Nature Reviews Rheumatology最新文献_第10页

Eosinophils regulate bone remodelling 嗜酸性粒细胞调节骨骼重塑

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-03-07 DOI: 10.1038/s41584-024-01100-0

Sarah Onuora

引用次数: 0

APOE in fat pad and synovium contributes to knee OA 脂肪垫和滑膜中的 APOE 是导致膝关节 OA 的原因之一。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-03-07 DOI: 10.1038/s41584-024-01099-4

Robert Phillips

引用次数: 0

Immunogenic Xist ribonucleoproteins drive sex-biased autoimmunity 具有免疫原性的 Xist 核糖核蛋白可驱动具有性别差异的自身免疫。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-03-07 DOI: 10.1038/s41584-024-01101-z

Jessica McHugh

引用次数: 0

Genetically transitional disease: conceptual understanding and applicability to rheumatic disease 基因过渡性疾病：对风湿病的概念理解和适用性。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-02-28 DOI: 10.1038/s41584-024-01086-9

Timothy B. Niewold, Ivona Aksentijevich, Peter D. Gorevic, Greg Gibson, Qingping Yao

{"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}

引用次数: 0

Targeting NP cell senescence in IVDD 针对 IVDD 中的 NP 细胞衰老

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-02-26 DOI: 10.1038/s41584-024-01095-8

Rebecca Kelsey

引用次数: 0

The therapeutic potential of immunoengineering for systemic autoimmunity 免疫工程对系统性自身免疫的治疗潜力。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-02-21 DOI: 10.1038/s41584-024-01084-x

David A. McBride, Ryan M. Jones, Nunzio Bottini, Nisarg J. Shah

{"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}

引用次数: 0

A critical view of WHO guidelines on management of low back pain 对世界卫生组织腰背痛管理指南的批判性看法。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-02-14 DOI: 10.1038/s41584-024-01088-7

Mohamad Bittar, Atul Deodhar

引用次数: 0

Deep learning in rheumatological image interpretation 风湿病图像解读中的深度学习。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-02-08 DOI: 10.1038/s41584-023-01074-5

Berend C. Stoel, Marius Staring, Monique Reijnierse, Annette H. M. van der Helm-van Mil

{"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}

引用次数: 0

ZEB2 promotes formation of age-related B cells ZEB2 可促进老年 B 细胞的形成。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-02-08 DOI: 10.1038/s41584-024-01089-6

Robert Phillips

引用次数: 0

GWAS data help unravel Dupuytren disease GWAS 数据有助于揭示杜普伊特伦病。

IF 33.7 1区医学

Nature Reviews Rheumatology Pub Date : 2024-02-08 DOI: 10.1038/s41584-024-01091-y

Sarah Onuora

引用次数: 0