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Red music during the Agrarian Revolutionary War was rooted in the leadership of the Communist Party of China and grew up in the revolutionary struggle. Based on a study of the background, style and effect of red music in this period, this paper discusses the role of red music in propagating revolutionary theories, mobilizing the masses and inspiring people during the founding and development of revolutionary bases such as Jinggangshan Mountain, Central Soviet Area and Shaanxi-Gansu Border.

Background: This study aimed to evaluate the clinical efficacy, safety, and feasibility of performing endoscopic submucosal resection with a ligation device (ESMR-L) after apical mucosal incision (AMI) for the treatment of gastric subepithelial tumors originating from the muscularis propria (SET-MPs).Methods: 14 patients with gastric SET-MPs were treated by ESMR-L with AMI between December 2016 and May 2017. The complete resection rate, operation duration, and postoperative complications were collected. All patients were followed for 2 - 6 months.Results: The complete resection rate was 100 %, the mean tumor size was 10.71 ± 3.45 mm (7 - 18 mm), and the median operative time was 18.5 minutes. Perforation occurred in four patients, with all lesions being completely repaired endoscopically. No delayed bleeding or peritoneal signs were observed. No residual lesions or recurrence were found during the follow-up period.Conclusions: AMI with ESMR-L appears to be an efficient and simple method for the histological diagnosis of gastric SET-MPs, but it carries a high perforation rate and cannot guarantee cure. [ABSTRACT FROM AUTHOR]

Introduction: Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, existing detection methods for COVID-19 severity assessment often lack consideration of organs and tissues other than the lungs, which limits the accuracy and reliability of these predictive models., Material and Methods: The retrospective study included data from 515 COVID-19 patients (Cohort 1, n=415; Cohort 2, n=100) from two centers (Shanghai Public Health Center and Brazil Niteroi Hospital) between January 2020 and July 2020. Firstly, a three-stage EAT segmentation method was proposed by combining object detection and segmentation networks. Lung and EAT radiomics features were then extracted, and feature selection was performed. Finally, a hybrid model, based on seven machine learning models, was built for detecting COVID-19 severity. The hybrid model's performance and uncertainty were evaluated in both internal and external validation cohorts., Results: For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) of the hybrid model increased by 0.09 (p<0.001), 19.3 % (p<0.05), and 18.0 % (p<0.05) in the internal validation cohort, and by 0.06 (p<0.001), 18.0 % (p<0.05) and 18.0 % (p<0.05) in the external validation cohort, respectively. Uncertainty and radiomics features analysis confirmed the interpretability of increased certainty in case prediction after inclusion of EAT features., Conclusion: This study proposed a novel three-stage EAT extraction method. We demonstrated that adding EAT radiomics features to a COVID-19 severity detection model results in increased accuracy and reduced uncertainty. The value of these features was also confirmed through feature importance ranking and visualization.