084-x","url":null,"abstract":"Disease-modifying drugs have transformed the treatment options for many systemic autoimmune diseases. However, an evolving understanding of disease mechanisms, which might vary between individuals, is paving the way for the development of novel agents that operate in a patient-tailored manner through immunophenotypic regulation of disease-relevant cells and the microenvironment of affected tissue domains. Immunoengineering is a field that is focused on the application of engineering principles to the modulation of the immune system, and it could enable future personalized and immunoregulatory therapies for rheumatic diseases. An important aspect of immunoengineering is the harnessing of material chemistries to design technologies that span immunologically relevant length scales, to enhance or suppress immune responses by re-balancing effector and regulatory mechanisms in innate or adaptive immunity and rescue abnormalities underlying pathogenic inflammation. These materials are endowed with physicochemical properties that enable features such as localization in immune cells and organs, sustained delivery of immunoregulatory agents, and mimicry of key functions of lymphoid tissue. Immunoengineering applications already exist for disease management, and there is potential for this new discipline to improve disease modification in rheumatology. Immunoengineering involves the design of materials with specific properties relating to the immune system. In this Review the authors consider the application of immunoengineering to systemic autoimmune diseases via site-specific and antigen-specific immunoregulation, the facilitation of immune cell therapy, novel approaches to immunodiagnostics and the generation of models to study autoimmunity.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139931998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1038/s41584-024-01088-7
Mohamad Bittar, Atul Deodhar
The World Health Organization (WHO) has published new guidelines for the non-surgical management of chronic primary low back pain in adults in primary and community care settings. Although the guidelines are commendable, they lack guidance on when to suspect and how to avoid missing important secondary causes of back pain.
{"title":"A critical view of WHO guidelines on management of low back pain","authors":"Mohamad Bittar, Atul Deodhar","doi":"10.1038/s41584-024-01088-7","DOIUrl":"10.1038/s41584-024-01088-7","url":null,"abstract":"The World Health Organization (WHO) has published new guidelines for the non-surgical management of chronic primary low back pain in adults in primary and community care settings. Although the guidelines are commendable, they lack guidance on when to suspect and how to avoid missing important secondary causes of back pain.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139735646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1038/s41584-023-01074-5
Berend C. Stoel, Marius Staring, Monique Reijnierse, Annette H. M. van der Helm-van Mil
Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice. Deep learning is a powerful technique with great potential for the analysis and interpretation of rheumatological images. To successfully use deep learning, rheumatologists should understand the tasks involved in image processing and the potential confounders and limitations that can affect the analysis of clinical data.
{"title":"Deep learning in rheumatological image interpretation","authors":"Berend C. Stoel, Marius Staring, Monique Reijnierse, Annette H. M. van der Helm-van Mil","doi":"10.1038/s41584-023-01074-5","DOIUrl":"10.1038/s41584-023-01074-5","url":null,"abstract":"Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice. Deep learning is a powerful technique with great potential for the analysis and interpretation of rheumatological images. To successfully use deep learning, rheumatologists should understand the tasks involved in image processing and the potential confounders and limitations that can affect the analysis of clinical data.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139707269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1038/s41584-024-01089-6
Robert Phillips
Age-related B cells (ABCs) have pathogenic roles in autoimmune diseases. Research has now identified ZEB2 as the transcription factor that mediates differentiation into ABCs.
老年 B 细胞（ABC）在自身免疫性疾病中具有致病作用。研究发现，ZEB2 是介导分化成 ABC 的转录因子。
{"title":"ZEB2 promotes formation of age-related B cells","authors":"Robert Phillips","doi":"10.1038/s41584-024-01089-6","DOIUrl":"10.1038/s41584-024-01089-6","url":null,"abstract":"Age-related B cells (ABCs) have pathogenic roles in autoimmune diseases. Research has now identified ZEB2 as the transcription factor that mediates differentiation into ABCs.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139707271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1038/s41584-024-01091-y
Sarah Onuora
A meta-analysis of data from six genome-wide association study cohorts implicates several signalling pathways, including Hedgehog and Notch signalling, in Dupuytren disease.
{"title":"GWAS data help unravel Dupuytren disease","authors":"Sarah Onuora","doi":"10.1038/s41584-024-01091-y","DOIUrl":"10.1038/s41584-024-01091-y","url":null,"abstract":"A meta-analysis of data from six genome-wide association study cohorts implicates several signalling pathways, including Hedgehog and Notch signalling, in Dupuytren disease.","PeriodicalId":18810,"journal":{"name":"Nature Reviews Rheumatology","volume":null,"pages":null},"PeriodicalIF":33.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139707270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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