Objective To analyze the medication law and academic thoughts of Professor Wang Junhong in the treatment of tic disorders(TD)in children.Methods The cases of children with TD diagnosed and treated by Professor Wang Junhong from January 2015 to November 2022 were selected.Excel 2016 was used to analyze the clinical information of children with TD.The frequency ranking,property,taste and meridian tropism,and changes of prescription drugs were analyzed in multiple dimensions.SPSS Modeler 18.0 was used to analyze the drug association rules of prescriptions in 2021 and 2022.Cytoscape 3.9.0 was used to analyze the complex network of drug-drug strong,medium and weak links obtained by SPSS Modeler 18.0.The drug groups were obtained in SPSS,and Excel 2016 was used to analyze the annual changes of high-frequency drugs.Results Totally 5586 prescriptions were included,involving 198 kinds of Chinese materia medica,with a total frequency of 108356 times.The top five kinds of high-frequency Chinese materia medica were Chrysanthemi Flos,Acori Tatarinowii Rhizoma,Coptidis Rhizoma,Crataegi Fructus,Polygalae Radix.The medicinal properties were mostly cold,warm and mild.The medicinal tastes were mainly bitter,sweet and pungent.The main meridians of drugs were liver,heart and lung meridians.The association rule analysis showed that the common couplet medicines were Chrysanthemi Flos-Acori Tatarinowii Rhizoma and Acori Tatarinowii Rhizoma-Scorpio.Commonly used triple combination was Chrysanthemi Flos-Scorpio-Acori Tatarinowii Rhizoma.Clustering analysis showed 4 drug groups,reflecting the characteristics of Professor Wang Junhong's treatment of calming liver and tranquilizing mind.According to the time-flow analysis,since 2020,the proportion of drugs such as Bupleuri Radix,Scutellariae Radix,Haliotidos Concha,Gastrodiae Rhizoma and Margaritifera Concha have gradually increased,indicating that more attention should be paid to treating the liver,resolving phlegm and calming the mind.Conclusion In the treatment of TD in children,Professor Wang Junhong takes heart,liver and lung as the center.The prescription medication is to relieve wind and phlegm,soothe the liver and tranquilize the mind.In recent years,it has attached importance to the role of regulating emotions and resolving phlegm in the treatment of children with TD.

Background Fine particulate matter (PM_2.5) monitoring stations may generate missing data for a certain period of time due to various factors. This data loss will adversely affect air quality assessment and pollution control decision-making. Objective To propose an inverse distance weighted (IDW) spatiotemporal interpolation method based on particle swarm optimization (PSO) to interpolate and fill missing PM_2.5 spatiotemporal sequence data and increase interpolation accuracy. Methods An interpolation experiment was designed into two parts. The first part used hourly PM_2.5 observational data from four moments on January 1, 2017 in the Yangtze River Delta region. The second part employed daily PM_2.5 observational data from the first 10 d of January 2017 in the Beijing-Tianjin-Hebei region. Interpolation accuracy was evaluated using four metrics: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean relative error (MRE). Results IDW spatiotemporal interpolation method optimized with PSO significantly improved the accuracy of filling missing PM_2.5 spatiotemporal sequence data. In the hourly-scale experiment conducted in the Yangtze River Delta region, compared to a distance index of 2, the accuracy metrics RMSE, MAE, MAPE, and MRE generated by the proposed method improved on average by 0.17 μg·m⁻³, 0.27 μg·m⁻³, 0.17%, and 0.01%, respectively. The PM_2.5 spatial field maps generated for four moments based on this method clearly illustrated the spatiotemporal distribution characteristics of hourly PM_2.5 concentrations in the Yangtze River Delta region. In the daily-scale experiment conducted in the Beijing-Tianjin-Hebei region, the PSO-optimized distance index outperformed the traditional method, with interpolation accuracy improvements of approximately 0.215 μg·m⁻³, 0.283 μg·m⁻³, 0.174%, and 0.014%, respectively. Furthermore, the seasonal PM_2.5 spatial field maps generated by this method revealed the spatiotemporal distribution characteristics of PM_2.5 concentrations in the Beijing-Tianjin-Hebei region across different seasons, further validating the effectiveness and applicability of this method. Conclusion The IDW spatiotemporal interpolation method optimized with PSO is highly accurate and reliable for interpolating the missing data in the Yangtze River Delta region and the Beijing-Tianjin-Hebei region, providing valuable insights for air pollution control and public health protection.