Targeting tumorous Circ-E-Cadherinencoded C-E-Cad inhibits the recruitment and function of breast cancer-associated myeloid-derived suppressor cells.

We previously demonstrated that the C-E-cad protein encoded by circ-E-cadherin promotes the self-renewal of glioma stem cells. The expression pattern of C-E-cad in breast cancer and its potential function in the tumor microenvironment are unclear. The expression of circ-E-cadherin and C-E-cad was detected in breast cancer specimens. The influence of C-E-cad expression on MDSCs was assessed using FACS and in vivo tumorigenesis experiments. The synergistic effect of anti-C-E-cad and anti-PD-1 antibodies was validated in vivo. circ-E-cadherin and the encoded protein C-E-cad were found to be upregulated in breast cancer vs. normal samples. C-E-cad promotes the recruitment of MDSCs, especially PMN-MDSCs. C-E-cad activates EGFR signaling in tumor cells and promotes the transcription of CXCL8; moreover, C-E-cad binds to MDSCs and maintains glycolysis in PMN-MDSCs. Targeting C-E-cad enhanced anti-PD-1 efficiency. Our data suggested that C-E-cad is markedly overexpressed in breast cancer and promotes MDSC recruitment and survival. Targeting C-E-cad increases the efficacy of immune checkpoint inhibitor therapy.

Peer victimization and non-suicidal self-injury among high school students: the mediating role of social anxiety, mobile phone addiction, and sex differences.

BACKGROUND: Peer victimization (PV) is one of the major causes of non-suicidal self-injury. Non-suicidal self-injury (NSSI), peer victimization, social anxiety, and mobile phone addiction are significantly related; however, the interaction mechanism and effect of sex differences remain to be determined. OBJECTIVE: Herein, we investigated the relationship between peer victimization and NSSI among Chinese high school students. We also explored the chain mediating roles of social anxiety and mobile phone addiction and the regulatory role of sex. The findings of this study provide insights for theoretical interventions based on internal mechanisms. METHOD: A self-reported survey of 14,666 high school students from Sichuan County was conducted using a peer victimization scale, NSSI scale, social anxiety scale, and mobile phone addiction scale. A self-administered questionnaire was used to capture sociodemographic information. RESULTS: Peer victimization, social anxiety, and mobile phone addiction were positively correlated with NSSI. Peer victimization had significant direct predictive effects on NSSI (95% CI: 0.341, 0.385) and significant indirect predictive effects on NSSI through social anxiety (95% CI: 0.008, 0.019) or mobile phone addiction (95% CI: 0.036, 0.053). Peer victimization had significant indirect predictive effects on NSSI through social anxiety as well as mobile phone addiction (95% CI: 0.009, 0.014). The first stage (predicting the effect of peer victimization on NSSI) and the third stage (predicting the effect of mobile phone addiction on NSSI) were both moderated by sex. CONCLUSIONS: Peer victimization could directly predict NSSI and indirectly predict NSSI through social anxiety and mobile phone addiction. Thus, social anxiety and mobile phone addiction exhibited chain mediating effects between peer victimization and NSSI in high school students; moreover, sex might be involved in the regulation of the mediation process.

YOLOv5-Atn: An Algorithm for Residual Film Detection in Farmland Combined with an Attention Mechanism.

The application of mulching film has significantly contributed to improving agricultural output and benefits, but residual film has caused severe impacts on agricultural production and the environment. In order to realize the accurate recycling of agricultural residual film, the detection of residual film is the first problem to be solved. The difference in color and texture between residual film and bare soil is not obvious, and residual film is of various sizes and morphologies. To solve these problems, the paper proposes a method for detecting residual film in agricultural fields that uses the attention mechanism. First, a two-stage pre-training approach with strengthened memory is proposed to enable the model to better understand the residual film features with limited data. Second, a multi-scale feature fusion module with adaptive weights is proposed to enhance the recognition of small targets of residual film by using attention. Finally, an inter-feature cross-attention mechanism that can realize full interaction between shallow and deep feature information to reduce the useless noise extracted from residual film images is designed. The experimental results on a self-made residual film dataset show that the improved model improves precision, recall, and mAP by 5.39%, 2.02%, and 3.95%, respectively, compared with the original model, and it also outperforms other recent detection models. The method provides strong technical support for accurately identifying farmland residual film and has the potential to be applied to mechanical equipment for the recycling of residual film.