OBJECTIVE:To study the inhibitory effects of minocycline (MC) on TNF-α induced monocyte-endothelial adhe-sion and the relative mechanism. METHODS:Primary human umbilical vein endothelial cells (HUVECs) were isolated from hu-man umbilical veins with enzyme digestion. HUVECs were divided into blank control group,model group,MC low-dose,medi-um-dose and high-dose groups(1,10,100 μmol/L). After treated for 2 h,10 ng/ml TNF-α was employed to stimulate monocytes THP-1 adhesion with HUVECs except for blank control group,in order to induce monocyte-endothelial adhesion model. The num-ber of adherent cell was observed by fluorescence microscope,and fluorescence intensity was detected by microplate reader. Flow cytometry was adopted to detect the expression of intercellular adhesion molecule (ICAM)-1. The expressions of NF-κB p65 pro-tein in cell nucleus and cytoplasm were detected by Western blot. RESULTS:Compared with blank control group,the number and fluorescence intensity of adherent cell,the expression of ICAM-1 and the protein expression of NF-κB p65 in nucleus were all in-creased in model group,while the protein expression of NF-κB p65 in cytoplasm was weakened,with statistical significance(P<0.01). Compared with model group,the number and fluorescence intensity of adherent cell,the expression of ICAM-1 were all de-creased in MC low-dose,medium-dose and high-dose groups;the protein expression of NF-κB p65 in nucleus was weakened in MC medium-dose and high-dose groups,while the protein expression of NF-κB p65 in cytoplasm was heightened,with statistical significance (P<0.01 or P<0.05). CONCLUSIONS:MC can inhibit TNF-α induced monocyte-endothelial adhesion by a likely mechanism of reducing the expression of ICAM-1 in HUVECs and inhibiting the expression of NF-κB p65 protein.

Objective:To establish a method for simultaneous determination of 7 elemental impurities(V,Co,Ni,As,Cd,Hg Pb)in amlodipine besylate tablets based on inductively coupled plasma mass spectrometry(ICP-MS).Methods:After the samples were treated by microwave digestion,the solution was analyzed by ICP-MS.Ge,In,Bi were selected as the internal standards.The established method was validated.The contents of 7 elemental impurities in amlodipine besylate tablets from 54 enterprises were determined by this method.Results:The content of(V,Co,Ni,As,Cd,Hg,Pb had good linear relationship in the ranges of 1-100,1-100,1-100,1-100,1-100,0.5-4,1-100 ng·mL-1,respectively.The correlation coefficients(r)all above 0.999 4.The detec-tion limits and quantification limits were in the range of 0.000 4-0.018 4 ng·mL-1 and 0.001 4-0.061 2 ng·mL-1.The RSD of precision was less then 1.8％.The RSD of repeatability was less then 6.1％.The average re-coveries(n=9)were between 85.4％-106.5％,while their RSD was less then 4.7％.The content of 7 elemental impurities in 54 sample batches were in accordance with the limit value.Conclusion:The method is simple,rapid,accurate,reliable and highly sensitive,and can be used for the quality control of elemental impurities in amlodipine besylate tablets.

The rapid development of artificial intelligence （AI） technology provides new opportunities for the modernisation of traditional Chinese medicine （TCM） diagnosis. By analysing the foundation， research progress and difficulties of the combination of AI and TCM diagnosis， it is concluded that AI has made remarkable development in intelligence-driven modernization of TCM tongue diagnosis， pulse diagnosis， listening and smelling diagnosis and text processing， and there are useful explorations in the field of constructing data-driven TCM diagnostic model and multidisciplinary integration of TCM diagnostic models. However， the current integration of AI technology in TCM diagnosis still faces many challenges， such as the scarcity and uneven quality of clinical data， the limited ability of AI algorithms to express TCM thinking model of syndrome differentiation and empirical knowledge， and the possible existence of ethical and privacy issues. By systematically sorting out the current research status and development direction of AI-empowered TCM diagnostics， it is proposed to promote the application of AI technology in TCM diagnostics in four aspects， namely， strengthening the construction of TCM big data and talent cultivation， encouraging cross-disciplinary cooperation， improving the legal and ethical framework， and promoting the popularity of the technology in primary care， so as to enhance the modernisation of TCM diagnostics.