Objective To investigate the relationship between metabolic syndrome (MS) and education level in Su-Xi-Chang area to provide evidence for healthcare policy making. Methods A clustered sampling of 6798 subjects from Su-Xi-Chang areas of Jiangsu Province was included. Questionnaires and physical examinations were completed to collect clinical data. Logistic regression was used to analyze weather education level could be an independent risk factor of MS. Results Significant differences were found in waist circumstance (WC) ,systolic blood pressure (SBP) and fasting blood glucose (FBG) among participants with different education level. Significant differences were also existed in triglyceride and diastolic blood pressure (DBP) between subjects with higher and lower education level ( P＜0. 05 ). In single-factor or multivariate analysis, education level was negatively correlated with the prevalence of MS (P＜0. 05 ). Conclusions Education level may be an independent risk factor of MS. People with low and moderate education level have higher risk of MS. Therefore,it is recommended that health-related information should become an integral part of the compulsory education system.

BACKGROUND:The effects of advanced glycation end products (AGEs) on osteoclast-induced bone resorption is controversial and the underlying mechanisms remain unclear. Most of the studies indicate that AGEs can enhance bone resorption, while some othersshowthe opposite effects.
OBJECTIVE:To investigate the effects of AGEs on osteoclast-induced inorganicmatrixdissolution and organic componentdegradation and the underlying mechanisms.
METHODS:RAW 264.7 cels were induced to generate osteoclasts,and AGEs (50-400 μg/mL) or control-bovine serum albumin (100 μg/mL) was added since the beginning of the induction. The effect of AGEs on bone resorption was evaluated by analyzing the area of resorption pits on the Osteo Assay Surface plates and the expression of cathepsin K. Furthermore, the number of tartrate-resistant acid phosphatase (TRAP)-positive multinucleated cels, nuclei per osteoclasts and the expression of integrinανβ3were detected.
RESULTS AND CONCLUSION:The area of resorption pits and expression of cathepsin K in AGEs groups were significantly decreased compared withthecontrol group, and this inhibiting effect became more obvious with the increase of AGEs concentration. TRAP staining also showed that number of TRAP-positivemultinucleated celsand nuclei per osteoclast were significantly reduced in an AGE dose-dependent manner. Quantitative PCR revealed that the expression of integrin ανβ3decreased significantly with the extension of AGEs incubation time. These data indicate that AGEs can exert inhibitory effects on organic and inorganicmatrixdegradation induced by osteoclasts. The underlying mechanism may be involved in the inhibitory effects of AGEs on directed differentiation and cel fusion of osteoclast precursor cels, and migration and adhension of osteoclasts.

Objective To compare the prediction effect of combined model and single model in HFRS incidence fitting and prediction, and to provide a reference for optimizing HFRS prediction model. Methods The province with the highest incidence in China (Heilongjiang Province) in recent years was selected as the research site. The monthly incidence data of HFRS in Heilongjiang Province from 2004 to 2017 were collected. The data from 2004 to 2016 was used as training data, and the data from January to December 2017 was used as test data. The training data was used to train SARIMA , ETS and NNAR models, respectively. The reciprocal variance method and particle swarm optimization algorithm (PSO) were used to calculate the model coefficients of SARIMA, ETS and NNAR, respectively, to construct combined model A and combined model B. The established models were used to predict the incidence of HFRS from January to December 2017. The fitted and predicted values of the five models were compared with the training data and test data, respectively. Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Standard Deviation (RMSE), and Mean Error Rate (MER) were used to evaluate the model fitting and prediction effects. Results The optimal SARIMA model was SARIMA(1,0,2)(2,1,1)₁₂. The optimal ETS model was ETS(M, N, M), and the smoothing parameter =0.738，=1*10. The optimal NNAR model was NNAR(13,1,7)₁₂. The residuals of the three single models were white noise (P>0.05). The expression of combined model A was ŷ=0.134*y_SARIMA+0.162*y_ETS+0.704*y_NNAR; the expression of combined model B was ŷ=0.246*y_SARIMA+0.435*y_ETS+0.319*y_NNAR. The MAPE, MAE, RMSE, and MER fitted by SARIMA, ETS, NNAR, combined model A and combined model B were 24.10%, 0.11, 0.17, 23.29%; 17.14%, 0.08, 0.14, 17.96%; 6.33%, 0.02, 0.03, 4.25%; 9.03%, 0.03, 0.05, 7.51%; 13.16%, 0.06, 0.09, 12.33%, respectively. The MAPE, MAE, RMSE, and MER predicted by the five models were 18.70%, 0.05, 0.06, 19.62%; 23.83%, 0.06, 0.07, 24.49%; 28.30%, 0.07, 0.10, 29.21%; 21.69%, 0.06, 0.08, 22.63%; 17.39%, 0.05, 0.07, 18.76%, respectively. Conclusion The fitting and prediction effects of the combined models are better than the single models. The combined model based on PSO to calculate the weight of the single model is the optimal model.

