Study on construction of rat model of acute pancreatitis with syndrome of liver depression and Qi stagnation / 中国中药杂志

The aim of this paper was to construct a rat model of acute pancreatitis(AP) with syndrome of liver depression and Qi stagnation, and provide evaluation tools for pharmacodynamic research and efficacy network verification of related traditional Chinese medicine in view of the etiology, pathogenesis and clinical manifestations of AP. According to the Chinese and Western medicine diagnosis and treatment guidelines for AP with syndrome of liver depression and Qi stagnation, etiology, pathogenesis and clinical syndromes in TCM, Meta-analysis results, and evaluation strategy of establishing an animal model combining disease and syndrome in our laboratory, the biological surrogate outcomes suitable for the evaluation of animal models of AP with syndrome of liver depression and Qi stagnation were extracted then. The chronic unpredictable stress(CUS) method and chronic unpredictable stress +L-arginine(CUS +L-Arg) method were used to construct the rat model, and the above biological surrogate outcomes were used to evaluate whether an AP rat model was established. During the experiment, the weight and syndrome scores of the rats were observed and recorded. At the end of the experiment, the rats' serum, organs and tissues were collected from the operation to detect the various indicators. As compared with the normal group, the syndrome scores of the CUS group and CUS +L-Arg group were significantly increased(P<0.01); the anti-syndrome medicine Chaihu Shugan Pills could significantly reduce the syndrome scores of the two groups of rats(P<0.01), indicating that both modeling methods can replicate the syndrome of liver depression and Qi stagnation in the rat model. As compared with the normal group, the serum amylase(AMY) activity level was increased by 3 times in the CUS +L-Arg group(P<0.01), and the AMY activity level was also increased in CUS group, but not up to 3 times of the normal value. As compared with the normal group, the le-vels of TNF-α and IL-6 mRNA in the pancreatic tissues of the CUS +L-Arg group were significantly increased(P<0.01); the levels of TNF-α mRNA in the pancreatic tissues of the CUS group were significantly increased(P<0.05), but IL-6 mRNA level only showed a rising trend, indicating that only the CUS + L-Arg method can be used to replicate the AP damage in the disease-syndrome combination model. The CUS + L-Arg method can be used for continuous modeling for 4 weeks to establish a disease-syndrome combination model of AP rats with syndrome of liver depression and Qi stagnation. The model has the characteristics of repeatability, stability after mode-ling, low animal mortality, and similar clinical pathogenesis. It can be used for the evaluation of traditional Chinese medicine efficacy and the verification of efficacy network.

Comparison of three time series models in predicting the incidence of healthcare-associated infection / 中国感染控制杂志

Objective To compare and evaluate the effect of different time series models in predicting incidence of healthcare-associated infection (HAI), and explore the best model for predicting incidence of HAI.Methods Seasonal autoregressive integrated moving average (ARIMA) model, nonlinear autoregressive neural network (NARNN), and ARIMA-back propagation neural network (ARIMA-BPNN) combination model were constructed based on fitting dataset of monthly HAI incidence from 2011 to 2016 (72 months) in a tertiary first-class hospital in Shanghai, predicting dataset of monthly infection incidence from January to December 2017 were used to test the predictive effect of model, the predictive effect of different models was evaluated and compared.Results For the fitting dataset, mean absolute percentage error (MAPE) of ARIMA, NARNN, and ARIMA-BPNN combination model were 13.00％, 14.61％, and 11.95％respectively;and for the predicting dataset, MAPE of ARIMA, NARNN, and ARIMA-BPNN combination model were 15.42％, 26.31％, and 14.87％ respectively.Conclusion Three time series models can effectively predict the incidence of HAI, of which the ARIMA-BPNN combination model showed the best performance in fitting and predicting the occurrence of HAI in this hospital, and can provide data support for the hospital decision-making.

1H-MRS detection of metabolites in posterior cingulate gyrus of Parkinson disease with cognitive impairment patients / 中国医学影像技术

Objective To investigate the value of 1H-MRS technology combined with linear combination model (LCmodel) software in diagnosis of Parkinson disease (PD) cognitive impairment.Methods Thirty-five PD patients (PD group) and 22 matched healthy subjects (control group) were collected.Patients in PD group were divided into PDN and PDMCI subgroups according to whether having cognitive impairment or not.The concentration of metabolites of posterior cingulate gyrus (PCG)was applied with 1H-MRS technology combined with LCmodel software.The differences of metabolites were compared between the two groups,and the correlations between metabolites level and cognitive status were analyzed.Results The absolute concentrations of metabolites in PDN subgroup were not significantly different from those in control group (all P＞0.05).The absolute concentrations of total creatine (tCr),N-acetyl aspartate (NAA),myo-inositol (mI) and glycerophosphocholine+ phosphocholine (tCho) in PDMCI subgroup were lower than those in control group (all P＜0.05).The absolute concentration of tCr in PDMCI subgroup was lower than that in PDN subgroup (P＜0.05).There was positive correlation among the absolute concentration of tCr (r=0.444,P=0.01),glutathione (GSH;r=0.393,P=0.024) and MMSE scores,as well as among the absolute concentration of tCr (r=0.367,P=0.035),GSH (r=0.376,P=0.031),tCho (r=0.375,P=0.031) and MoCA scores.Conclusion 1 H-MRS technology combined with LCmodel software can quantitatively analyze the changes of metabolites in PCG,therefore being helpful to evaluating PD cognitive impairment.

