OBJECTIVE:To study the pharmacokinetics of magnolin in rats. METHODS:High performance liquid chromatog-raphy(HPLC)was adopted. The determination was performed on Kromasil C18 with mobile phase consisted of acetonitrile-tetrahy-drofuran-water (39∶1∶60),at the flow rate of 1.0 ml/min,with the measurement wavelength of 278 nm,column temperature of 35 ℃ and the sample size of 20 μl. 8 Wistar rats were docked to collect blood from the caudal vein before administration [10 mg(medicinal materials)/kg] and 0.25,0.5,0.75,1,1.5,2,4,8,12 and 20 h after administration,to determine the concentra-tion of the drug in blood. DAS2.1.1 software was used to calculate pharmacokinetic parameters. RESULTS:In the determination of magnolin,the mass concentration linear range was found to be 0.05-10.00 μg/ml(r=0.999 5);the RSD of precision test and sta-bility test were less than 13%(n=6);relative recovery rate was 97.32%-102.15%(n=6);extraction recovery rate was 84.63%-90.02%;t1/2α of magnolin in rats was(0.48±0.22)h,t1/2β was(7.96±2.57)h,CL/F was(0.09±0.032)L/(h·kg),and AUC0-20 h was(944.43±212.83)mg·h/L. CONCLUSIONS:This method conforms to the requirements for the determination of bio-logical samples with respect to precision,stability and accuracy. Magnolin demonstrates a good linear relationship between AUC0-20 h and the dose in rats,with the process compatible with two-compartment model.

Objective To evaluate the impacts of air temperature on years of life lost(YLL) among the residents in Guangzhou and Zhuhai,Guangdong province. Methods Daily mortality and meteorology data in Guangzhou and Zhuhai were collected,and distributed lag non-linear model (DLNM)was used to evaluate the cumulative and delayed effects of daily air temperature on YLL of total non-accident mortality. The accumulative effect of air temperature on mortality under the extreme high temperature(0-1 days)and extreme low temperature(0-13 days)situation in Guangzhou and Zhuhai were analyzed respectively. Results The average YLL was 1 928.0 in Guangzhou and 202.5 in Zhuhai. The exposure-response functions seemed to be non-linear. The hot effect seemed to be acute and reached the peak at the same day,while the cold effect reached the peak at 5th days and lasted for about two weeks. Low temperature had stronger gross effect than high temperature had. The cold effect among males was greater than that among females in Guangzhou. The hot/cold effect on YLL was greater in people aged ≥65 years than in people aged <65 years and in people suffering from respiratory disease than in people suffering from cardiovascular disease in both Guangzhou and Zhuhai. Conclusion The effects of high and low temperatures on YLL were obvious,and the impact of low temperature was greater. The elderly and people suffering from respiratory disease or cardiovascular disease are the vulnerable populations.

OBJECTIVETo explore the impact of the socio-economic factors on the temperature-mortality association in different cities in southern China.

METHODSDaily mortality registration data, meteorological data and air pollution data of the cities as Changsha and Kunming during 2006-2009, and cities as Guangzhou and Zhuhai during 2006-2010, were collected to explore modifying effects, stratified by age, gender, education and place of death, of socio-economic factors on the association between temperature and mortality, by distributed lag non-linear model. The accumulative effect of temperature-mortality were separately analyzed in each city, under the high temperature (0-3 days) and low temperature (0-20 days) situation. The association between temperature and mortality was evaluated by general linear threshold model. The above process was firstly adopted to analyze the impact in single city and then Meta analysis was applied to analyze the impact in several cities by effect-combine.

RESULTSThe relationship between temperature and mortality in the four cities showed nonlinearity. The minimum mortality risk was separately 23.5 °C, 20.5 °C, 25.0 °C and 26.0 °C in Changsha, Kunming, Guangzhou and Zhuhai. The results of effect-combine showed that low-temperature (RR = 1.67, 95%CI:1.54-1.80) has a higher gross effect than high-temperature (RR = 1.11, 95%CI:1.01-1.18) on population. With the age increasing, risk of death increased both under high and low temperature situation, and the effect of low temperature was greater (RR = 1.83, 95%CI:1.65-2.04) for the elderly than it of high temperature (RR = 1.17, 95%CI:1.03-1.33). The mortality risk among females (cold and hot effects(95%CI) were 1.75(1.57-1.97) and 1.11(0.99-1.25), respectively)was higher than it among males (cold and hot effects(95%CI) were 1.59(1.45-1.77) and 1.11(1.03-1.19), respectively). Whereas the mortality risk on higher education population was significantly higher (cold and hot effects (95%CI) were 1.89(1.48-2.45)and 1.34(1.19-1.48), respectively) than it on other educated people.

