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BackgroundLeft-behind children (LBC) refer to those who have been separated from at least one parent for six months or more due to parental migration for work. This phenomenon poses a significant threat to the mental health of over 61 million LBC in China. This study aims to compare the prevalence of mental health symptoms between LBC and non-left-behind children (non-LBC) and to explore the predictive effect of bullying victimization on adolescent mental health problems.MethodsIn 2019, we conducted a cross-sectional analysis involving 28,036 children and adolescents in Mianyang City, Sichuan Province, China, with ages ranging from 8 to 19 years. Mental health symptoms were assessed using the 7-item Generalized Anxiety Disorder (GAD-7) scale and the 9-item Patient Health Questionnaire (PHQ-9). The Delaware Bullying Victimization Scale-Student (DBVS-S) was employed to gather data on experiences of bullying victimization. Information on self-injury was collected by inquiring whether participants had engaged in self-injurious behavior and the reasons for such behavior. Multivariable logistic regression was utilized to analyze the risk and protective factors associated with mental health symptoms, with a particular focus on different types of bullying victimization.ResultsCompared to non-left-behind children (non-LBC), left-behind children (LBC) exhibited a higher prevalence of mental health issues: anxiety symptoms (24.0% vs. 18.0%, p

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.

BackgroundIncreasing evidence indicated a clear association between COVID-19 pandemic and mental health. This study aimed to assess the dynamic change of mental burden during and after the COVID-19 outbreak and related predictive factors among Chinese undergraduate medical students.MethodsThis longitudinal survey was conducted among Chinese undergraduate medical students before, during, and after the COVID-19 outbreak. We focused on COVID-19 related mental burdens including psychological distress, stress reaction, and insomnia symptoms, and defined the sum score of the three specific mental burden indexes as the overall mental burden index. The prevalence of specific and overall mental burdens and their changing patterns at two phases of the pandemic (during vs. after the COVID-19 outbreak) were measured. In addition, multinomial logistic regressions were used to assess the associations between the psychosocial status before the pandemic and specific and overall mental burden changing patterns.ResultsOur findings showed that the prevalence of overall mental burden increased (from 27.46 to 37.28%) after the COVID-19 outbreak among the 863 Chinese undergraduate medical students who participated in the surveys at baseline, during, and after the COVID-19 outbreak. Specifically, the prevalence of stress reaction symptoms decreased (from 10.90 to 3.60%), while the rates of psychological distress (from 28.06 to 37.95%) and insomnia symptoms (from 12.54 to 20.71%) increased. Participants, with obsessive-compulsive symptoms, somatic symptoms, internet addiction, childhood adversity, stressful life events, and being neurotic were found to have a higher risk of developing mental burden in at least one survey (during or after the COVID-19 outbreak). Healthy family function and being extravert were found to positively impact mental burden.ConclusionPsychological distress, stress reaction and insomnia symptoms have been prevalent among Chinese undergraduate medical students during the COVID-19 outbreak, and the prevalence of overall mental burden increased after the COVID-19 outbreak. Some students, especially those with the risk factors noted above, exhibited persistent or progression symptoms. Continued mental health care was in demand for them even after the COVID-19 outbreak.