Abstract：
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.
摘要：
自身抗体是自身免疫的标志,具体来说,抗核抗体（ANAs）是全身性自身免疫性风湿性疾病（SARDs）中存在的最相关的自身抗体。多年来,从LE细胞到HEp-2间接免疫荧光（IIF）的不同方法，固相测定（SPA），最后开发了多分析物技术来研究与ANA相关的SARD。所有这些都提供了补充信息，这对于提供最有临床价值的信息很重要。新的生物标志物与多分析物平台的识别将有助于关闭所谓的“血清阴性间隙”，并正确分类和诊断SARD患者。最后,人工智能和机器学习是一个仍有待开发的领域，但在未来的未来将有助于提取患者数据中的模式，并利用这些模式来预测患者的预后，以改善临床管理。
