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Background Pradefovir is a liver-targeted prodrug of adefovir, a nucleoside/nucleotide analogue with antiviral activity against hepatitis B virus (HBV) DNA polymerase. This phase 2 study compared the efficacy and safety of oral pradefovir (30, 45, 60, or 75 mg) versus tenofovir disoproxil fumarate (TDF; 300 mg) and aimed to identify the most appropriate dose of pradefovir for the forthcoming phase 3 study. Methods Treatment-naive and experienced (not on treatment >6 months) patients with chronic hepatitis B were eligible. Results A total of 240 participants were randomized and treated in the study (48 per group). Approximately 80% were hepatitis B e antigen (HBeAg) positive, and 10% had liver cirrhosis. The reductions from baseline in HBV DNA levels achieved at week 24 were 5.40, 5.34, 5.33, and 5.40 log10 IU/mL, with pradefovir doses of 30-, 45-, 60-, and 75-mg, respectively, compared with 5.12 log10 IU/mL with TDF. However, HBeAg loss was attained by more participants who received 45-, 60-, or 75-mg pradefovir than by those receiving TDF (12%, 6%, and 9% vs 3%). The TDF group exhibited a more significant increase in serum creatinine than the pradefovir 30- and 45-mg groups, and serum phosphate levels were comparable among all groups. Most adverse events (AEs) were mild (grade 1). No treatment-related severe AEs were reported. Overall, AEs and laboratory abnormalities were comparable to those in the TDF group. Conclusions Pradefovir and TDF exhibited comparable reductions in HBV DNA levels. All treatments were safe and well tolerated. Clinical Trials registration NCT00230503 and China Drug Trials CTR2018042 [ABSTRACT FROM AUTHOR]

This article provides an overview of assisted reproductive technology (ART) in China. It highlights the rapid development of ART in China over the past 30 years, with over 1 million ART cycles and 300,000 ART babies born annually. The article also discusses the challenges faced by reproductive medicine in China, such as the aging population and increasing infertility rates. It presents several studies conducted in China on topics such as polycystic ovary syndrome, luteal phase deficiency, cervical cancer, chromosome aberrations, and premature ovarian insufficiency. The article concludes by expressing gratitude to the authors, reviewers, and editors for their contributions to reproductive medicine in China. [Extracted from the article]

Since graph learning could preserve the structure information of the samples to improve the learning ability, it has been widely applied in both shallow learning and deep learning. However, the current graph learning methods still suffer from the issues such as outlier influence and model robustness. In this paper, we propose a new dynamic graph neural network (DGCN) method to conduct semi-supervised classification on multi-view data by jointly conducting the graph learning and the classification task in a unified framework. Specifically, our method investigates three strategies to improve the quality of the graph before feeding it into the GCN model: (i) employing robust statistics to consider the sample importance for reducing the outlier influence, i.e. assigning every sample with soft weights so that the important samples are with large weights and outliers are with small or even zero weights; (ii) learning the common representation across all views to improve the quality of the graph for every view; and (iii) learning the complementary information from all initial graphs on multi-view data to further improve the learning of the graph for every view. As a result, each of the strategies could improve the robustness of the DGCN model. Moreover, they are complementary for reducing outlier influence from different aspects, i.e. the sample importance reduces the weights of the outliers, both the common representation and the complementary information improve the quality of the graph for every view. Experimental result on real data sets demonstrates the effectiveness of our method, compared to the comparison methods, in terms of multi-class classification performance. [ABSTRACT FROM AUTHOR]