OBJECTIVETo establish the method of graphite furnace atomic absorption spectrometry for the measurement of stannum in urine with calcium nitrate as the matrix modifier.

METHODSGraphite tube was pretreated with calcium nitrate as the matrix modifier, the urine sample was diluted with 1% nitric acid and then direct injection was performed for these samples, and graphite furnace atomic absorption spectrometry was applied for measurement.

RESULTSThe concentration of stannum in urine showed a good linear relationship within the range of 8.0~40.0 μg/L, with a correlation coefficient of 0.9981. The minimum detectable concentration was 0.72 μg/L, the degree of precision was 1.54%~6.69%, and the recovery rate was 99.23%~107.63%.

CONCLUSIONThis method can determine the content of stannum in urine accurately and rapidly, with a high sensitivity and a low cost.

Graphite ; Humans ; Spectrophotometry, Atomic ; Tin ; urine

Background In recent years, the incidence of metabolic syndrome (MS) is increasing significantly in China. Some studies have found that temperature is related to single metabolic index, but there is a lack of research on associated mechanism and identifying path of the influence of temperature on MS. Objective Based on the data of Guangdong Province, to investigate the effect of temperature on MS and its pathway. Methods A total of 8524 residents were enrolled by multi-stage random sampling from October 2015 to January 2016 in Guangdong. Basic characteristics, behavioral characteristics, health status, and physical activity level were obtained through questionnaires and physical examinations, and meteorological data were obtained from meteorological monitoring sites. We matched individual data both with the temperature data of the physical examination day and of a lag of 14 d. A generalized additive model was used to explore the exposure-effect relationship between temperature and MS and its indexes, calculate effect values, and explore the effects of single-day lag temperature. Based on the literature and the results of generalized additive model analysis, a path analysis was conducted to explore the pathways of temperature influencing MS. Results The association between daily average temperature on the current day or lag 14 day and MS risk was not statistically significant. When daily average temperature increased by 1 ℃, the change values of fasting blood-glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), and high density lipoprotein cholesterol (HDL-C) were −0.033 (95%CI: −0.040-−0.026) mmol·L⁻¹, −0.662 (95%CI: −0.741-−0.583) mmHg, −0.277 (95%CI: −0.323-−0.230) mmHg, and −0.005 (95%CI: −0.007-−0.004) mmol·L⁻¹ respectively. The effects of average daily temperature on FBG, blood pressure, HDL-C, and waist circumference lasted until lag 14 day. The effects of daily average temperature on SBP and DBP were the largest on the current day. Daily average temperature of current day had direct and indirect effects on FBG and SBP. Temperature had an indirect effect on TG, and the intermediate variables were waist circumference and FBG, with an indirect effect value of −0.011 (95%CI: −0.020-−0.002). The indirect effects of daily average temperature on SBP, FBG, and TG were weak. Conclusion There is no significant correlation between temperature and risk of MS, and daily average temperature of current day could significantly affected blood pressure and FBG with a lag effect. Daily average temperature of current day has indirect effects on FBG and TG.

Objective To compare the epidemiological characteristics of COVID-19 in Guangzhou and Wenzhou, and evaluate the effectiveness of their prevention and control measures. Methods Data of COVID-19 cases reported in Guangzhou and Wenzhou as of 29 February, 2020 were collected. The incidence curves of COVID-19 in two cities were constructed. The real time reproduction number ( R t ) of COVID-19 in two cities was calculated respectively. Results A total of 346 and 465 confirmed COVID-19 cases were analysed in Guangzhou and Wenzhou, respectively. In two cities, most cases were aged 30-59 years (Guangzhou: 54.9%; Wenzhou: 70.3%). The incidence curve peaked on 27 January, 2020 in Guangzhou and on 26 January, 2020 in Wenzhou, then began to decline in both cities. The peaks of imported COVID-19 cases from Hubei occurred earlier than the peak of COVID-19 incidences in two cities, and the peak of imported cases from Hubei occurred earlier in Wenzhou than in Guangzhou. In early epidemic phase, imported cases were predominant in both cities, then the number of local cases increased and gradually took the dominance in Wenzhou. In Guangzhou, the imported cases was still predominant. Despite the different epidemic pattern, the R t and the number of COVID-19 cases declined after strict prevention and control measures were taken in Guangzhou and in Wenzhou. Conclusion The time and scale specific differences of imported COVID-19 resulted in different epidemic patterns in two cities, but the spread of the disease were effectively controlled after taking strict prevention and control measures.

Objective: To evaluate the exported risk of novel coronavirus pneumonia (NCP) from Hubei Province and the imported risk in various provinces across China. Methods: Data of reported NCP cases and Baidu Migration Indexin all provinces of the country as of February 14, 2020 were collected. The correlation analysis between cumulative number of reported cases and the migration index from Hubei was performed, and the imported risks from Hubei to different provinces across China were further evaluated. Results: A total of 49 970 confirmed cases were reported nationwide, of which 37 884 were in Hubei Province. The average daily migration index from Hubei to other provinces was 312.09, Wuhan and other cities in Hubei were 117.95 and 194.16, respectively. The cumulative NCP cases of provinces was positively correlated with the migration index derived from Hubei province, also in Wuhan and other cities in Hubei, with correlation coefficients of 0.84, 0.84, and 0.81. In linear model, population migration from Hubei Province, Wuhan and other cities in Hubei account for 71.2%, 70.1%, and 66.3% of the variation, respectively. The period of high exported risk from Hubei occurred before January 27, of which the risks before January 23 mainly came from Wuhan, and then mainly from other cities in Hubei. Hunan Province, Henan Province and Guangdong Province ranked the top three in terms of cumulative imported risk (the cumulative risk indices were 58.61, 54.75 and 49.62 respectively). Conclusion The epidemic in each province was mainly caused by the importation of Hubei Province. Taking measures such as restricting the migration of population in Hubei Province and strengthening quarantine measures for immigrants from Hubei Province may greatly reduce the risk of continued spread of the epidemic.

Objective To assess the imported risk of COVID-19 in Guangdong province and its cities, and conduct early warning. Methods Data of reported COVID-19 cases and Baidu Migration Index of 21 cities in Guangdong province and other provinces of China as of February 25, 2020 were collected. The imported risk index of each city in Guangdong province were calculated, and then correlation analysis was performed between reported cases and the imported risk index to identify lag time. Finally, we classified the early warming levels of epidemic by imported risk index. Results A total of 1 347 confirmed cases were reported in Guangdong province, and 90.0% of the cases were clustered in the Pearl River Delta region. The average daily imported risk index of Guangdong was 44.03. Among the imported risk sources of each city, the highest risk of almost all cities came from Hubei province, except for Zhanjiang from Hainan province. In addition, the neighboring provinces of Guangdong province also had a greater impact. The correlation between the imported risk index with a lag of 4 days and the daily reported cases was the strongest (correlation coefficient: 0.73). The early warning base on cumulative 4-day risk of each city showed that Dongguan, Shenzhen, Zhongshan, Guangzhou, Foshan and Huizhou have high imported risks in the next 4 days, with imported risk indexes of 38.85, 21.59, 11.67, 11.25, 6.19 and 5.92, and the highest risk still comes from Hubei province. Conclusions Cities with a large number of migrants in Guangdong province have a higher risk of import. Hubei province and neighboring provinces in Guangdong province are the main source of the imported risk. Each city must strengthen the health management of migrants in high-risk provinces and reduce the imported risk of Guangdong province.