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Traditional Chinese medicine component compatibility optimizing method based on uniform design and artificial neural network

An artificial neural network, a technology of traditional Chinese medicine components, applied in neural learning methods, biological neural network models, pharmaceutical formulations, etc., to achieve the effects of simple and economical experimental design, perfect structure, and reasonable process design

Inactive Publication Date: 2017-01-04
NANJING UNIVERSITY OF TRADITIONAL CHINESE MEDICINE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there is no report on the method of selecting the blood sugar level of II diabetic rats as the drug efficacy index, designing the ratio through the uniform design technology, and using the artificial neural network to model the prescription-drug effect of traditional Chinese medicine.

Method used

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  • Traditional Chinese medicine component compatibility optimizing method based on uniform design and artificial neural network
  • Traditional Chinese medicine component compatibility optimizing method based on uniform design and artificial neural network
  • Traditional Chinese medicine component compatibility optimizing method based on uniform design and artificial neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] Example 1 Compatibility Design of Auxiliary Jiangtang Tablets Based on Uniform Design

[0034] According to the composition of the raw materials of Auxiliary Jiangtang Tablets, bitter gourd extract, kudzu root extract, Digupi extract, Panax notoginseng extract, mulberry leaf extract and Astragalus extract are labeled as A, B, C, D, E, F, according to the uniform experimental design method U10*(10 8 ) to optimize the distribution ratio of the groups. There are 6 influencing factors in total, and 10 dosage levels are set for each factor. The experimental design is shown in Table 1, and there are 10 combinations in total. The animal experiments were divided into three groups: normal animal group, model group and test group, and the test group included 10 kinds of ratios.

[0035] Table 1. Compatibility optimization and uniform design of different components of Fuju Jiangtang Tablets

[0036]

Embodiment 2II

[0037] The mensuration of the blood sugar after embodiment 2 type II diabetes rats gavage

[0038] On the basis of feeding with high-calorie feed, supplemented with small doses of streptozotocin, it causes sugar / lipid metabolism disorder, insulin resistance, and induces experimental type II diabetes rat model.

[0039] Purchase healthy female rats (180±20g), adapt to feeding with ordinary maintenance food for 3-5 days, fast for 3-4 hours, take tail blood, measure the blood sugar level before glucose administration (that is, 0 hours), and give 2.5g / kg ·The blood glucose values ​​at 0.5 and 2 hours after BW glucose were taken as the basic values ​​of the batch of animals. Divided into 12 groups according to blood glucose level at 0 and 0.5 hours, namely 1 blank control group, 1 model control group and 10 test groups, with 8 animals in each group. The blank control group was not treated, and the 10 test groups were given the test samples (540mg / kg b.w.) with different ratios in ...

Embodiment 3

[0041] Example 3 Using Artificial Neural Network Modeling to Optimize the Group Distribution Ratio of Auxiliary Jiangtang Tablets

[0042] The Auxiliary Jiangtang Tablet group-effect relationship neural network model is divided into the following steps to model:

[0043] 1.1 Construction of neuron network: RBFANN model with 3-layer structure is adopted. The 6 ports of the input layer correspond to the distribution ratio data of 6 groups respectively, and the output port is the measured value of blood glucose. The RBFANN model is taken as (6-9-1) structure, the hidden layer function is taken as a Gaussian radial basis function, the diffusion speed of the radial basis function is 0.55, and the output layer adopts a linear function.

[0044] 1.2 Neural network training: In order to ensure the credibility of the model, first use the "leave one out method" to cross-validate 10 samples, that is, select one of the 10 samples without repetition each time as the prediction sample, and ...

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Abstract

The invention discloses a traditional Chinese medicine component compatibility optimizing method based on uniform design and an artificial neural network. The method comprises the following steps that 1, different compatibility groups are set by applying a uniform experimental design method; 2, a model adopted in efficacy evaluation is built; 3, different proportion-efficacy relation models are built by applying the artificial neural network, and the optimal proportion and the optimal efficacy are acquired through network searching. The traditional Chinese medicine component compatibility optimizing method based on the uniform design and the artificial neural network is reasonable in process design and high in operability and can be used for optimizing traditional Chinese medicine component compatibility; compared with a traditional method, the optimizing efficiency can be significantly improved, the optimizing cost can be reduced, and the advantages of being rapid to operate, accurate, good in repeatability and the like are achieved.

Description

technical field [0001] The invention relates to a method for optimizing the composition of medicinal components, in particular to a method for optimizing the composition of traditional Chinese medicine based on uniform design and artificial neural network. Background technique [0002] With the development of the modernization of traditional Chinese medicine, the research on the compatibility and composition of traditional Chinese medicine has been raised from the level of traditional decoction pieces to the level of components. The combination of effective components of traditional Chinese medicine has become a new model of innovative research on traditional Chinese medicine, which innovates and extends the theory of prescription compatibility. One of the key issues in component compatibility research is how to obtain the optimal dose-effect ratio of each component for specific clinical diseases and drug efficacy indicators, so as to be able to quantitatively describe the re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61K36/815A61P3/10A01K67/02G06N3/04G06N3/08
CPCA01K67/02G06N3/08A61K36/258A61K36/42A61K36/481A61K36/488A61K36/605A61K36/815A01K2267/0362A01K2207/25G06N3/045A61K2300/00
Inventor 陶益任玉超陈西蔡宝昌
Owner NANJING UNIVERSITY OF TRADITIONAL CHINESE MEDICINE
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