OBJECTIVE： To establish a method for simultaneous determination of spinosin and jujuboside A in the seads of Ziziphus jujuba， and to investigate its quality grading standard. METHODS： HPLC-ELSD method was adopted. The separation was carried out on Inertsil ODS-SP column with mobile phase consisted of acetonitrile-water （gradient elution） at the flow rate of 1.0 mL/min. The column temperature was 30 ℃， the temperature of drift tube was 90 ℃， the flow of carrier gas was 2.9 L/min and injection volume was 20 μL. The thickness， width， length and 100-grain quality of the medicinal materials were used as indicators to investigate the appearance traits. SPSS 22.0 software was used to analyze the correlation of the contents of spinosin and jujuboside A， its appearance traits with the quality constant of TCM， and establish a quality classification standard for the seads of Z. jujuba. RESULTS： The linear range of spinosin and jujuboside A were 1.03-6.18 μg/mL （r＝0.999 7）， 1.05-6.30 μg/mL （r＝0.999 8）； the limits of quantitation were 0.171， 0.174 μg/mL， respectively； the limits of detection were 0.052， 0.053 μg/mL， respectively. RSDs of precision， stability and reproducibility tests were all lower 2％. The recoveries were 99.01％-102.97％ （RSD＝1.39％， n＝6）， 97.94％-101.03％ （RSD＝1.13％， n＝6）， respectively. Correlation analysis results showed that the length， width， 100-grain quality spinosin content and jujuboside A content of the medicinal materials were positively correlated with the quality constant of TCM. The results of quality classification for 30 batches of medicinal materials showed that S1-S4 and S7-S12 were first-class products； S5， S6， S13-S17 and S20-S30 were second-class products； S18 and S19 were third-class products. CONCLUSIONS： Established content determination method is simple， precision， accurate and stable， and can be used for simultaneous determination of spinosin and jujuboside A in the seads of Z. jujuba. Established quality grading standard of the seads of Z. jujuba can be used to evaluate the quality.

Objective: Network pharmacology combines drug and disease targets with biological information networks based on the integrity and systematicness of the interactions between drugs and disease targets. This study aims to explore the molecular basis of Hanshi Zufei formula for treatment of COVID-19 based on network pharmacology and molecular docking techniques. Methods: Using TCMSP, the chemical constituents and molecular targets of Atractylodis Rhizoma, Citri Reticulatae Pericarpium, Magnoliae Officinalis Cortex, Pogostemonis Herba, Tsaoko Fructus, Ephedrae Herba, Notopterygii Rhizoma et Radix, Zingiberis Rhizoma Recens, and Arecae Semen were investigated. The predicted targets of novel coronavirus were screened using the NCBI and GeneCards databases. To further screen the drug-disease core targets network, the corresponding target proteins were queried using multiple databases (Biogrid, DIP, and HPRD), a protein interaction network graph was constructed, and the network topology was analyzed. The molecular docking studies were also performed between the network's top 15 compounds and the coronavirus (SARS-CoV-2) 3CL hydrolytic enzyme and angiotensin conversion enzyme II (ACE2). Results: The herb-active ingredient-target network contained nine drugs, 86 compounds, and 49 drug-disease targets. Gene ontology (GO) enrichment analysis resulted in 1566 GO items (P < 0.05), among which 1438 were biological process items, 35 were cell composition items, and 93 were molecular function items. Fourteen signal pathways were obtained by enrichment screening of the KEGG pathway database (P < 0.05). The molecular docking results showed that the affinity of the core active compounds with the SARS-CoV-2 3CL hydrolase was better than for the other compounds. Conclusion: Several core compounds can regulate multiple signaling pathways by binding with 3CL hydrolase and ACE2, which might contribute to the treatment of COVID-19.