Theriac and Tao： More Aspects on Byzantine Diplomatic Gifts to Tang China

This article deals with the presenting of diplomatic gifts around the 7th Century from the Roman Empire in the East （i.e. the Byzantine Empire）, and among these a kind of medication supposedly capable of curing all sorts of illnesses, theriac. The links between these uses and Taoist expectations regarding ever-lasting life are discussed herein in the context of that diplomatic mission. It explores the different understandings of what theriac meant in the Byzantine and in the Tang courts. This sheds light on the kind of different misunderstandings, sometimes of an ironical nature, which may happen in intercultural contacts of political-diplomatic nature.

Mechanism of Yinchenhao decoction in the treatment of jaundice based on network pharmacology

Objective:This study aimed to examine the mechanism of classic ancient prescription of Chinese medicine Yinchenhao decoction in treating jaundice based on network pharmacology.Method:An oral bioavailability of≥30%,a drug likeness of≥0.18,and literature studies were used to screen for Yinchen(Artemisiae scopariae herba),Zhizi(Gardeniae fructus),Dahuang(Rhei radix et rhizome)in the Chinese Medicine System Pharmacology Database and Analysis Platform.The active ingredient was introduced into the PubChem database to collect drug component targets and import into the Uniprot database for gene standardization.The target gene of Yinchen(Artemisiae scopariae herba)was screened via Human Gene Database(GeneCards).Then,use the Cytoscape 3.7.2 software was used for network visualization analysis,and the R3.6.1 software was used for gene ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses.Results:We collected a total of 47 active constituents of classic ancient prescription of Chinese medicine Yinchenhao decoction,of which 17 were related to jaundice;189,9 targets of jaundice were screened,of which 41 were interdigitated with the targets of classic ancient prescription of Chinese medicine Yinchenhao decoction.Gene ontology functional enrichment analysis revealed 111 biological processes,14 cellular components,and 28 molecular functions,and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed 34 Kyoto Encyclopedia of Genes and Genomes pathways including hepatocellular carcinoma,PI3K-Akt signaling pathway,HIF-1 signaling pathway,prolactin signaling pathway,and non-alcoholic fatty liver disease.Conclusion:Based on the network pharmacology,the analysis of jaundice and classic ancient prescription of Chinese medicine Yinchenhao decoction provides a novel idea and direction for the study of classic ancient prescription of Chinese medicine Yinchenhao decoction in the treatment of jaundice.

Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning

Objective:This study aimed to construct an intelligent prescription-generating(IPG)model based on deep-learning natural language processing(NLP)technology for multiple prescriptions in Chinese medicine.Materials and Methods:We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation,and the Yellow Emperor’s Canon of Internal Medicine,the Classic of the Miraculous Pivot,and the Classic on Medical Problems as supplementary datasets for fine-tuning.We selected the word-embedding model based on the Imperial Collection of Four,the bidirectional encoder representations from transformers(BERT)model based on the Chinese Wikipedia,and the robustly optimized BERT approach(RoBERTa)model based on the Chinese Wikipedia and a general database.In addition,the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model.Multiple IPG models were constructed based on the pretraining strategy and experiments were performed.Metrics of precision,recall,and F1-score were used to assess the model performance.Based on the trained models,we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns.Results:Among all the trained models,the RoBERTa-large model performed the best,with a test set precision of 92.22%,recall of 86.71%,and F1-score of 89.38%and 10-fold cross-validation precision of 94.5%±2.5%,recall of 90.47%±4.1%,and F1-score of 92.38%±2.8%.The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer’s patterns showed the associations of symptom–symptoms,disease–symptoms,and symptom–punctuations,while the between-layer’s patterns showed a progressive or dynamic symptom and disease transformation.Conclusions:Deep-learning-based NLP technology significantly improves the performance of IPG model.In addition,NLP-based semantic feature extraction may be vital to further investigate the ancient Chinese medicine texts.