Spatial analysis and influencing factors of pulmonary tuberculosis among students in Nanning, during 2012-2018.

BACKGROUND: Economically underdeveloped areas in western China are hotspots of tuberculosis, especially among students. However, the related spatial and temporal patterns and influencing factors are still unclear and there are few studies to analyze the causes of pulmonary tuberculosis in students from the perspective of space. METHODS: We collected data regarding the reported incidence of pulmonary tuberculosis (PTB) among students at township level in Nanning, from 2012 to 2018. The reported incidence of pulmonary tuberculosis among students in Nanning was analyzed using spatial autocorrelation and spatial scan statistical analysis to depict hotspots of PTB incidence and spatial and temporal clustering. Spatial panel data of the reported incidence rates and influencing factors at district and county levels in Nanning were collected from 2015 to 2018. Then, we analyzed the spatial effects of incidence and influencing factors using the spatial Durbin model to explore the mechanism of each influencing factor in areas with high disease prevalence under spatial effects. RESULTS: From 2012 to 2018, 1609 cases of PTB were reported among students in Nanning, with an average annual reported incidence rate of 14.84/100,000. Through the Joinpoint regression model, We observed a steady trend in the percentage of cases reported each year (P>0.05). There was spatial autocorrelation between the annual reported incidence and the seven-years average reported incidence from 2012 to 2018. The high-incidence area was distributed in the junction of six urban areas and spread to the periphery, with the junction at the center. The population of college students, per capita financial expenditure on health, per capita gross domestic product, and the number of health technicians per 1,000 population were all influencing factors in the reported incidence of PTB among students. CONCLUSION: We identified spatial clustering of the reported incidence of PTB among students in Nanning, mainly located in the urban center and its surrounding areas. The clustering gradually decreased from the urban center to the surrounding areas. Spatial effects influenced the reported incidence of PTB. The population density of college students, per capita health financial expenditure, gross domestic product (GDP) per capita, and the number of health technicians per 1,000 were all influencing factors in the reported incidence of PTB among students.

Identification of Forsythia suspensa (Thunb.) Vahl in different harvest periods using intelligent sensory technologies, HPLC characteristic fingerprint coupled with chemometrics.

INTRODUCTION: Forsythia suspensa (Thunb.) Vahl (FS), the fruit of Oleaceae plants, as a large part of traditional Chinese medicine, is classified as "Qingqiao (Q)" and "Laoqiao (L)" based on the harvest time. Because the maturation of FS is a gradual process, its accurate identification based on different maturity levels is an important issue. OBJECTIVES: We suggest colorimetric, electronic tongue, and high-performance liquid chromatography (HPLC) characteristic fingerprints to discriminate FS in different harvest periods. MATERIAL AND METHODS: First, FS fruits from different harvest times were collected, and then, their colour parameters, E-tongue sensory properties, HPLC characteristic fingerprints, and contents of nominal ingredients were determined. Finally, multivariate statistical analyses, including three-dimensional scatter plots, hierarchical cluster, principal component, linear discriminant, similarity, and partial least squares discriminant analyses were performed. RESULTS: The results demonstrated that the three experimental techniques could effectively discriminate FS based on different harvest times with 100% accuracy. Under the qualitative conditions, nine common peaks were identified in the HPLC fingerprints of 60 samples, among which, six peaks [variable importance in projection (VIP) > 1] could be used as index peaks for qualitative identification. In fact, the contents of quality marker components, including forsythin, phillygenin, rutin and forsythoside A, were significant different (P < 0.001) at different harvest times. Interestingly, the quality markers not only accurately reflected the maturity of FS but also showed close correlations with the colour parameters and sensory E-tongue responses. CONCLUSION: In our present investigation, bionic technologies, including a colorimeter, E-tongue analysis, and HPLC characteristic fingerprints, combined with chemometrics, were employed to develop a novel and accurate method for discriminating FS based on different harvest times.