Establishment of Feedforward Control Model for Dry Granulation of Polysaccharide Components Based on Quality by Design / 中国实验方剂学杂志

ObjectiveA feedforward control model for dry granulation of polysaccharide components was established to guide the adjustment and optimization of critical process parameters （CPPs） in the design space， so as to reduce the impact of fluctuations in raw materials properties on the quality of medicines. MethodTaking Astragali Radix extract powder as the model drug， the design space of dry granulation CPPs was determined by Box-Behnken design. Astragali Radix mixed powder with different powder properties were prepared by mixture design， the variance inflation factor （VIF） was used to diagnose the multicollinearity of the powder properties， and principal component analysis （PCA） was used to extract the characteristic data of the model. Radial basis function neural network （RBFNN） was used to establish a feedforward control model for reflecting the relationship between the powder properties of polysaccharide components， dry granulation CPPs and one-time molding rate. ResultThe design space for dry granulation CPPs of polysaccharide components was 16-35 Hz for feeding speed， 10-23 Hz for roller speed， and 10-46 kg·cm-2 for roller pressure. The established RBFNN feedforward control model had a good predictive effect on the one-time molding rate of dry granulation of polysaccharide components， which could be used to guide the adjustment and optimization of CPPs in the design space， the relative error was 0.38%-6.73%， and the average relative error was 3.42%. ConclusionThe established feedforward control model can well reflect the relationship between the powder properties of the polysaccharide components， the dry granulation CPPs and the one-time molding rate of the granules， which can be used to guide the adjustment and optimization of CPPs in the design space， reduce the impact of material property fluctuation on product quality， and provide ideas for promoting the quality of traditional Chinese medicine from passive control to active control.

Prediction of Disintegration Time of Traditional Chinese Medicine Tablets Based on Generalized Regression Neural Network / 中国实验方剂学杂志

Objective:This paper constructs a generalized regression neural network （GRNN） model to predict the disintegration time of traditional Chinese medicine （TCM） tablets. Method:Taking Astragali Radix as a model drug， the mixed Astragali Radix powders with different powder properties were prepared by mixing Astragali Radix extract powders with microcrystalline cellulose and lactose， which were made to Astragali Radix tablets by direct compression method. The powder properties of mixed Astragali Radix powders and the disintegration time of Astragali Radix tablets were determined， respectively. The correlation between the original data was eliminated by principal component analysis （PCA）. The principal component factors were used as the input layer of the GRNN model， and the disintegration time was used as the output layer for network training. Finally， the verification group data was used to predict the disintegration time， and the network prediction accuracy was calculated by comparing with the actual value. Result:Three principal component factors were obtained through PCA by analyzing the original nine variables that were correlated with each other （Hausner ratio， true density， tap density， compression degree， angle of repose， bulk density， porosity， water content and total dissolved solids）， which reduced the complexity of the network. The prediction value of the disintegration time based on this prediction method was in good agreement with the actual value， the error of disintegration time was 0.01-1.34 min and the average relative error was 3.16%. Conclusion:Based on the GRNN mathematical model， the physical properties of Astragali Radix extract powders can be used to accurately predict the disintegration time of Astragali Radix tablets， which provides a reference for studying the disintegration time of TCM tablets.

Research on Bayesian fault diagnosis model of traditional Chinese medicine dry granulation based on failure model and effect analysis (FMEA) / 中国中药杂志

This paper aims to construct a Bayesian(BN) fault diagnosis model of traditional Chinese medicine dry granulation based on the failure model and effect analysis(FMEA), effectively control risk factors and ensure the quality of granules.Firstly, the risk ana-lysis of dry granulation process was carried out with FMEA, and the selected medium and high risk factors were taken as node variables to establish corresponding BN network with causality.According to the mathematical reasoning method of probability theory, the model was accurately inferred and verified by Netica, and the granule nonconformance was used as the evidence for reversed reasoning to determine the most likely cause of the failure that affected the granule quality.The BN fault diagnosis model of traditional Chinese medicine dry gra-nulation was established based on the medium and high risk factors of process, prescription and equipment screened out by FMEA, such as roller pressure, raw material viscosity, clearance between rollers in the paper.The fault diagnosis of traditional Chinese medicine dry granulation process was then carried out according to the model, and the posterior probability of each node under the premise of nonconforming granule quality was obtained.This method could provide strong support for operators to quickly eliminate faults and make decisions, so as to improve the efficiency and accuracy for fault diagnosis and prediction, with innovation in its application.

Studies on the correlation between long-term blood pressure variability and leukoaraiosis / 解放军医学杂志

Objective To investigate the correlation between long-term blood pressure variability (BPV) and leukoaraiosis (LA). Methods Blood pressure was measured monthly in 209 patients visiting Zhejiang Hospital during June 2011 to June 2013, and they were followed up for 12 months. Te patients were divided into LA 0-3 groups according to the MRI results and Fazekas scale. Te clinical information, long-term blood pressure, and the index of long-term BPV were compared between LA groups and non-LA group, and among patients with different levels of LA, and then the correlation was regressively analyzed. Results One hundred and thirty-eight patients in LA group, and 71 in non-LA group. Regression analysis revealed that signifcant differences in age and the history of hypertension and occurrence of stroke between LA groups and non-LA group (P<0.05). Te maximal systolic blood pressure, the mean value of long-term blood pressure, the standard deviation (SD), coefficient of variation (C V), and variation independent of mean (VIM) of blood pressure were higher in LA groups than in non-LA group (P<0.05). Tere was signifcant difference in maximun systolic pressure between every level of LA groups (P<0.05). Te mean long-term systolic blood pressure and SD, CV and VIM of long-term blood pressure were signifcantly higher in the patients of LA3 group than that in the patients of LA1 group (P<0.05). Logistic regression analysis showed that the mean long-term diastolic blood pressure (OR=1.048, 95% CI 1.028-1.067), systolic blood pressure SD (OR=1.064, 95% CI 1.001-1.131), CV (OR=1.086, 95% CI 1.002-1.177), VIM (OR=1.060, 95% CI 1.001-1.122), and diastolic blood pressure SD (OR=1.111, 95% CI 1.000-1.233) and CV (OR=1.091, 95% CI 1.001-1.190) were the independent risk factors of LA (P<0.05). Conclusion Tere is a signifcant correlation between long-term diastolic blood pressure and long-term BPV and LA.