Object To establish a SDS PAGE method on quality control of melittin. Methods Melittin was extracted and purified from bee venom by gel chromatography. Purity and molecular mass of melittin were determined by SDS PAGE. Results In the gelatinous board, the sample was a single peptide chain with a molecular mass of about 2 800 dalton as revealed by SDS PAGE. The purity was (90 59?0 85)%, which was in parallel with the result tested by HPLC. Conclusion SDS PAGE is accurate, reliable and simple for the determination of melittin.

Objective To study the reversal effect on acquired multidrug resistance (MDR) of human hepatocellular caricinoma (HCC) by Ganli Injection. Methods Proliferative inhibition rate of HCC BEL-7402/5-Fu cell line and reversal index of 5-Fu acquired MDR were measured by MTT assay. Cell morphological changes were observed under inverted microscope and Wright-Giemsa stain method. Fluorescence intensity of Adriamycin (ADM) was detected by Flow cytometry. Results HCC BEL-7402/5-Fu cell line exhibited cross-resistance to general chemotherapeutics in clinic. Proliferative inhibition rates of HCC BEL-7402/5-Fu cell line treated for 72 h by Ganli Injection at the (matrine) concentration of 0.230, 0.115, 0.058 mg/mL were (9.25?2.38)%, (8.46?1.90)%, and (4.23?2.05)%, respectively. The corresponding reversal indexes to 5-Fu were 5.50, 3.83, and 2.25. Pre-treatment of Galli Injection at the concentration of 0.230 mg/mL for 2 h could increase the accumulation of ADM in cells to 38.61%.Conclusion Ganli Injection has the effect on reversing 5-Fu acquired MDR of human hepatocellular carcinoma in vitro.

Objective: To establish a risk assessment index system for epidemic infectious diseases in primary and secondary schools, for early warning and prevention of school infectious diseases accurately and timely. Methods: Through literature review, the indicators pool was established by consulting relevant experts and referring to practical experience. Then two rounds of expert consultation using the Delphi method were conducted(13 experts of round 1, and 20 experts of round 2). The concentration of expert opinions (the average, the percentage of full marks) and the coordination of expert opinions (the coefficient of variation, the coordination coefficient W ) were calculated and analyzed. The coordination coefficient W was obtained by nonparametric Kendall s W test of multiple samples. According to the experts feedback, necessary addition, reduction and modification of the indicators were carried out to determine the evaluation index system, and the analytic hierarchy process (AHP) was used to calculate the weight of each indicator. Results: The expert authority coefficients of the two rounds of consultation were 0.89 and 0.88, respectively, and the form recovery rate was 100%. The range of the variation coefficient of each three level indicator was 0.07-0.31 and 0.06-0.19, and the coordination coefficient W was 0.25 and 0.47, respectively, indicating that the consistency and credibility of expert opinions were good. Finally, the risk assessment index system of infectious diseases in primary and secondary schools was established, which was composed of 5 first level indexes, 23 second level indexes and 86 third level indexes. The AHP was used to calculate the intra level weight of the index. The variation coefficient of five first level indicators of school health management and security system, school health personnel facilities, health monitoring and cognition level, severity and coping measures were 0.09, 0.06, 0.08, 0.12 and 0.06 , respectively, and the weight coefficients were 20.42%, 21.19%, 19.87%, 17.45% and 21.08%, respectively. Conclusion The risk assessment index system of school infectious diseases is applicable to primary and secondary schools. The initiative, coordination and authority of experts in this system are all high. It is of practical guiding significance for the early warning of infectious diseases in primary and secondary schools.

Objective: To investigate incidence of traffic injury and its associated factors among secondary school students in Shanghai, so as to provide scientific basis for intervention. Methods: Using the method of multi stage stratified random cluster sampling, an on-the-spot anonymous questionnaire survey was conducted among 19 629 adolescents from 16 districts in Shanghai from May to June 2021. The multivariate Logistic regression was used to analyze possible associated factors. Results: The rate of road traffic injuries among secondary school students in Shanghai was 0.74%, the highest reporting rate among secondary vocational school students (1.35%), followed by junior high school students (0.72%), and the lowest reporting rate of road traffic injuries among senior high school students (0.55%), with statistically significant differences ( χ 2=13.39, P <0.01). The results of multivariate Logistic regression analysis showed that the junior high school ( OR =0.56, 95% CI =0.35-0.89) or senior high school ( OR = 0.40 , 95% CI =0.24-0.68) and residential school ( OR =0.39, 95% CI =0.18-0.88) were protective factors of road traffic injuries( P <0.05). Male ( OR =1.65, 95% CI =1.14-2.37), unaware of seat belt ( OR =6.38, 95% CI =2.81-14.50), had a cycling chase/fight ( OR =2.33, 95% CI =1.39-3.90), music or phone calls on a bicycle ( OR =2.54, 95% CI =1.72-3.75) were positively correlated with road traffic injury( P <0.05). Conclusions The road traffic injury among secondary school students in Shanghai is related to many factors, and traffic hazards are common among students in all school sections. Targeted measures should be taken to strengthen traffic safety knowledge education and intervention in healthy behaviors among adolescents, in order to reduce the incidence of road traffic injury.