CONCLUSIONAge, gender, educational level and place of death showed modifying effects on the association between temperature and mortality. The elderly, women and highly educated people were vulnerable to the temperature influence on mortality.

Aged ; Air Pollution ; China ; Cold Temperature ; adverse effects ; Female ; Hot Temperature ; adverse effects ; Humans ; Male ; Mortality ; Nonlinear Dynamics ; Risk ; Socioeconomic Factors

Objective To estimate the effect of daily diurnal temperature range (DTR) on mortality in different areas in China.Methods A time series study using the data collected from 66 areas in China was conducted,and Meta-analysis was used to analyze the estimates of associations between DTR and daily mortality.Modifying effects of extremely low and high DTR-mortality relationship by season and socioeconomic status (SES) were also evaluated respectively.Cumulative excess risk (CER) was used as an index to evaluate the effects.Results The information about 1 260 913 registered deaths were collected between 1 January 2006 and 31 December 2011,we found the relationship between extreme DTR and mortality was non-linear in all regions and the exposure-response curve was J-shaped.In central and south areas of China,the result indicated the obvious acute effect of extremely high DTR,and the mortality effect in central area (CER=5.1％,95％CI:2.4％-7.9％) was significant higher than that in south area (CER=4.5％,95％CI:1.7％-7.3％).Regarding to the modification of seasons,the cumulative mortality effect of DTR in cold season (CER=5.8％,95％CI:2.5％-9.2％) was higher than that in hot season (CER=3.1％,95％CI:1.1％-5.1％).Generally,deaths among the elderly (≥75 years) were associated more strongly with extremely high DTR.Conclusions The mortality effects of extremely DTR in different areas and seasons showed different characteristics,that in central area and in cold season it was significantly stronger.After modified by season and SES,DTRs were the greatest threat to vulnerable population,especially to the elderly (≥75 years).Therefore,more attention should be paid to vulnerable groups and protection measures should be taken according to the local and seasonal conditions.

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.

Background It is projected that the frequency, density, and duration of compound hot extreme may increase in the 21st century in the context of global warming. Objective To explore the association between compound hot extreme and blood pressure, and identify sensitive populations. Methods This was a cross-sectional study. The study subjects were from six Guangdong Province Chronic Disease and Nutrition Surveys during 2002 through 2015. A questionnaire was administered to the participants with questions about demographic information, drinking and smoking status, and measurements on their height, weight, and blood pressure were also collected. We chose the data of May, September, and October to explore the association between compound hot extreme and blood pressure. Compound hot extreme means a hot day with a proceeding hot night. Daily meteorological data were obtained from China Meteorological Data Service Centre. We employed inverse distance weighting to interpolate the temperature and relative humidity values for each participant. A distributed lag non-linear model was used to estimate the association between compound hot extreme and blood pressure. Stratified analyses by sex, age, area, body mass index (BMI), smoking status, and drinking status were also performed to identify sensitive populations. A sensitivity analysis was conducted by adjusting the degrees of freedom for lag spline and removing relative humidity. Result A total of 10967 participants without history of hypertension were included in this study. The average systolic blood pressure (SBP) was 120.8 mmHg and the average diastolic blood pressure (DBP) was 74.5 mmHg. The proportion of participants who experienced hot day, hot night, or compound hot extreme were 9.34%, 17.95% and 2.90%, respectively. Compared to hot day, hot night and compound hot extreme were related with decreased blood pressure, and the effect of compound hot extreme was stronger: the changes and 95%CI for SBP was −6.2 (−10.3-−2.1) mmHg, and for DBP was −2.7 (−5.2-−0.2) mmHg. Compound hot extreme induced decreased SBP among male, population ≥ 65 years, and those whose BMI < 24 kg·m^-2, and their ORs (95%CIs) were −6.2 (−10.7-−1.6). −19.1 (−33.0-−5.1), and −6.7 (−11.8~−1.6) mmHg, respectively, and also decreased DBP among population ≥ 65 years, and its OR (95%CI) was −8.4 (−15.6-−1.1) mmHg. During compound hot extremes, participants living in rural areas showed decreased SBP and DBP, and the ORs (95%CIs) were −10.5 (−16.6-−4.5) and −4.4 (−7.7-−1.1) mmHg respectively, while those living in urban areas showed increased SBP, and the OR (95%CI) was 9.7 (2.9-16.5) mmHg. A significant decrease in blood pressure [OR (95%CI)] was also found in non-smokers [DBP, −3.7 (−6.6-−0.8) mmHg] and non-drinkers [SBP, −4.8 (−9.4-−0.2) mmHg; DBP, −3.4 (−6.0-−0.9) mmHg]. Conclusion Compound hot extreme is negatively associated with SBP, and being male, aged 65 years and over, and having BMI < 24 kg·m⁻² may be more sensitive to compound hot extreme.

