During the 12th International Workshop on Autoantibodies and Autoimmunity held in Sao Paulo, Brazil, on August 28, 2014, a full day session was devoted to establishing a consensus on the nomenclature of staining patterns observed in the antinuclear antibody (ANA) indirect immunofluorescence test on HEp-2 cells. The current report summarizes the collective agreements with input from the host Brazilian and international communities that represented research, clinical, and diagnostic service laboratories. Patterns are categorized in three major groups (nuclear, cytoplasmic, and mitotic patterns) and each pattern has been defined and described in detail. The consensus nomenclature and representative patterns are made available online at the international consensus on antinuclear antibody pattern (ICAP) website (www.ANApatterns.org). To facilitate continuous improvement and input, specific comments on ICAP are encouraged and these will be discussed in subsequent ICAP meetings. The ultimate goal with the establishment of the ICAP is to promote harmonization and understanding of autoantibody test nomenclature, as well as interpretation guidelines for ANA testing, thereby optimizing usage in patient care.
Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that 1) the proposed framework can effectively outperform existing models by properly applying data augmentation, 2) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.
Since 1st October 2019, new CCC certification rules have been in place for Ex products sold in China; these replace the previous National Production License System (NPLS). A transition period of one year was provided. By 1st October 2020, most Ex products sold in China must be covered by a new CCC Ex product certification and mark.