A research on syndrome element differentiation based on phenomenology and mathematical method

HPE-GCN: predicting efficacy of tonic formulae via graph convolutional networks integrating traditionally defined herbal properties

Chinese herbal formulae are the heritage of traditional Chinese medicine (TCM) in treating diseases through thousands of years. The formula function is not just a simple herbal efficacy addition, but produces complex and nonlinear relationships between different herbs and their overall efficacy, which brings challenges to the formula efficacy analysis. In our study, we proposed a model called HPE-GCN that combines graph convolutional networks (GCN) with TCM-defined herbal properties (TCM-HPs) to predict formulae efficacy. In addition, to process the unstructured natural language in the formula text, we proposed a weighting calculation method related to herb frequency and the number of herbs in a formula called Formula-Herb dependence degree (FHDD), to assess the dependency degree of a formula with its herbs. In our research, 214 classic tonic formulae from ancient TCM books such as Synopsis of the Golden Chamber, Jingyue's Complete Works and the Golden Mirror of Medicin were collected as datasets. The performance of HPE-GCN on multi-classification of tonic formulae reached the best result compared with classic machine learning models, such as support vector machine, naive Bayes, logistic regression, gradient boosting decision tree, and K-nearest neighbors. The evaluated index Macro-Precision, Macro-Recall, Macro-F1 of HPE-GCN on the test set were 87.70%, 84.08% and 83.51% respectively, increased by 7.27%, 7.41% and 7.30% respectively from second best compared models. GCN has the advantage of low-dimensional feature expression for herbs and formulae, and is an effective analysis tool for TCM research. HPE-GCN integrates TCM-HPs and fits the complex nonlinear mapping relationship between TCM-HPs and formulae efficacy, which provides new ideas for related research.

An exploration of new methods for metabolic syndrome examination by infrared thermography and knowledge mining

Metabolic syndrome (MS) is a clinical syndrome with multiple metabolic disorders. As the diagnostic criteria for MS still lacking of imaging laboratory method, this study aimed to explore the differences between healthy people and MS patients through infrared thermography (IRT). However, the observation region of the IRT image is uncertain, and the research tried to solve this problem with the help of knowledge mining technology. 43 MS participants were randomly included through a cross-sectional method, and 43 healthy participants were recruited through number matching. The IRT image of each participant was segmented into the region of interest (ROI) through the preprocessing method proposed in this research, and then the ROI features were granulated by the K-means algorithm to generate the formal background, and finally, the two formal background were separately built into a knowledge graph through the knowledge mining method based on the attribute partial order structure. The baseline data shows that there is no difference in age, gender, and height between the two groups (P > 0.05). The image preprocessing method can segment the IRT image into 18 ROI. Through the K-means method, each group of data can be separately established with a 43 × 36 formal background and generated a knowledge graph. It can be found through knowledge mining and independent-samples T test that the average temperature and maximum temperature difference between the chest and face of the two groups are statistically different (P < 0.01). IRT could reflect the difference between healthy people and MS people. The measurement regions were found by the method of knowledge mining on the premise of unknown. The method proposed in this paper may add a new imaging method for MS laboratory examinations, and at the same time, through knowledge mining, it can also expand a new idea for clinical research of IRT.

The Diagnosis and Management of Allergic Reactions Caused by Chinese Materia Medica

Traditional Chinese medicines (TCM) have been used in China for thousands of years. Although TCM has been generally perceived to be safe, adverse reactions to Chinese materia medica (CMM) have been reported. Most of the adverse reactions are allergic in nature, but other mechanisms may play a role. This review focuses on the mechanism and clinical presentation of these allergic reactions. Allergic reactions can occur as a result of the active and inactive ingredients of CMM. Impurities and chemicals generated during the production process can also lead to allergic or adverse reactions. Environmental factors such as temperature, humidity, and light can cause changes in the allergenicity of drugs. Human error in formulating CMM drugs also contributes to adverse drug reactions. The management of allergic reactions to CMM includes taking a good history, avoidance of medications in the same class as those which caused prior reactions, the proper training of staff, adherence to manufacturer guidelines and expiration dates, evaluation of benefit and risk balance, and the formulation of a risk management strategy for the use of CMM. A small test dose of a considered drug before using, improvements in drug purification technology, and proper storage and clinical administration help reduce allergic reactions due to CMM.

Evidence-based traditional Chinese medicine research: Two decades of development, its impact, and breakthrough

It has been over 20 years since the introduction of evidence-based medicine (EBM) into the research of traditional Chinese medicine (TCM). The development of evidence-based TCM research has profoundly influenced the process of clinical research and decision-making, impelling researchers to pay attention to raise evidence quality, accumulate data, and explore appropriate evaluation methods adaptive to TCM original theories and knowledge. In this paper, the authors aim to summarize and review the existing work and seek promising research interests in this field, expecting to inspire more thoughts leading to breakthroughs in the near future.

The RIGHT Extension Statement for Traditional Chinese Medicine: Development, Recommendations, and Explanation

Nowadays, the number of traditional Chinese medicine (TCM) guidelines is constantly increasing, but its reporting quality remains unsatisfactory. One of the main reasons is that there is a lack of suitable reporting standard to guide it. In response to this long-standing problem, the Reporting Items for practice Guidelines in HealThcare (RIGHT) Working Group has invited a group of TCM clinical experts, methodologists and epidemiology, and developed the RIGHT Extension Statement for TCM (RIGHT-TCM) through a multi-staged development process, including systematic review, reporting quality evaluation and online Delphi expert consensus. The RIGHT-TCM extends two sections of the RIGHT Statement, includes basic information and recommendations section. Seven strong recommendation sub-items were added to RIGHT Statement and formed the final RIGHT-TCM. The group hopes that the RIGHT-TCM may assist TCM guideline developers in reporting guidelines, support journal editors and peer reviewers when considering TCM guideline reports, and help health care practitioners understand and implement a TCM guideline. This article will introduce its background, development, recommendations and explanation.

The Causality Research between Syndrome Elements by Attribute Topology

Background

The traditional Chinese medicine (TCM) is an empirical medical system and has its own diagnosis and treatment method. The syndrome elements are atoms to modern TCM diagnosis proposed by Professor Zhu Wenfeng. Researching and analyzing the syndrome element system is one of the active issues for TCM research. At present, most related researches focus on the correlativity and hierarchical relationship of the diseases and symptoms, but the causality researches between syndrome elements themselves have not been reported so far.

Methods

To explore the causality between syndrome elements, a method named causality by attribute topology (CAT) is proposed. Based on the subordinate relations in attribute topology, the inference method analyzes and reasons the dependency relationship between the sets of objects which contain attributes. Through the removal of attributes in the attribute topology, the formal context is updated constantly. Thus, the causal relationship among the attributes is deduced. In this method, 500 records are mathematically transferred to a binary context for syndrome element analysis. Through the analysis and verification of the potential causal relationship between the syndrome elements, knowledge discovery of the diagnostic data of traditional Chinese medicine based on attribute topology structure diagram is conducted.

Results

This paper has verified the causal transformation between these syndrome elements. The experimental results between the female group data and the male group data show that different genders have different characteristics and relations of syndrome elements. The experimental results are basically consistent with the traditional Chinese medicine theory.

Conclusion

The experiment shows that causality by attribute topology (CAT) is feasible to describe the causality between TCM syndrome elements. Further research on possible knowledge discovery in TCM diagnostic data should be conducted through the analysis of the potential causal relationship between TCM diagnostic data and each syndrome element.