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The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.
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Objective:To construct a prediction model of pancreatic fistula after pancreaticoduodenectomy and explore its application value.Methods:The retrospective case-control study was conducted. The clinicopathological data of 285 patients with periampullary diseases who underwent pancreaticoduodenectomy in the the First Affiliated Hospital of Harbin Medical University from January 2015 to September 2018 were collected. There were 183 males and 102 females, aged (56±14)years, with a range from 12 to 84 years. According to the random numbers showed in the computer, patients were randomly divided into training dataset consisting of 214 patients and validation dataset consisting of 71 patients, with a ratio of 3∶1. The training dataset was used to construct prediction model, and the validation dataset was used to evaluate performance of prediction model. Observation indicators: (1) incidence of postoperative pancreatic fistula; (2) construction of prediction model of pancreatic fistula after pancreaticoduodenectomy; (3) validation of prediction model of pancreatic fistula after pancreaticoduodenectomy. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed by the t test. Measurement data with skewed distribution were represented as M (range), and comparison between groups was analyzed using the Mann-Whitney rank sum test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Univariate analysis and multivariate analysis were conducted using the Logistic regression model. The accuracy of prediction model was analyzed by drawing receiver operating characteristic curve and calculating area under curve (AUC). Results:(1) Incidence of postoperative pancreatic fistula: of 214 patients in the training dataset, 45 patients had postoperative pancreatic fistula, including 39 of grade B and 6 of grade C, respectively. (2) Construction of prediction model of pancreatic fistula after pancreaticoduodenectomy. Results of univariate analysis showed that body mass index(BMI), diameter of the main pancreatic duct on computed tomography (CT) scan, diameter of the main pancreatic duct by intraoperative exploration, pancreas texture, and level of amylase in ascites at the postoperative first day were related factors for pancreatic fistula after pancreaticoduodenectomy ( χ2=32.450, 15.789, 19.577, 4.559, Z=-7.962, P<0.05). Results of multivariate analysis showed that BMI>25 kg/m 2, diameter of the main pancreatic duct by intraoperative exploration <3 mm and level of amylase in ascites at the postoperative first day >2 651U/L were independent risk factors for pancreatic fistula after pancreaticoduodenectomy ( odds ratio=0.148, 4.286, 0.086, 95% confidence interval: 0.058-0.376, 1.736-10.580, 0.032-0.231, P<0.05). Based on results of multivariate analysis, a prediction model of pancreatic fistula after pancreaticoduodenectomy was built: the predicted value of pancreatic fistula=Exp[0.452-1.914(BMI)+ 1.455(diameter of the main pancreatic duct by intraoperative exploration)-2.451(level of amylase in ascites at the postoperative first day)]/1+ Exp[0.452-1.914(BMI)+ 1.455(diameter of the main pancreatic duct by intraoperative exploration)-2.451(level of amylase in ascites at the postoperative first day)]. The model had the AUC of 0.888 (95% confidence interval : 0.832-0.943, P<0.05). (3) Validation of prediction model of pancreatic fistula after pancreaticoduodenectomy: in the validation dataset, the prediction model of pancreatic fistula after pancreaticoduodenectomy had the AUC of 0.868 (95% confidence interval: 0.780-0.957, P<0.05). There was no significant difference in the AUC between the training dataset and validation dataset ( Z=0.514, P>0.05). Conclusions:BMI>25 kg/m 2, diameter of the main pancreatic duct by intraoperative exploration <3 mm and level of amylase in ascites at the postoperative first day >2 651 U/L are independent risk factors for pancreatic fistula after pancreaticoduodenectomy. Construction of a prediction model of pancreatic fistula after pancreaticoduo-denectomy can effectively predict the risks of postoperative pancreatic fistula.
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Objective To investigate the effects of capsaicin on the invasion ability of human large cell carcinoma NCI-H460 and the expressions of E-cadherin and Snail;To discuss the possible mechanisms of anti-non-small cell lung cancer.Methods NCI-H460 cells were cultured in vitro and treated with capsaicin at various concentrations, and no capsacin-treated group was set as control group. Effects of capsaicin on NCI-H460 apoptosis, its invasion ability, and the changes in protein expressions of E-cadherin and Snail were evaluated by Hoechst33342 nuclear staining assay, Transwell chamber invasion assay, and Western blot respectively. Results Compared with the control group, Hoechst33342 nuclear staining assay showed that capsaicin could induce NCI-H460 cell apoptosis (P<0.05);Transwell invasion in vitro results showed that capsaicin could significantly inhibit invasion of penetrating cells (P<0.05);Western blot analysis showed that E-cadherin expression level was significantly elevated and snail expression level significantly decreased (P<0.05).Conclusion Capsaicin can induce NCI-H460 cell apoptosis. Decrease the Snail expression and stimulate E-cadherin expression so as to inhibit the invasion ability of NCI-H460, which may be one of its mechanisms of anti-non-small cell lung cancer.
