Ro Nus ZhurJes theory is the core content of Tibetan medicine,it can be divided into "taste performance"(Ro Yi Nus Pa) and "essential performance"(Ngo Bovi Nus Pa),and "taste performance" is divided into six tastes,eight properties,three tastes after digestion,seventeen effects and so on."Essential performance" is divided into eight kinds,including odor efficacy,sources efficacy,color efficacy,et al.Form bearing relationships that "taste performance" and "essential performance",the butt welding joins the three-factors and twenty characteristics of disease,it is the theoretical basis of Tibetan medicine from collection and processing,from formula to finished product. It is also the main method for the evaluation of drug performance by Tibetan medicine.Many classic Tibetan medicine prescriptions are compatible under the guidance of Ro Nus ZhurJes.However,at present,the research of Tibetan medicine mainly concentrates on the chemical composition and pharmacological mechanism.It is seldom studied from the Ro Nus ZhurJes theory of Tibetan medicine.It is difficult to find the core rules and effect of Tibetan medicine.Therefore,based on the ancient literature of Tibetan medicine,this paper combs the theory of Tibetan medicine property,explains the six tastes,three tastes after digestion,and seventeen effects of Tibetan medicine function in three-factors,twenty characteristics and other diseases attributes,in order to provide theoretical reference for the study of drug properties of Tibetan medicine.

“Ro Nus ZhurJes” theory is the core content of Tibetan medicine property theory, and also the core compatibility law of Tibetan medicine. It takes six tastes of medicine as the surface medicinal property, three tastes after digestion by "three gastropyretie" as the inner medicinal property, eight properties and seventeen effects as the deep medicinal property, and its essential property is the special medicinal property, forming a multi-level medicinal property theory, but its internal structure still needs to be unified and sorted out, clinical practicability needs to be further improved. In this paper, literature mining, Tibetan medicine theory, vector structural model, K-means clustering analysis, Gephi 0.9.2 visualization and other methods were used to sort out the theory of Tibetan medicinal properties. It was found that the inherent structure of Tibetan medicine properties had the multi-dimensional structural characteristics of "seven medicinal properties+three characteristics", and that the same medicinal properties would overlap, while the opposite medicinal properties would offset and could be expressed by vectors. Therefore, the vector structural model of "Ro Nus ZhurJes" of Tibetan medicine was proposed in this paper. Taking twenty-five-flavor Yuganzi powder(skyu-ru-nyer-lnga) and 92 commonly used Tibetan medicine prescriptions as examples, the rates of single prescription with seventeen effects and multiple prescriptions acting on three factors were analyzed. The model analysis results were highly consistent with the clinical efficacy, and the main diseases were the same, which showed that this model had a high degree of fitting and interpretation. It could effectively and intuitively quantify the properties of Tibetan medicine prescriptions and uncover the complex hidden knowledge of Tibetan medicine theory. In the future, the vector structural model of "Ro Nus ZhurJes" will be applied to analyze the classical prescriptions of Tibetan medicine, and the fitting evaluation and modification optimization will be carried out continuously in order to improve the practicability of theory of Tibetan medicine property in the big data environment and provide methodological reference for the quantification of medicinal property of Tibetan medicine prescriptions.

Objective To investigate the feasibility of establishment of ultrasound radiomics-based models for classification of hepatic echinococcosis, so as to provide insights into precision ultrasound diagnosis of hepatic echinococcosis. Methods The ultrasonographic images were retrospectively collected from 200 patients with hepatic echinococcosis in Shiqu County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province in October 2014, and the regions of interest were plotted in ultrasonographic images of hepatic echinococcosis lesions. The ultrasound radiomics features of hepatic echinococcosis were extracted with 25 methods, and screened using pre-selection and the least absolute shrinkage and selection operator. Then, all ultrasonographic images were randomly assigned into the training and independent test sets according to the type of lesions at a ratio of 7:3. Machine learning models for classification of hepatic echinococcosis were created based on two classifiers, including kernel logistic regression (KLR) and medium Gaussian support vector machine (MGSVM). The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity and areas under the curves (AUC) of the created machine learning models for classification of hepatic echinococcosis were calculated. Results A total of 5 005 ultrasound radiomics features were extracted from 200 patients with hepatic echinococcosis using 25 methods, and 36 optimal radiomics features were screened through feature selection, based on which two machine learning models were created, including KLR and MGSVM. ROC curve analysis showed that MGS-VM presented a higher efficacy for hepatic echinococcosis classification than KLR in the training set, with a sensitivity of 0.82, a specificity of 0.78 and AUC of 0.88, while KLR presented a higher efficacy for hepatic echinococcosis classification than MGSVM in the independent test set, with a sensitivity of 0.82, a specificity of 0.72 and AUC of 0.86, respectively. Conclusions Ultrasound radiomics-based machine learning models are feasible for hepatic echinococcosis classification.

In this study, UPLC-Q-Exactive-MS/MS was used to rapidly analyze the chemical constituents of Meconopsis quintupli-nervia, and the anti-liver fibrosis mechanism of M. quintuplinervia was preliminarily analyzed by network pharmacology, molecular docking, and cell experiments. The chemical constituents of M. quintuplinervia were identified according to the information of MS~1 and MS~2, as well as the data in the literature and databases. SwissTargetPrediction and TargetNet were used to predict the potential targets. The targets related to liver fibrosis were collected from GeneCards and OMIM. The protein-protein interaction(PPI) network was constructed by STRING. Cytoscape 3.6.1 was used to construct and analyze the "constituent-target-disease" network to obtain key targets and their corresponding constituents in the network. DAVID 6.8 was used for GO analysis and KEGG signaling pathway enrichment analysis. Finally, the preliminary verification was carried out by molecular docking and cell experiments. As a result, 106 chemical constituents were identified from M. quintuplinervia, including 66 flavonoids, 16 alkaloids, 18 phenolic acids, 1 anthocyanin, and 5 other constituents. Among them, 3 constituents were identified as potential new compounds, and 59 constituents were reported in M. quintuplinervia for the first time. Network pharmacology analysis showed that M. quintuplinervia presumably acted on AKT1, SRC, JUN, EGFR, STAT3, HSP90 AA1, MAPK3, and other core targets through luteolin, isorhamnetin, quercetin, apigenin, kaempferide, amurine, 2-methylflavinantine, allocryptopine, the multi and other active compounds, thereby regulating the PI3 K/AKT signaling pathway, pathways in cancer, proteoglycans in cancer, FoxO signaling pathway, and other pathways to exert anti-liver fibrosis effects. M. quintuplinervia extract(MQE) could significantly down-regulate PI3 K and AKT protein levels in the HSC-T6 cell model induced by TGF-β1, suggesting that MQE may have the ability to regulate the PI3 K/AKT signaling pathway. The findings of this study indicated that the anti-liver fibrosis effect of M. quintuplinervia had multi-constituent, multi-target, and multi-pathway characteristics, which may provide a scientific basis for the research on the pharmacodynamic materials, action mechanism, and quality markers of M. quintupli-nervia.
Tandem Mass Spectrometry ; Molecular Docking Simulation ; Network Pharmacology ; Proto-Oncogene Proteins c-akt ; Papaveraceae ; Liver Cirrhosis ; Drugs, Chinese Herbal/pharmacology*