Clinical performance of AMA-M2, anti-gp210 and anti-sp100 antibody levels in primary biliary cholangitis: When detected by multiplex bead-based flow fluorescent immunoassay.

BACKGROUND AND AIM: Primary biliary cholangitis (PBC) is a chronic autoimmune cholangiopathy, characterized by the presence of some autoantibodies in the serum. This study aimed to evaluate the clinical significance of AMA-M2, anti-gp210 and anti-sp100 antibody levels detected by multiplex bead-based flow fluorescent immunoassay (MBFFI) in PBC. METHODS: This study cohort included 238 PBC patients, 81 autoimmune hepatitis (AIH) patients, 62 systemic lupus erythematosus (SLE) patients, and 118 healthy controls. Serum AMA-M2, anti-gp210 and anti-sp100 antibody were detected by MBFFI and immunoblotting assay (IBT). The relationship between three antibody levels and cirrhosis, liver function, cholestasis markers and therapeutic effect to ursodesoxycholic acid (UDCA) was evaluated in PBC. RESULTS: MBFFI were presented good coincidence rate (87.39%-95.38%) with IBT. The level of AMA-M2, anti-gp210 and anti-sp100 antibodies in PBC patients were higher than other disease group and healthy controls (p ＜ .01). When compared with the healthy controls group, the AUC of AMA-M2, anti-gp210 and anti-sp100 antibodies were 0.9245, 0.7619, and 0.6789, respectively. In addition, gp210 antibody levels have diagnostic value in patients with liver cirrhosis (AUC: 0.7567). We found that when combine detect these three antibodies, the sensitivity was higher than individually detection. High level of serum anti-gp210 antibody could be related to worse liver function and more severe cholestasis in PBC patients. Moreover, serum antibody levels may decrease or remained flat in patients who responded well to UDCA. CONCLUSION: The detection of AMA-M2, anti-gp210 and anti-sp100 antibody levels by MBFFI showed good performance in the diagnosis of PBC. Serum anti-gp210 antibody level is related to cirrhosis, poor liver function and severe cholestasis in PBC.

Biomarkers and biologics related with psoriasis and psoriatic arthritis.

Over the past half century, psoriasis is considered as an immune-mediated inflammatory skin disease with the combined hallmarks of autoimmunity and autoinflammation, according to growing volumes of clinical and experimental findings. There is currently no cure for psoriasis, current treatment strategies focus on symptom control, disease minimization, and patient's quality of life enhancement. To meet these challenges, it keeps imperative to discover potential biomarkers, so that not only can they be used for the prediction and monitoring of psoriasis disease in clinic, but also can provide novel therapeutic targets or treatment strategies for psoriasis sufferers. This review systematically demonstrates the research progress of psoriasis-related biomarkers and elaborates their related mechanisms in the pathological development of psoriasis and psoriatic arthritis. In addition, we summarize the development of biologic therapies for psoriasis and psoriatic arthritis in order to drive the broader discussion of psoriasis as an autoimmune-mediated inflammatory skin disease.

One shot ancient character recognition with siamese similarity network.

Ancient character recognition is not only important for the study and understanding of ancient history but also has a profound impact on the inheritance and development of national culture. In order to reduce the study of difficult professional knowledge of ancient characters, and meanwhile overcome the lack of data, class imbalance, diversification of glyphs, and open set recognition problems in ancient characters, we propose a Siamese similarity network based on a similarity learning method to directly learn input similarity and then apply the trained model to establish one shot classification task for recognition. Multi-scale fusion backbone structure and embedded structure are proposed in the network to improve the model's ability to extract features. We also propose the soft similarity contrast loss function for the first time, which ensures the optimization of similar images with higher similarity and different classes of images with greater differences while reducing the over-optimization of back-propagation leading to model overfitting. Specially, we propose a cumulative class prototype based on our network to solve the deviation problem of the mean class prototype and obtain a good class representation. Since new ancient characters can still be found in reality, our model has the ability to reject unknown categories while identifying new ones. A large number of experiments show that our proposed method has achieved high-efficiency discriminative performance and obtained the best performance over the methods of traditional deep learning and other classic one-shot learning.