Advances in the role of gut microbiota in the regulation of the tumor microenvironment (Review).

As a protector of human health, the gut microbiota plays an important role in the development of the immune system during childhood, and the regulation of dietary habits, metabolism and immune system during adulthood. Dysregulated gut flora is not pathogenic, but it can weaken the protective effect of the immune system and cause various diseases. The tumor microenvironment is a physiological environment formed during tumor growth, which provides nutrients and growth factors necessary for tumor growth. As an important factor affecting the tumor microenvironment, the intestinal microflora affects the development of tumors through the mechanisms of gut and microflora metabolites, gene toxins and signaling pathways. The present article aimed to review the components and mechanisms of action, clinical applications, and biological targets of gut microbiota in the regulation of the tumor microenvironment. The present review provides novel insights for the future use of intestinal flora, to regulate the tumor microenvironment, to intervene in the occurrence, development, treatment and prognosis of tumors.

Modeling of influenza-like illness prediction based on Elman neural network / 预防医学

Objective @#To build a model for influenza-like illness（ILI）prediction based on Elman neural network and to provide evidence for early warning of influenza epidemic in Zhejiang Province. @*Methods @#The data of ILI from 11 sentinel hospitals,influenza pathogen detection,meteorological factors and air pollutants in Zhejiang Province from 2013 to 2014 were collected. Time-delay correlation analysis was conducted to select variables for modeling. Based on Elman neural network,data from the 14th week of 2013 to the 44th week of 2014 were used as a training set to establish the model and the data from 45th week to 52nd weeks of 2014 were used as a test set for the model performance. @*Results @#There were ILI reported every week during 2013 and 2014,with a total of 506 391. The percentage of ILI cases per week was（3.07 ± 0.73）%. Ten variables selected by time-delay correlation analysis were the weekly average values of atmospheric pressure（13 weeks in advance）,vapor pressure（11 weeks in advance）,temperature（9 weeks in advance）,SO2（5 weeks in advance）,NO2（5 weeks in advance）,CO（5 weeks in advance）,PM2.5（5 weeks in advance）,PM10（5 weeks in advance）,air quality index（5 weeks in advance）and positive rate of pathogen（1 weeks in advance）. Elman neural network（10-15-1-1）was selected as the optimal model,and the prediction performed well,with 10.58% as the mean error rate and 0.876 7 as the nonlinear correlation coefficient. @*Conclusion @#This study demonstrated that Elman neural network including variables of meteorological factors,air pollutants and the positive rate of pathogen performed well on the short-term prediction of ILI incidence.