Objective:To identify the threshold of a health warning system based on the association of apparent temperature and years of life lost (YLL).Methods:Daily mortality records and meteorological data were collected from 364 Chinese counties for 2006-2017. Distributed lag nonlinear model and multivariate Meta-analyses were applied to estimate the association between the apparent temperature and YLL rate. A regression tree model was employed to estimate the warning thresholds of the apparent temperature. Stratified analyses were further conducted by age and cause of death.Results:The daily YLL rate was 23.6/10 5. The mean daily apparent temperature was 15.7 ℃. U-shaped nonlinear associations were observed between apparent temperature and YLL rate. The actual temperature-caused YLL rate for the elderly was higher than the young population. The daily excess deaths rate increased with the higher effect levels. Conclusions:Regression tree model was employed to define the warning threshold for meteorological health risk. The present study provides theoretical support for the weather-related health warning system.

OBJECTIVETo evaluate the impacts of air temperature on years of life lost (YLL) among the residents in Guangzhou and Zhuhai, Guangdong province.

METHODSDaily mortality and meteorology data in Guangzhou and Zhuhai were collected, and distributed lag non-linear model (DLNM) was used to evaluate the cumulative and delayed effects of daily air temperature on YLL of total non-accident mortality. The accumulative effect of air temperature on mortality under the extreme high temperature (0-1 days) and extreme low temperature (0-13 days) situation in Guangzhou and Zhuhai were analyzed respectively.

RESULTSThe average YLL was 1 928.0 in Guangzhou and 202.5 in Zhuhai. The exposure-response functions seemed to be non-linear. The hot effect seemed to be acute and reached the peak at the same day, while the cold effect reached the peak at 5(th) days and lasted for about two weeks. Low temperature had stronger gross effect than high temperature had. The cold effect among males was greater than that among females in Guangzhou. The hot/cold effect on YLL was greater in people aged ≥ 65 years than in people aged < 65 years and in people suffering from respiratory disease than in people suffering from cardiovascular disease in both Guangzhou and Zhuhai.

CONCLUSIONThe effects of high and low temperatures on YLL were obvious, and the impact of low temperature was greater. The elderly and people suffering from respiratory disease or cardiovascular disease are the vulnerable populations.

Aged ; Air ; Cardiovascular Diseases ; epidemiology ; China ; epidemiology ; Extreme Cold ; adverse effects ; Extreme Heat ; adverse effects ; Female ; Humans ; Male ; Middle Aged ; Mortality, Premature ; Nonlinear Dynamics ; Respiratory Tract Diseases ; epidemiology ; Time Factors

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 identify the definition of heat wave based on mortality risk assessment in different regions of China. Methods: Daily mortality (from China Information System for Disease Control and Prevention) and meteorological data (from National Meteorological Information Center in China) from 66 counties with a population of over 200 000 were collected from 2006-2011. With the consideration of climate type and administrative division, China was classified as seven regions. Firstly, distributed lag non-linear model (DLNM) was used to estimate community-specific effects of temperature on non-accidental mortality. Secondly, a multivariate meta-analysis was applied to pool the estimates of community-specific effects to explore the region-specific temperature threshold and the duration for definition of heat wave. Results: We defined regional heat wave of Northeast, North, Northwest, East, Central and Southwest China as being two or more consecutive days with daily mean temperature higher than or equal to the P₆₄, P₇₁, P₈₅, P₆₇, P₇₅ and P₇₇ of warm season (May to October) temperature, respectively, while the thresholds of temperature were 21.6, 23.7, 24.3, 25.7, 28.0 and 25.3 ℃. The heat wave in South China was defined as five or more consecutive days with daily mean temperature higher than or equal to the P₉₃ (30.4 ℃) of warm season (May to October) temperature. Conclusion The region-specific definition of heat wave developed in our study may provide local government with the guidance of establishment and implementation of early heat-health response systems to address the negative health outcomes due to heat wave.