Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Objective:To explore the effect of cognitive enhancement and self-management intervention led by specialist nurses in patients with diabetic retinopathy (DR) .Methods:From July 2020 to June 2021, a total of 116 DR patients in Ophtalmology Department of the First Affiliated Hospital of Zhengzhou University were selected using convenience sampling method and randomly divided into the control group and the observation group with 58 cases in each group. The control group conducted conventional nursing management, and the observation group carried out cognitive enhancement and self-management intervention led by specialist nurses on the basis of the control group for 3 months. Before discharge and 3 months after discharge, the quality of life and self-care ability of the two groups were compared.Results:After discharge, the fasting plasma glucose, postprandial 2 hours blood glucose, glycosylated hemoglobin, and quality of life scores of the two groups were lower than those before discharge, while self-care ability scores of the two groups were higher than those before discharge. There were significant differences in the indexes of the observation group before and after intervention ( P<0.05) . After intervention, fasting blood glucose, 2 hours postprandial blood glucose, glycosylated hemoglobin and quality of life scores of the observation group were lower than those of the control group, while the score of self-care ability was higher than that of the control group, with statistically significant differences ( P<0.05) . Conclusions:Cognitive enhancement and self-management intervention led by specialist nurses are beneficial to blood glucose control in DR patients, and can effectively improve their self-care ability and quality of life.

Objective To evaluate the application of acellular dermal matrix (ADM) in the reconstruction of defect after head and neck tumor resection.Methods A total of 75 cases applied with ADM after head and neck tumor resection were reviewed.There were 47 cases of laryngeal carcinoma (glottic 41,supraglottic 3,and infraglottic 3),5 cases of hypopharyngeal carcinoma,11 cases of maxillary sinus carcinoma,9 cases of oral cavity tumor,3 cases of cervical trachea tumor.Among them 25 cases of laryngeal carcinoma,4 cases of hypopharyngeal carcinoma,7 cases maxillary carcinoma,6 cases oral carcinoma,and one case of cervical trachea malignancy received postoperative adjuvant chemoradiotherapy.The patients were postoperatively followed up from 2 weeks to 3 years.Results Among 75 cases,65 cases,including 40 cases of laryngeal carcinoma,4 cases of hypopharyngeal carcinoma,9 cases of oral cavity tumor,9 cases of maxillary carcinoma and 3 cases trachea tumor,presented with good wound healing without any complication within 3 months;the 47 patients with tracheotomy were decannulated successfully;2 cases of laryngeal carcinoma and 1 case of hypopharyngeal carcinoma were complicated with pharyngeal fistula,which healed with dressing change,followed by decannulation;5 cases had laryngeal stenosis and decannulation failed in these patients;and 3 cases of maxillary carcinoma had wound infection.Conclusion ADM application is a easy and feasible method for the repair of the defect after head and neck tumor resection.