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https://www.selleckchem.com/products/blu-451.html This study aims to estimate the mental distress prevalence of Chinese postgraduate students and the association with the social changes based on the data between 2000 and 2019. This is a cross-temporal meta-analysis study. The literature was retrieved with both English and Chinese electronic databases, including articles published from 2002 to 2019. Statistical analyses were performed with R 3.6.1 and SPSS 22. Eighty-nine primary studies with 99 reports were included in our meta-analysis, totaling 54,722 Chinese postgraduate students. The result showed that (a) the prevalence of mental distress was 28% (95% confidence interval [CI] 25%-31%), and the prevalence of moderately positive symptoms was 9% (95% CI 7%-11%); (b) the prevalence of positive symptoms was negatively correlated with the years of data collection and the prevalence of mental distress decreased by at least 16% from 2000 to 2019; and (c) social changes, particularly the policies of mental health and the educational environment had a significant contribution to these changes. More than a quarter of postgraduate students have mental illness in China, whereas the prevalence of their mental distress has been decreasing. Social changes are shown to play an important role in contributing to this change. More than a quarter of postgraduate students have mental illness in China, whereas the prevalence of their mental distress has been decreasing. Social changes are shown to play an important role in contributing to this change.Latent factor modeling applied to single-cell RNA sequencing (scRNA-seq) data is a useful approach to discover gene signatures. However, it is often unclear what methods are best suited for specific tasks and how latent factors should be interpreted. Here, we compare four state-of-the-art methods and propose an approach to assign derived latent factors to pathway activities and specific cell subsets. By applying this framework to scRNA-s

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