BK virus-associated hemorrhagic cystitis (BKV-HC) is a severe complication after allogeneic hematopoietic stem cell transplantation. So far, no specific antiviral drug with proven efficacy has been approved for treating BKV-HC. Leflunomide is an immunosuppressive drug with antiviral activity and has been used in treating BKV-associated nephropathy after renal transplantation. This is the first report on the efficacy and safety of leflunomide in the treatment of BKV-HC. From January 2006 to January 2009, 89 patients received allogeneic hematopoietic stem cell transplantation, and among them, 18 patients were identified as having BKV-HC, with a 20% cumulative incidence. Fourteen patients were treated with oral leflunomide. Three days of 100 mg/day leflunomide was used as loading doses and followed by maintenance doses of 20 mg/day. The urinary BKV-DNA load was monitored weekly by real-time quantitative PCR. The efficacy was evaluated on day 20 after leflunomide treatment. Seven patients (50%) achieved complete remission, 5 patients (35.7%) achieved partial remission, and 2 patients (14.3%) had more than a 1-log reduction in urinary BKV-DNA loads after treatment. During the leflunomide treatment, the graft-versus-host disease of the patients did not progress, and the dosages of the immunosuppressant were reduced simultaneously. One patient discontinued treatment because of intolerable gastrointestinal symptoms. Neutropenia occurred in 2 cases. These preliminary data suggest that leflunomide may be a potentially effective medication for treating BKV-HC without significant toxicity, but evidence supporting its use requires randomized controlled trials.

Due to the rich physical meaning of aurora morphology, the classification of aurora images is an important task for polar scientific expeditions. However, the traditional classification methods do not make full use of the different features of aurora images, and the dimension of the description features is usually so high that it reduces the efficiency. In this paper, through combining multiple features extracted from aurora images, an aurora image classification method based on multi-feature latent Dirichlet allocation (AI-MFLDA) is proposed. Different types of features, whether local or global, discrete or continuous, can be integrated after being transformed to one-dimensional (1-D) histograms, and the dimension of the description features can be reduced due to using only a few topics to represent the aurora images. In the experiments, according to the classification system provided by the Polar Research Institute of China, a four-class aurora image dataset was tested and three types of features (MeanStd, scale-invariant feature transform (SIFT), and shape-based invariant texture index (SITI)) were utilized. The experimental results showed that, compared to the traditional methods, the proposed AI-MFLDA is able to achieve a better performance with 98.2% average classification accuracy while maintaining a low feature dimension.

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