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To explore the therapeutic efficacy of intracorporeal urinary diversion (ICUD) and extracorporeal urinary diversion (ECUD) after robot-assisted radical cystectomy for bladder cancer through systematic review. This study systematically retrieved electronic databases of PubMed, Web of Science, Cochrane Library, CNKI, and Wanfang to include peer-reviewed studies comparing ICUD and ECUD after robot-assisted radical cystectomy for bladder cancer. Relative risk (RR) and mean difference (MD) were used to represent the pooled effect size and estimate its 95% confidence interval (CI). This study included 15 studies with good quality, involving 5,370 patients undergoing robot-assisted radical cystectomy for bladder cancer. Meta-analysis showed that ICUD reduced intraoperative bleeding volume by 64.12 ml (95% confidence interval [CI]: [−100.95, −27.29]), significantly decreased the risk of blood transfusion (RR: 0.40; 95% CI: [0.24, 0.68]) and gastrointestinal complications (odds ratio [ OR ]: 0.61; 95% CI: [0.47, 0.80]), with shorter postoperative time of exhaust (MD: −9.27; 95% CI: [−18.47, −0.08]) and oral intake (MD: −0.92; 95% CI: [−1.30, −0.54]). However, ICUD had a relatively longer surgical duration (MD: 30.84 min, 95% CI: [5.03, 56.66]). In addition, there was no statistically significant difference concerning the impact of ICUD and ECUD on the length of stay in the hospital (MD: −0.68d; 95% CI: [−1.79, 0.42]), overall complications (30-day: RR: 1.16; 95% CI: [0.93, 1.46]; and 90-day, RR: 0.85; 95% CI: [0.69, 1.04]) and readmission rate (30-day: RR: 0.96; 95% CI: [0.72, 1.27]; and 90-day: RR: 1.15; 95% CI: [0.80, 1.64]). ICUD after robot-assisted laparoscopic radical cystectomy for bladder cancer exhibits obvious positive effects, especially in reducing the risk of blood transfusion and gastrointestinal complications, and shortening postoperative time of exhaust and oral intake. The findings in this meta-analysis should be confirmed by multiple high-quality studies in the future. [ABSTRACT FROM AUTHOR]

Small target detection has become an important part of space exploration missions. The existence of weak illumination and interference from the background of star charts in deep and distant space has brought great challenges to space target detection. In addition, the distance of space targets is usually far, so most of them are small targets in the image, and the detection of small targets is also very difficult. To solve the above problems, we propose a multi-scale feature fusion pyramid network. First, we propose the CST module of a CNN fused with Swin Transformer as the feature extraction module of the feature pyramid network to enhance the extraction of target features. Then, we improve the SE attention mechanism and construct the CSE module to find the attention region in the dense star map background. Finally, we introduce improved spatial pyramid pooling to fuse more features to increase the sensory field to obtain multi-scale object information and improve detection performance for small targets. We provide two versions and conducted a detailed ablation study to empirically validate the effectiveness and efficiency of the design of each component in our network architecture. The experimental results show that our network improved in performance compared to the existing feature pyramid. [ABSTRACT FROM AUTHOR]