scholarly journals 2019_nCoV/SARS‐CoV‐2: rapid classification of betacoronaviruses and identification of Traditional Chinese Medicine as potential origin of zoonotic coronaviruses

2020 ◽  
Vol 70 (5) ◽  
pp. 342-348 ◽  
Author(s):  
T.M. Wassenaar ◽  
Y. Zou
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Potential Origin

scholarly journals Classification of interventions in Traditional Chinese Medicine

2018 ◽  
Vol 38 (2) ◽  
pp. 315-320 ◽  
Author(s):  
Deng Hongyong ◽  
Clive E Adams ◽  
Farhad Shokraneh ◽  
Liang Shanghua
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine

scholarly journals Stability of Active Ingredients of Traditional Chinese Medicine (TCM)

2009 ◽  
Vol 4 (12) ◽  
pp. 1934578X0900401 ◽  
Author(s):  
Wang Meng ◽  
Ren Xiaoliang ◽  
Gao Xiumei ◽  
Franco Francesco Vincieri ◽  
Anna Rita Bilia
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Animal Origin ◽  
Active Ingredients ◽  
Rational Development ◽  
Plant Constituents ◽  
Crude Drugs ◽  
The Stability ◽  
First Time

Studies on stability of active ingredients are fundamental and critical for the rational development of Traditional Chinese Medicine (TCM) in view of its modernization and worldwide use. The stability of both active and marker constituents of plants used in TCM is reviewed for the first time. More than 100 papers, mostly written in Chinese, have been reviewed. Studies concerning plant constituents were analyzed according to their chemical classification of active ingredients. In addition, several crude drugs of animal origin are also reported. Stability of active ingredients is summarized during extraction and/or storage of the herbal drug preparations, and under stress conditions (pH, temperature, solvents, light, and humidity) and in the presence of preservatives, antioxidants, and metals.

A Computer Method for Validating Traditional Chinese Medicine Herbal Prescriptions

2005 ◽  
Vol 33 (02) ◽  
pp. 281-297 ◽  
Author(s):  
J. F. Wang ◽  
C. Z. Cai ◽  
C. Y. Kong ◽  
Z. W. Cao ◽  
Y. Z. Chen
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Statistical Learning ◽  
Conventional Medicine ◽  
Computer Method ◽  
Support Vector ◽  
Random Combination ◽  
System A ◽  
Statistical Learning Method

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an alternative to conventional medicine. TCM herbal prescriptions contain a mixture of herbs that collectively exert therapeutic actions and modulating effects. Traditionally defined herbal properties, related to the pharmacodynamic, pharmacokinetic and toxicological, as well as physicochemical properties of their principal ingredients, have been used as the basis for formulating TCM multi-herb prescriptions. These properties are used in this work to develop a computer program for predicting whether a multi-herb recipe is a valid TCM prescription. This program is based on a statistical learning method, support vector machine (SVM), and it is trained by using 575 well-known TCM prescriptions and 1961 non-TCM recipes generated by random combination of TCM herbs. Testing results by using 72 well-known TCM prescriptions and 5039 non-TCM recipes showed that 73.6% of the TCM prescriptions and 99.9% of non-TCM recipes are correctly classified by this system. A further test by using 48 TCM prescriptions published in recent years found that 68.7% of these are correctly classified. These accuracies are comparable to those of SVM classification of other biological systems. Our study indicates the potential of SVM for facilitating the analysis of TCM prescriptions.

scholarly journals Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yang Xiang ◽  
Lai Shujin ◽  
Chang Hongfang ◽  
Wen Yinping ◽  
Yu Dawei ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Fundus Photography ◽  
Diabetes Diagnosis ◽  
Medical Resources ◽  
New Approach

In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.

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