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As one of the core issues of roof cutting pressure relief gob-side entry retaining technology, roof cutting parameters have a great influence on the stability of surrounding rock.Taking the 1226 working face of Shuguang Mine as the engineering background, the reference values of cutting top height and cutting top Angle are 7.1~9.5 m and 11.4° respectively through theoretical analysis.Through numerical simulation, the roof collapse form, roof subsidence and stress distribution of solid coal side are analyzed when the roof cutting height is 4 m, 7 m, 10 m, 13 m and the roof cutting Angle is 0°, 10°, 15°, 20° and 30° respectively from two angles of roof cutting effect and surrounding rock deformation.The results show that the optimal cutting top parameters are the cutting top height 10 m and the cutting top Angle 15°.The optimum hole spacing, blasting method and charge quantity are determined by field blasting test.The surrounding rock deformation of the rear roadway of the working face was monitored after the roadway retaining.The deformation of the surrounding rock of the roadway was small and the effect of the roadway retaining was good.

Background and purpose: Serine/arginine-rich splicing factor 1 (SRSF1) is closely related to the development of tumor. Esophageal carcinoma is the common malignant tumor of digestive system. However, the role of SRSF1 in esophageal carcinoma is rarely reported. This study aimed to detect the expression of SRSF1 in esophageal squamous cell carcinoma (ESCC) and its effects on the proliferation, invasion and migration of ESCC cells, and to explore its mechanism. Methods: Forty pairs of ESCC tissues and paracancerous tissues resected at the Fourth Hospital of Hebei Medical University from January 2020 to December 2021 were collected for this study. The expression of SRSF1 in ESCC was detected by immunohistochemistry. The mRNA expression and protein level of SRSF1 in ESCC cells were detected by real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) and Western blot, respectively. Eca9706 cell with high expression of SRSF1 was selected. The mRNA expression of SRSF1 was reduced by small interfering RNA (siRNA). The proliferation, migration and invasion of Eca9706 cells were detected by cell counting kit-8 (CCK-8), transwell and Matrigel matrix gel assays, respectively. Database was used to analyze the expression of vascular endothelial growth factor A (VEGFA) in ESCC tissues, and the correlation between expressions of SRSF1 and VEGFA was also analyzed. RTFQ-PCR was used to detect the expression levels of VEGFA Iso8a and Iso8b isoform in Eca9706 cells, and the change in VEGFA Iso8a and Iso8b isoform expressions was also detected after knocking down SRSF1. Results: The expression of SRSF1 was significantly higher in ESCC tissues than in normal esophageal tissues (P

Background and Aim: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. Methods: In this population‐based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long‐short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. Results: In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897–0.916), 0.908 (95% CI 0.899–0.918), 0.910 (95% CI 0.901–0.919), and 0.793 (95% CI 0.769–0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. Conclusions: Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients. [ABSTRACT FROM AUTHOR]

Intestinal microbiota is symbiotically associated with host health, learning about the characteristics of microbiota and the factors that modulate it could assist in developing strategies to promote human health and prevent diseases. Polysaccharides from Lycium barbarum (LBPs) are found beneficial for enhancing the activity of gut microbiota, as a potential prebiotic, which not only participates in improving body immunity, obesity, hyperlipidemia and systemic inflammation induced by oxidative stress, but also plays a magnificent role in regulating intestinal microenvironment and improving host health and target intestinal effects via its biological activities, as well as gut microbiota and metabolites. To highlight the internal relationship between intestinal microbiota and LBPs, this review focuses on the latest advances in LBPs on the intestinal microbiota, metabolites, immune regulation, intestinal barrier protection, microbiota-gut-brain axis and host health. Moreover, the preparation, structure, bioactivity and modification of LBPs were also discussed. This review may offer new perspective on LBPs improving health of gut and host via intestinal microbiota, and provide useful guidelines for the application of LBPs in the food industry. [ABSTRACT FROM AUTHOR]