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Introduction:Acquired hemophilia A (AHA) is a rare bleeding disease caused by autoantibodies against factor VIII (FVIII). Spontaneous bleeding symptoms usually affect the skin, musco, muscle, and internal organs, while joint hemarthrosis in AHA is an extremely rare manifestation. AHA may have an autoimmune cause and is often associated with autoimmune disease, but no demonstrable platelet impairment was found in AHA patients. We report a patient with AHA complicated with a right shoulder joint hemarthrosis and immune thrombocytopenia. The patient was treated with fresh frozen plasma (FFP) and human prothrombin complex concentrate (hPCC) to control the active bleeding. Simultaneously this patient firstly accepted cyclophosphamide combined with prednisone to eradicate the inhibitor, while the treatment effect of cyclophosphamide combined with prednisone was not satisfactory. At last, she was successfully treated through the use of an anti-CD20 monoclonal antibody.Conclusion:AHA is an autoimmune disease and can co-exist with immune cytopenia besides connective tissue disease (CTD). Joint hemarthrosis is not specific to congenital hemophilia and mainly related to the extent of prolonged aPTT and weight loading of joint in AHA. When the first-line therapy of cyclophosphamide combined with prednisone is not enough to eradicate the inhibitor, especially for a higher inhibitor titer, anti-CD20 monoclonal antibody could play an important role.

The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover the disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. The method, which is called “RW-RVM,” is a combination of Random Walk (RW) and Relevance Vector Machines (RVM). RW was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple RVMs were applied to identify hyperthyroidism-related genes. The result of 10-cross validation shows that the area under the receiver operating characteristic curve (AUC) of our method reached 0.9, and area under the precision-recall curve (AUPR) was 0.87. Seventy-eight novel genes were found to be related to hyperthyroidism. We investigated two genes of these novel genes with existing literature, which proved the accuracy of our result and method.