Objective: To understand the relationship between stress and Internet addiction among middle school students in Shanghai, so as to provide a scientific basis for promoting students mental health and preventing Internet addiction. Methods: From May to June 2021, a stratified random cluster sampling method was used to select 6 123 middle and high school students in Shanghai for health risk behavior monitoring. Daily Stressors Evaluation Scale for Urban Secondary School Students was used to evaluate students' stress, and the Internet Addiction Test compiled by Young was used to evaluate students Internet addiction. The correlation between student stress and Internet addiction was analyzed by Kruskal-Wallis H test , Chi square test and multiple Logistic regression. Results: Total stress score of middle school students in Shanghai was 24 (12, 39), academic stress score was 8 (5, 13), physical and psychological stress score was 6 (2, 10), interpersonal stress score was 5 (1, 9), and family stress score was 4 (1, 8). The detection rate of Internet addiction was 4.7%. Multivariate Logistic regression analysis showed that the risk of Internet addiction among middle school students with high levels of stress was 8.05 times(95% CI =4.59-14.12) that of students with low levels of stress( P <0.05). The risk of Internet addiction among middle school students with high levels of academic stress, physical and psychological stress, interpersonal stress and family stress was 5.98(95% CI =3.69-9.70), 6.92(95% CI =4.03-11.88), 4.85(95% CI =3.11-7.55), and 4.18(95% CI =2.73-6.40) times that of students with low levels of stress, respectively( P <0.05). Conclusion The academic stress, physical and psychological stress, interpersonal stress, and family stress among middle school students can all lead to an increased risk of Internet addiction.

Objective: To explore the current status and associated factors of bullying behavior among middle school students in Shanghai, so as to provide a reference basis for carrying out bullying intervention work. Methods: From May to June 2021, a stratified cluster sampling method was used to select 19 000 middle school students from 63 schools in 16 districts of Shanghai, and the Shanghai Youth Health Risk Behavior Survey Questionnaire was used to conduct an anonymous questionnaire survey of enrolled students. Chi square test and binary Logistic regression analysis were employed to investigate the associated factors of bullying among middle school students. Results: The prevalence of bullying behaviors in Shanghai was 15.5%, with males and junior high school students exhibiting the higher reporting rate(19.5%,17.2%). And malicious teasing or name calling had the highest reported rate at 9.4%. The results of binary Logistic regression analysis showed that fighting ( OR =5.02), attempting to smoke ( OR =3.22), having a feeling of sadness and hopelessness ( OR =2.50) and getting drunk( OR =1.72) were positively associated with bullying behavior among middle school students. Fighting ( OR =3.83-8.97), attempting to smoke ( OR =2.92-5.52), having a feeling of sadness and hopelessness ( OR =2.40-4.34), and getting drunk ( OR =1.66-2.34) were positively correlated with 6 forms of bullying (malicious teasing or name calling, intentionally damaging someone else s belongings, deliberately excluding someone from activities or isolating someone, threatening or intimidating others, hitting, kicking, or pushing someone, and verbally harassing or attacking someone online) ( P <0.05). Conclusions Bullying behavior of middle school students in Shanghai primarily presents as verbal harassment. In the future, greater attention should be directed towards bullies, and it should recognize potential hazards promptly and implement precise intervention measures.

Objective: To explore the association between dietary behaviors and sleep duration among middle school students in Shanghai, so as to provide reference for interventions targeting insufficient sleep. Methods: From May to June 2021, a stratified cluster random sampling method was employed to select a sample of 10-17yearold middle school students for monitoring their healthrisk behaviors. A total of 5 538 valid questionnaires were collected. The survey included items such as daily sleep duration, weekly consumption of sugary beverages, freshly squeezed fruit juice, fresh fruits, fresh vegetables, fried foods, milk and yogurt, breakfast habits, and frequency of eating outside. Statistical analysis was conducted using Chisquare test, Wilcoxon ranksum test, and multivariable Logistic regression model. Results: About 73.7% of middle school students reported insufficient sleep in Shanghai. There was a positive correlation between the average daily consumption of fresh fruits and breakfast consumption with sleep duration. In other words, a higher frequency of consuming fresh fruits (OR=1.29) and eating breakfast (OR=1.07) were associated with a higher likelihood of sufficient sleep. Conversely, there was a negative correlation between the frequency of consuming desserts (OR=0.78) and fried foods (OR=0.88) and sleep duration (P<0.05). Conclusions Increasing the consumption of fresh fruits and maintaining regular breakfast habits while reducing the intake of fried foods can contribute to achieving sufficient sleep among middle school students. When implementing interventions to improve sleep among middle school students, promoting healthy and balanced diets can be considered as one of the intervention strategies.

