By means of a demand characteristic analysis of high-value consumables in surgical operations,this article established the agile supply chain model of those consumables,and studied its management model as well as the running mechanism based on a series of advanced information technologies,such as case knowledge base,virtual storage,dynamic monitoring,etc.With such a system in place,the hospital realized on-demand and zero-inventory management of high-value consumables to satisfy the dynamic demand for surgical operations.

Objective:To observe the effect of low-expression or over-expression of NUP88 gene on the proliferation and invasion ability of breast cancer cell line BT-20.Methods: NUP88 recombinant adenovirus expression vector and NUP88 RNAi adenovirus vector were transfected into breast cancer BT-20 cells to obtain BT-20 cells over-expressing NUP88 and BT-20 cells lower-expressing NUP88 and then detected the expression of NUP88 mRNA and NUP88 protein.After that,the apoptosis of BT-20 cells was detected by flow cytometry and the invasion and metastasis of BT-20 cells were detected by Transwell invasion assay.The expression of apoptosis protein and invasion and metastasis proteins were detected by Western blot.Results: BT-20 cell with the over expression levels of NUP88 mRNA and NUP88 protein and BT-20 cell with the low expression levels of NUP88 mRNA and NUP88 protein were structured.The over-expression of NUP88 gene led to proliferation rate and the number of invasive cells were significantly higher than BT-20 cells,apoptosis cells were significantly lower than BT-20 cells(P<0.05).However,the low-expression of NUP88 gene led to proliferation rate and the number of invasive cells were significantly lower than BT-20 cells,apoptosis cells was significantly higher than BT-20 cells(P<0.05).The over-expression of NUP88 gene led to Bcl-2 and β-catenin level were significantly higher than that of BT-20 cells,and Bax and E-cadherin level were significantly lower than that of BT-20 cells(P<0.05).However,the low-expression of NUP88 gene led to Bcl-2 and β-catenin level were significantly lower than that of BT-20 cells,and Bax and E-cadherin level were significantly higher than that of BT-20 cells(P<0.05).Conclusion: NUP88 gene regulates the proliferation and invasion and migration ability of breast cancer cells by regulating the expression of Bax,Bcl-2,E-cadherin and β-catenin.It has an important significance in the target treatment of breast cancer.

Objective: To investigate the changes in the composition of intestinal microbiota in mice with acute liver failure and identify characteristic bacteria, and to provide a basis for the diagnosis and treatment of acute liver failure with intestinal microbiota disorders. Methods: A total of 30 specific pathogen-free male BALB/c mice were used in this study, including 25 mice in the model group and 5 mice in the control group. An acute liver failure model was induced by D-galactosamine. Microbial DNA was extracted from intestinal contents in different segments of the lower digestive tract (ileum and colon) and feces and then were amplified using PCR. The regions of 16S V3-V4 were subjected to high-throughput sequencing, followed by bioinformatics analyses, including OTU hierarchical clustering, species annotation, alpha-diversity analysis, and LEfSe (LDA Effect Size) analysis. Comparison of continuous data was made using t-test, while comparison of categorical data was made using χ2 test. Results: A total of 10 mice survived in the two groups, with 80% mortality rate in the model group. The alpha-diversity analysis revealed increased bacterial diversity and abundance in the ileum, increased bacterial diversity and reduced bacterial abundance in the colon, and reduced bacterial diversity and insignificantly changed bacterial abundance in feces in the model group compared with the control group. Based on the optimized classification level, significantly reduced abundance of Clostridiaceae (44.95% ± 19.28% vs 7.51% ± 16.57%, P = 0.011) in the ileum, whereas significantly increased abundance of Rikenellaceae (1.08% ± 1.01% vs 4.18% ± 2.39%, P = 0.028), S24-7 (4.75% ± 4.87% vs 22.77% ± 13.05%, P = 0.020), and F16 (0.24% ± 0.28% vs 2.18% ± 1.61%, P = 0.029) in the colon were found in model group compared with the control group. The LEfSe analysis demonstrated that there were significant differences in Staphylococcaceae and S24-7 between the two groups, and S24-7 could be defined as the characteristic bacteria. Conclusion Intestinal microbiota disorders, especially the excessive growth of microbes in the ileum, are observed in mice with acute liver failure. Moreover, acute liver failure may be closely associated with the excessive growth of S24-7.

Objective To construct risk prediction models of death or readmission in patients with acute heart failure(AHF)during the vulnerable phase based on machine learning algorithms and screen the optimal model.Methods A total of 651 AHF patients with admitted to Department of Cardiology of the Second Affiliated Hospital of Army Medical University from October 2019 to July 2021 were included.The clinical data consisting of admission vital signs,comorbidities and laboratory results were collected from electronic medical records.The composite endpoint was defined as all-cause death or readmission for worsening heart failure within 3 months after discharge.The patients were divided into a training set(521 patients)and a test set(130 patients)in a ratio of 8:2 through the simple random sampling.Six machine learning models were developed,including logistic regression(LR),random forest(RF),decision tree(DT),light gradient boosting machine(LGBM),extreme gradient boosting(XGBoost)and neural networks(NN).Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical benefit of the models.Shapley additive explanation(SHAP)was used to explain and evaluate the effect of different clinical characteristics on the models.Results A total of 651 AHF patients were included,of whom 203 patients(31.2%)died or were readmitted during the vulnerable phase.ROC curve analysis showed that the AUC values of the LR,RF,DT,LGBM,XGBoost and NN model were 0.707,0.756,0.616,0.677,0.768 and 0.681,respectively.The XGBoost model had the highest AUC value.DCA showed that the XGBoost model exhibited greater clinical net benefit compared with other models,with the best predictive performance.SHAP algorithm analysis showed that the clinical features that had the greatest impact on the output of the model were serum uric acid,D-dimer,mean arterial pressure,B-type natriuretic peptide,left atrial diameter,body mass index,and New York Heart Association(NYHA)classification.Conclusion The XGBoost model has the best predictive performance in predicting the risk of death or readmission of AHF patients during the vulnerable phase.