Irure-Ventura Juan, López-Hoyos Marcos
Immunology Department, University Hospital Marqués de Valdecilla, 39008 Santander, Spain.
Autoimmunity and Transplantation Research Group, Research Institute "Marqués de Valdecilla" (IDIVAL), 39011 Santander, Spain.
Diagnostics (Basel). 2022 Mar 7;12(3):647. doi: 10.3390/diagnostics12030647.
Autoantibodies are a hallmark of autoimmunity and, specifically, antinuclear antibodies (ANAs) are the most relevant autoantibodies present in systemic autoimmune rheumatic diseases (SARDs). Over the years, different methods from LE cell to HEp-2 indirect immunofluorescence (IIF), solid-phase assays (SPAs), and finally multianalyte technologies have been developed to study ANA-associated SARDs. All of them provide complementary information that is important to provide the most clinically valuable information. The identification of new biomarkers together with multianalyte platforms will help close the so-called "seronegative gap" and to correctly classify and diagnose patients with SARDs. Finally, artificial intelligence and machine learning is an area still to be exploited but in a next future will help to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management.
自身抗体是自身免疫的标志,具体而言,抗核抗体(ANA)是系统性自身免疫性风湿病(SARDs)中最相关的自身抗体。多年来,已经开发了从LE细胞到HEp-2间接免疫荧光(IIF)、固相分析(SPA),以及最后的多分析物技术等不同方法来研究与ANA相关的SARDs。所有这些方法都提供了补充信息,这些信息对于提供最具临床价值的信息很重要。新生物标志物的识别以及多分析物平台将有助于缩小所谓的“血清阴性差距”,并正确分类和诊断SARDs患者。最后,人工智能和机器学习是一个仍有待开发的领域,但在不久的将来将有助于提取患者数据中的模式,并利用这些模式预测患者的预后,以改善临床管理。