Effect of combination model on fitting cancer mortality and prediction / 中华流行病学杂志

Objective To reduce the cancer burden in the Jinchang cohort and provide evidence for developing cancer prevention strategies and performing effectiveness evaluation in the Jinchang cohort.We are fitting thirteen years of cancer mortality data from the Jinchang cohort by using six kinds of predicting methods to compare relative fitness and to select good predicting methods for the prediction of cancer mortality trends.Methods The mortality data of cancer in Jinchnag cohort from 2001-2013 were fitted using six kinds of predicting methods:dynamic series,linear regression,exponential smoothing,autoregressive integrated moving average (ARIMA) model,grey model (GM),and Joinpoint regression.Weight coefficients of combination models were calculated by four methods:the arithmetic average method,the variance inverse method,the mean square error inverse method,and the simple weighted average method.Results The cancer mortality was fitted and compared by using six kinds of forecasting methods;the fitting precision of the Joinpoint linear regression had the highest accuracy (87.64％),followed by linear regression (87.32％),the dynamic series (86.99％),GM (1,1) (86.25％),exponential smoothing (85.72％) and ARIMA (1,0,0) (81.98％),respectively.Prediction accuracy of the combination model derived from GM (1,1) and linear regression (＞99％) was higher than that of the combination model derived from ARIMA (1,0,0) and GM (1,1).The combination model derived from the GM (1,1) and linear regression,with weight coefficients based on the arithmetic average method and the mean square error inverse method,had the best prediction effect of the four weight calculation methods.Conclusion Prediction accuracy of the combination model,with accuracy ＞95％,was higher than that of the single prediction methods.

Effect of combination model on fitting cancer mortality and prediction / 中华流行病学杂志

Objective To reduce the cancer burden in the Jinchang cohort and provide evidence for developing cancer prevention strategies and performing effectiveness evaluation in the Jinchang cohort.We are fitting thirteen years of cancer mortality data from the Jinchang cohort by using six kinds of predicting methods to compare relative fitness and to select good predicting methods for the prediction of cancer mortality trends.Methods The mortality data of cancer in Jinchnag cohort from 2001-2013 were fitted using six kinds of predicting methods:dynamic series,linear regression,exponential smoothing,autoregressive integrated moving average (ARIMA) model,grey model (GM),and Joinpoint regression.Weight coefficients of combination models were calculated by four methods:the arithmetic average method,the variance inverse method,the mean square error inverse method,and the simple weighted average method.Results The cancer mortality was fitted and compared by using six kinds of forecasting methods;the fitting precision of the Joinpoint linear regression had the highest accuracy (87.64％),followed by linear regression (87.32％),the dynamic series (86.99％),GM (1,1) (86.25％),exponential smoothing (85.72％) and ARIMA (1,0,0) (81.98％),respectively.Prediction accuracy of the combination model derived from GM (1,1) and linear regression (＞99％) was higher than that of the combination model derived from ARIMA (1,0,0) and GM (1,1).The combination model derived from the GM (1,1) and linear regression,with weight coefficients based on the arithmetic average method and the mean square error inverse method,had the best prediction effect of the four weight calculation methods.Conclusion Prediction accuracy of the combination model,with accuracy ＞95％,was higher than that of the single prediction methods.

Advantages, Disadvantages and Sticking Points of Traditional Chinese Medicine Animal Syndrome Models And Disease-syndrome Combination Models / 世界科学技术-中医药现代化

Studies showed that animal model of the so-called simple syndrome currently have corresponding modern medical disease without exception. Even such animal model is called simple syndrome model, its properties is a cer-tain modern medicine disease. There is no specific corresponding relationship between the syndrome-attribution and the syndrome. The signs and symptoms differentiation and abnormal physical and chemical detection indices after pathogen-modeling of traditional Chinese medicine (TCM) cannot be determined since there are no specific corre-sponding relationships. The study on disease-syndrome combination model contains both modern medical modeling method and TCM modeling method. This type of modeling method has its own existing shortcomings. Meanwhile, two modeling methods used in the model establishment have uncertain relationship. The disease-syndrome combination model is usually not established in the comparison among different syndromes of one disease. Therefore, it has no ex-clusive attribute. There are many problems existed in the TCM modeling method, disease-syndrome combination modeling method, and syndrome-attribute evaluation methods. The basic reasons are the wide difference between symptoms and signs for diagnosis in modern medicine disease. There are a few symptoms and signs for diagnosis in modern medicine. But there are much more symptoms and signs for syndrome differentiation of the same disease. Many symptoms for syndrome differentiation are unknown for its source of origin existed in a virtual state. It caused that many virtual syndromes abstracted by virtual symptoms, and was reflected by the relevant standards. It leads to devastating consequences for the development of animal modeling method.