The American journal of Chinese medicine
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Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. ⋯ The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.
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18[Formula: see text]-glycyrrhetinic acid (GA) is the active ingredient of the traditional Chinese medicinal herb Glycyrrhizae radix et rhizoma. We previously demonstrated that GA inhibited tumor growth in hepatocellular carcinoma (HCC). However, the effect of GA on transforming growth factor-[Formula: see text] (TGF-[Formula: see text]-induced epithelial-mesenchymal transition (EMT) and metastasis were still unclear. ⋯ Mechanistically, GA inhibited the phosphorylation of STAT3 by increasing the expression of Src homology 2 domain-containing protein tyrosine phosphatases 1 and 2 (SHP1 and SHP2). Therefore, we concluded that GA inhibited TGF-[Formula: see text]-induced EMT and metastasis via the SHP1&SHP2/STAT3/Snail pathway. Our data provide an attractive therapeutic target for future multimodal management of HCC.
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While pattern identification (PI) is an essential process in traditional medicine (TM), it is difficult to objectify since it relies heavily on implicit knowledge. Therefore, this study aimed to propose a machine learning (ML)-based analysis tool to evaluate the clinical decision-making process of PI in terms of explicit and implicit knowledge, and to observe the actual process by which this knowledge affects the choice of diagnosis and treatment in individual TM doctors. Clinical data for the development of the analysis tool were collected using a questionnaire administered to allergic rhinitis (AR) patients and the diagnosis and prescription results of TM doctors based on the completed AR questionnaires. ⋯ The analysis results for eight doctors showed that our tool could successfully identify explicit and implicit knowledge in the PI process. This is the first study to evaluate the actual process by which explicit and implicit knowledge affect the choice of individual TM doctors and to identify assessment tools for the definition of the decision-making process in diagnosing PI and prescribing herbal treatments by TM clinicians. The assessment tool suggested in this study could be broadly used for the standardization of precision medicine, including TM therapeutics.
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Dandelion (Taraxacum species) is a wild plant with over 2500 species. Flavonoids, phenolic compounds, saponins, sesquiterpenes, and sugars have been detected in the organs of Taraxacum, and for centuries it has been used in traditional medicine for the relief and treatment of various diseases. However, details of its working mechanism remain unclear. ⋯ What's more, we also found that the effects of fresh T. formosanum were much stronger than that of T. mongolicumin HeLa cells. Based on these results, we suggest that T. formosanum may contain specific compound(s) that are potentially useful for cancer therapy. However, much work remains to identify these effective compounds for the future application of Taraxacumto cancer therapy.
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The development of anti-COVID-19 drugs has become the top priority since the outbreak of the epidemic, and Traditional Chinese medicine plays an important role in reducing mortality. Here, hesperidin and its glycosylation product, glucosyl hesperidin were selected to determine their antiviral activity against SARS-CoV-2 due to their structural specificity as reported. To be specific, their binding ability with ACE2, M, S, RBD and N proteins were verified with both in silico and wet lab methods, i.e., molecular docking and binding affinity tests, including biolayer interferometry assay (BLI) and isothermal titration calorimetry assay (ITC). ⋯ In addition, both hesperidin and glucosyl hesperidin were shown to have a great impact on immune, inflammation and virus infection induced by COVID-19 according to the systematic pharmacological analysis. Moreover, the IC50s of hesperidin and glucosyl hesperidin against SARS-CoV-2 were further determined (51.5 [Formula: see text]M and 5.5 mM, respectively) with cell-based in vitro assay, suggesting their great anti-SARS-CoV-2 activity. All in all, present research was the first to verify the binding ability of hesperidin and glucosyl hesperidin with SARS-CoV-2 proteins with both in silico and wet-lab methods and proposed the possibility of applying hesperidin and glucosyl hesperidin to treat COVID-19.