Since the outbreak of the COVID-19 at the end of December 2019, Hubei province has actively adopted a series of prevention and control measures such as ^“quarantine, treatment, testing, and containment^”, and have basically blocked the spread of COVID-19. However, with the development of overseas epidemics and the occurrence of case clusters in local areas, we not only face the threat of imported cases, but also face the urgent need to resume normal work and daily life. This puts forward higher requirements for regular prevention and control of COVID-19. Therefore, we should more deeply understand the significance of regular prevention and control as well as the epidemic situation in our province, summarize experience and lessons, and adhere to the prevention and control strategy of ^“government-led, group-specialist combination, and specialized-oriented^”. Meanwhile, it is necessary to implement the working requirement that combines regular prevention and control surveillance with rapid emergency response to local COVID-19 outbreaks. Furthermore, we should establish a regular multi-point trigger early warning mechanism for COVID-19, strengthen the reserve of emergency supplies and carry out training and drills on epidemic prevention and control across the province to make full preparations for the coming autumn and winter epidemics. The most important is to reform the system of disease prevention and control and public health, comprehensively improve the ability of prevention and treatment, and promote the modernization of public health governance.

Objective To explore the applicability of the TBATS in predicting the incidence of mumps. Methods The incidence of mumps of Jiangxi Province from 2004 to 2017 was used as the demonstration data. The incidence of mumps in Jiangxi Province from July to December 2017 was used as test data. The training data from January 2004 to June 2017 were used to train the TBATS and the SARIMA, and predict the value from July to December 2017. The fitted and predicted values were compared with the test data. The MAPE, RMSE, MAE and MER were used to evaluate model fitting and prediction effects. Results SARIMA (1,0,0)(1,1,0)₁₂ with drift was the optimal SARIMA. The MAPE, MAE, RMSE and MER fitted by the TBATS and the SARIMA were 15.06%, 0.21, 0.29, 13.57% and 21.93%, 0.29, 0.41, 18.73%, respectively. The MAPE, MAE, RMSE and MER predicted by the TBATS and the SARIMA were 7.95%, 0.08, 0.11, 7.12% and 15.33%, 0.17, 0.18, 14.93%. Conclusion The TBATS has high accuracy in predicting the incidence of mumps and is worthy of popularization and application.

Objective To understand the epidemiological characteristics of the novel coronavirus diseases 2019 (COVID-19), and to scientifically guide the prevention and control of COVID-19 in Hubei Province. Methods All COVID-19 cases reported online in Hubei Province as of March 31, 2020 were extracted from Hubei^'s Infectious Disease Information System. The epidemic curve, age and sex characteristics, and spatiotemporal distribution characteristics of the COVID-19 cases were analyzed. Results As of March 31, 2020, a total of 70 764 cases were reported in Hubei Province, including 49 195 confirmed cases. A total of 4 579 deaths occurred among the confirmed cases, and the reported case fatality rate was 6.47%. The peak of the onset of symptoms occurred from January 20 to February 14, 2020. The sex ratio of male to female of the confirmed cases was 0.99: 1, and most were 30-69 years old. The cases diagnosed before January 5 were mainly reported by Wuhan City. From January 6 to January 31, all counties and districts in the province reported that the incidence of confirmed COVID-19 cases began to rise, and about 50% counties reported that the morbidity rate of confirmed COVID-19 cases was over 10 cases per 100 000. The morbidity rate of COVID-19 cases rose rapidly between February 1-15, and then gradually reached its peak after February 16. Conclusion Wuhan City of Hubei Province first discovered and reported the COVID-19 outbreak. The onset of symptoms peaked in January 20 to February 14, and the 30-69 years old group was the key population. Many measures such as restricting personnel movement, reducing contact, and strengthening health education played an important role in controlling the outbreak of COVID-19 in Hubei.