Background: Given the serious consequences of depression and the lack of information about it during the crucially developmental period from the National College Entrance Exam (CEE, i.e., Chinesegaokao) to college, this study aimed to estimate the cumulative incidence, prevalence, age of onset, correlates, and service use of depressive disorders (DDs) among youth who passed the CEE and were enrolled at Hunan Normal University in China.Methods: A two-stage cross-sectional epidemiological survey of DDs was conducted from October to December, 2017 among 6,922 incoming college students (98.5% effective response, N = 6,818, 71.4% female, age range: 16–25 years, mean age = 18.6). Using a stratified sampling method based on the risk of depression, 926 participants (mean age = 18.5, 75.2% female) were selected and subsequently interviewed with the Kiddie–Schedule for Affective Disorders and Schizophrenia–Present and lifetime version (K-SADS-PL).Results: The sex-adjusted 9-month (i.e., 3 months pre-CEE, 3 months after CEE, and 3 months post-matriculation) incidence of new-onset DDs was 2.3% (standard error [S.E.] 0.3%), and the sex-adjusted 1-month, 6-month and lifetime prevalence were 0.7 (S.E. 0.3%), 1.7 (S.E. 0.2%) and 7.5% (S.E. 1.3%), respectively. The median age of onset was 17 (interquartile range: 16–18) years. Critically, over one-third (36.5%, S.E. 0.6) of depressed youth had their new onset during the 9-month period. The risk factors for depression included having mothers with higher education, experiencing major life events, being female, and experiencing parental divorce or death. The adjusted lifetime treatment rate was 8.7%.Conclusion: The 9-month incidence of new-onset depression fromgaokaoto college among the youth sample in China is similar to the global annual incidence (3.0%), but the 1-month and lifetime prevalence are significantly lower than the global point (7.2%) and lifetime prevalence (19%). These findings suggest a high proportion of new-onset depression during the CEE to college among the sample youth in China. The risk of depression is associated with familial and stress correlates. Low treatment is a serious concern. Emphasis on early prevention and available treatment for adolescent and young adult depression is a critical need in China.

Foundation models like the Segment Anything Model (SAM) have shown promising performance in general image segmentation tasks. However, their effectiveness is limited when applied to pathology images due to the inherent multi-scale structural complexity and staining heterogeneity. To address these challenges, we introduce SegAnyPath, a foundational model specifically designed for pathology image segmentation. SegAnyPath is trained on an extensive public pathology dataset comprising over 1.5 million images and 3.5 million masks. We propose a multi-scale proxy task to handle the diverse resolutions in pathology images, complementing the reconstruction objective in the supervised learning stage. To enhance segmentation performance across stain variations, we introduce a novel self-distillation scheme based on stain augmentations. Furthermore, we propose an innovative task-guided Mixture of Experts (MoE) architecture in the decoder of SegAnyPath for efficient management of distinct pathology segmentation tasks, including cell, tissue, and tumor segmentation. Experimental results demonstrate SegAnyPath’s zero-shot generalization capability, achieving a Dice score of 0.6797 across multiple datasets and organs while maintaining consistent performance across varying staining styles and resolutions. In comparison, the fine-tuned SAM achieves a Dice score of only 0.5258 on the same external test sets, indicating a substantial 29.27% improvement by SegAnyPath. SegAnyPath has the potential to advance the field of pathology analysis and improve diagnostic accuracy in clinical settings. The code is available at https://github.com/wagnchogn/SegAnyPath.

The wide prevalence of staining variations in digital pathology presents a significant obstacle, often undermining the effectiveness of diagnosis and analysis. The current strategies to counteract this issue primarily revolve around Stain Normalization (SN) and Stain Augmentation (SA). Nonetheless, these methodologies come with inherent limitations. They struggle to adapt to the vast array of staining styles, tend to presuppose linear associations between color spaces, and often lead to unrealistic color transformations. In response to these challenges, we introduce RandStainNA++, a novel method seamlessly integrating SN and SA. This method exploits the versatility of random SN and SA within randomly selected color spaces, effectively managing variations for the foreground and background independently. By refining the transformations of staining styles for the foreground and background within a realistic scope, this strategy promotes the generation of more practical staining transformations during the training phase. Further enhancing our approach, we propose a unique self-distillation method. This technique incorporates prior knowledge of stain variation, substantially augmenting the generalization capability of the network. The striking results yield that, compared to conventional classification models, our method boosts performance by a significant margin of 16-25%. Furthermore, when juxtaposed with baseline segmentation models, the Dice score registers an increase of 0.06. The codes are available at https://github.com/wagnchogn/RandStainNA-plusplus .

Chinese hamster ovary (CHO) cells are commonly used to produce recombinant therapeutic proteins (RTPs). The yield of RTPs in CHO cells has been greatly improved through cell editing and optimization of culture media, cell culture processes, and expression vectors. However, the heterogeneity of cell clones and product aggregation considerably affect the yield and quality of RTPs. Recently, novel technologies such as semi-targeted and site-specific transgene integration, endoplasmic reticulum-residents, and cell culture process optimization have been used to address these issues. In this review, novel developments in the field of CHO cell expression system heterogeneity are summarized. Moreover, the advantages and limitations of the new strategies are discussed, and important methods for the control of RTP quality are outlined.