Objective: To investigate the distribution characteristics and related factors of weight loss behavior among middle school students in Shanghai, so as to provide a reference for guiding scientific weight loss among middle school students. Methods: From May to June 2021, a stratified cluster random sampling method was used to select 16 758 junior and high school students in 16 districts of Shanghai. Youth Risk Behavior Surveillance System was administered to assess the basic condition and weight loss behaviors of the students. An unordered multinomial Logistic regression model was employed to analyze the factors associated with weight loss behaviors. Results: A total of 5 881 (35.09%) reported engaging in exercise for weight loss, 6 344 (37.86%) reported dieting for weight loss, and 461 (2.75%) engaged in unhealthy weight loss behaviors. The unordered multinomial Logistic regression analysis indicated that compared with the no weight loss behavior group, students from urban areas( OR =1.35,95% CI =1.10-1.66), those with Internet addiction ( OR =1.71,95% CI =1.23-2.38), those with victims of bullying ( OR =2.09, 95% CI =1.68-2.61), those experiencing insomnia ( OR =2.33,95% CI = 1.74-3.11), those feelings of sadness or despair ( OR =3.10, 95% CI =2.42- 3.97 ), and those who perceived their body weight as slightly heavy ( OR =2.77, 95% CI = 2.17-3.55) or very heavy ( OR =3.41, 95% CI =2.44-4.75) were more likely to engage in unhealthy weight loss behaviors ( P <0.05). Conclusions There are significant differences in weight loss behaviors among middle school students with varying characteristics in Shanghai. Negative emotions such as insomnia and feelings of sadness or despair, Internet addiction, cognitive bias in weight and experiences of bullying are identified as related factors for unhealthy weight loss behaviors. Targeted intervention measures should be implemented to guide students towards scientific approaches to weight management.

Objective: To analyze the temporal variation patterns of sickness absenteeism among primary and secondary school students in Shanghai, so as to explore models suitable for predicting peaks and intensity of absenteeism rates. Methods: The seasonal and trend decomposition using loess (STL) method was used to analyze the seasonal and long term trend changes in sickness absenteeism among primary and secondary school students from September 1 in 2010 to June 30 in 2018, in Shanghai. A hierarchical clustering method based on Dynamic Time Warping (DTW) was employed to classify absenteeism symptoms with similar temporal patterns. Based on historical data, the study constructed and evaluated different time series algorithms and machine learning models to optimize the accuracy of predicting the trend of sickness absenteeism. Results: During the research period, the average new absenteeism rate due to illness was 16.86 per 10 000 person day for every academic year, and the trend of sickness absenteeism exhibited both seasonality and a long term upward trend, reaching its highest point in the 2017 academic year (22.47 per 10 000 person day). The symptoms of absenteeism were divided into three categories: high incidence in winter and spring (respiratory symptoms, fever and general discomfort, etc.), high incidence in summer (eye symptoms, nosebleeds, etc.) and those without obvious seasonality (skin symptoms, accidental injuries, etc.).The constructed time series models effectively predicted the trend of absenteeism due to illness, although the accuracy of predicting peak intensity was relatively low. Among them, the multi layer perceptron (MLP) model performed the best, with an root mean squared error (RMSE) of 8.96 and an mean absolute error (MAE) of 4.37, reducing 36.51% and 39.02% compared to the baseline model. Conclusion Time series models and machine learning algorithms could effectively predict the trend of sickness absenteeism, and corresponding prevention and control measures can be taken for absenteeism caused by different symptoms during peak periods.

Objective: To elucidate the association between biorhythm disorders and health risk behaviors in adolescence, so as to provide reference for appropriate interventions. Methods: From March to April 2023, 2 381 adolescents in Shanghai were selected as research objects using convenience sampling and stratified random cluster sampling methods. The Self rating Questionnaire of Biological Rhythm Disorders for Adolescents (SQBRDA) and the self report health risk behaviors questionnaire were used to investigate the status of adolescent biorhythm disorders and nine kinds of health risk behaviors, while a multivariate Logistic regression model was employed to analyze the association between the two variables. Results: The average SQBRDA score was (68.25±0.42) The incidence and detection rates of health risk behaviors in the groups with no co occurrence, mild co occurrence, moderate co occurrence, and severe co occurrence were 234(9.83%), 1 176(49.39%), 830(34.86%) and 141(5.92%), respectively. The total SQBRDA score was positively correlated with the risk of co occurrence of health risk behaviors. The risk of mild co occurrence, moderate co occurrence, and severe co occurrence of health risk behaviors was 9.05 times (95% CI =4.25-19.15, P <0.01), 44.55 times (95% CI =20.75-96.05, P <0.01) and 110.05 times (95% CI =40.65-297.95, P <0.01) higher, respectively, among adolescents with higher scores of biorhythm disorders compared to adolescents with lower scores of biorhythm disorders. Conclusions Health risk behaviors among adolescents in Shanghai draw attention to a serious phenomenon whereby biorhythm disorders are positively correlated with the risk of co occurrence. Comprehensive interventions aimed at addressing adolescent health risk behaviors should focus on regulating biorhythm disorders.