[Comprehensive evaluation of Pinellia ternata germplasm resources based on phenotypic trait classification].

The evaluation of germplasm resources is the prerequisite for the development, utilization, and conservation of Chinese medicinal resources. The selection of excellent germplasm is the key to the breeding and orderly production of Pinellia ternata. In this study, 21 germplasm materials of P. ternata from major production areas in China were collected and analyzed for population diversity after phenotypic preliminary screening. The results have revealed that the P. ternata population has abundant phenotypic variation, and the phenotypic changes could be divided into five phenotypes in terms of organ trait variation. Further analysis of variation in 20 quantitative traits of the population revealed that the coefficient of variation for adenosine content(339.05%) was the largest, while the coefficient of variation for the underground plant height(16.35%) was the smallest. Correlation analysis showed that there was a strong correlation among various traits, with 52 pairs of traits showing highly significant correlation(P<0.01) and 19 pairs of traits showing a significant correlation(P<0.05). The 21 germplasms in the test could be classified into three major clusters by cluster analysis, with Cluster Ⅱ having the highest number and content of nucleosides, making it suitable for the selection and breeding of P. ternata varieties with high content of nucleosides. The yield in Cluster Ⅲ was higher than that in other groups, making it suitable for the selection and breeding of P. ternata varieties with a high yield. All trait indicators could be simplified into five principal component factors through principal component analysis, and the cumulative contribution rate was up to 86.04%. Further, comprehensive analysis using membership function and stepwise regression analysis identified nine traits, such as plant height, main leaf length, and underground plant height as characteristic indicators for the comprehensive evaluation of germplasm resources of P. ternata. BX007, BX008, and BX005 were identified as germplasms with both high yield and high uridine content, with BX007 having the highest uridine content of 479.51 µg·g~(-1). It belonged to the germplasm of P. ternata with double bulbils and could be cultivated as a potential good variety. Based on the phenotypic classification of P. ternata, systematic resource evaluation was carried out in this study, which could lay a foundation for the excavation of genetic resources and the breeding of new varieties of P. ternata.

Navigating traditional chinese medicine network pharmacology and computational tools.

The concept of "network target" has ushered in a new era in the field of traditional Chinese medicine (TCM). As a new research approach, network pharmacology is based on the analysis of network models and systems biology. Taking advantage of advancements in systems biology, a high degree of integration data analysis strategy and interpretable visualization provides deeper insights into the underlying mechanisms of TCM theories, including the principles of herb combination, biological foundations of herb or herbal formulae action, and molecular basis of TCM syndromes. In this study, we review several recent developments in TCM network pharmacology research and discuss their potential for bridging the gap between traditional and modern medicine. We briefly summarize the two main functional applications of TCM network models: understanding/uncovering and predicting/discovering. In particular, we focus on how TCM network pharmacology research is conducted and highlight different computational tools, such as network-based and machine learning algorithms, and sources that have been proposed and applied to the different steps involved in the research process. To make network pharmacology research commonplace, some basic network definitions and analysis methods are presented.

[Application of bron-kerbosch algorithm for discovery of basic formulas of traditional Chinese medicine].

OBJECTIVE: To study the feasibility of applying the Bron-Kerbosch (BK) algorithm on the discovery of basic formulas (BFs) of traditional Chinese medicine. METHOD: This essay introduces the BK algorithm for the discovery of BFs of traditional Chinese medicine and relevant indicators. On the basis of the compatibility network of drugs, with the confidence coefficient of basic formulas anti the support degree to the decline of alpha level as indicators, the BK algorithm was used for the discovery of BFs of traditional Chinese medicine. And prescriptions of Professor Ma Shaoyao, a traditional Chinese medicine (TCM) dermatologist of Longhua hospital, were taken as examples for analysis. RESULT: After the parameters were optimized, three BFs for psoriasis and four BFs for eczema were found using the BK algorithm, which had a relatively high confidence coefficient and basically conformed to the clinical practices. CONCLUSION: The BK algorithm can be used for the discovery of basic formulas (BF) of traditional Chinese medicine, and therefore it is one of the effective methods to study and summarize the diagnosis and treatment experiences and prescription thoughts of famous and experienced TCM doctors.