Objective: To analyze the epidemiological characteristics of category C intestinal infectious diseases among children and adolescents in Shenzhen from 2012 to 2024 and the association with meteorological factors, so as to provide a scientific basis for the targeted prevention and control of infectious diseases for children and adolescents. Methods: Using data from the "Infectious Disease Reporting Information Management System" of the "China Disease Prevention and Control Information System" covering the period from January 1, 2012 to December 31, 2024, the study analyzed clinical and confirmed cases of hand, foot, and mouth disease, other infectious diarrhea, and acute hemorrhagic conjunctivitis among individuals aged 6-19 years old to describe demographic and temporal characteristics. It used Joinpoint regression to calculate the average annual percent change (AAPC) and annual percent change (APC) to analyze incidence trends, and Spearman s correlation was combined to generalize linear models so as to assess the association between category C intestinal infectious diseases and meteorological factors. Results: From 2012 to 2024, a cumulative total of 61 019 cases of hand, foot, and mouth disease among children and adolescents, 58 498 cases of other infectious diarrhea, and 6 377 cases of acute hemorrhagic conjunctivitis were reported. The AAPC in the incidence rates of these three diseases was 19.19%, 31.03% and 31.48 %, respectively(all P <0.05). Notably, the incidence of hand, foot, and mouth disease increased significantly after 2022 (APC= 133.66 %, P <0.01). The temporal distribution showed that hand,foot,and mouth disease was most prevalent in May,June and July (seasonal index of 2.39,3.64,1.97), other infectious diarrhea was most prevalent in February,March and December (seasonal index of 1.22,1.25,1.47), and acute hemorrhagic conjunctivitis peaked in September and October (seasonal index of 4.22,2.16). Monthly average temperature could increase the risk of hand,foot,and mouth disease( β = 0.18 ,95% CI =0.11-0.25); as monthly average wind speed increased, the incidence of other infectious diarrhea ( β =-0.86, 95% CI = -1.50 to -0.22) and acute hemorrhagic conjunctivitis ( β =-1.32, 95% CI =-2.60 to -0.05) both decreased (all P < 0.05 ). Conclusions Among children and adolescents in Shenzhen, category C intestinal infectious diseases remain prevalent throughout the year；the number of reported hand, foot, and mouth disease cases has shown an upward trend in recent years.Temperature and wind speed significantly affect the number of reported cases of three types with category C intestinal infectious diseases.

The aim of this study was to investigate the mechanism of quercetin in inhibiting the fer-roptosis induced by zearalenone(ZEN)via mediating the nuclear factor erythroid-2-related factor 2(Nrf2)/glutathione peroxidase 4(GPX4)signaling pathway.IPEC-J2 cells were cultured in vitro and treated with ZEN(25 mg/L)and different concentrations of quercetin(10,20,40 μmol/L).Bi-ochemical methods were used to detect the levels of lactate dehydrogenase(LDH)in the cell cul-ture medium supernatant,total antioxidant capacity(T-AOC),superoxide dismutase(SOD),glu-tathione(GSH),and malondialdehyde(MDA).Fluorescence probes were used to detect the levels of Fe2+,reactive oxygen species(ROS),and lipid peroxidation.Western blot was performed to de-tect the expression levels of Nrf2,long chain acyl CoA synthetase 4(ACSL4),and GPX4.The IPEC-J2 cells were divided into the control group,ZEN group,quercetin group,and ML385(Nrf2 inhibitor)+quercetin group for further analysis.Except for the control group,the other groups were treated with ZEN in the prescence of quercetin and ML385.The changes of Nrf2/GPX4 path-way-related proteins and ferroptosis-related indexes(LDH,Fe2+,MDA,and GSH)were detected.Compared with the control group,IPEC-J2 cells in the ZEN group exhibited a decrease in cell via-bility,T-AOC,and GSH,SOD levels,Nrf2,and GPX4 protein expressions(P＜0.05),while LDH release rate,Fe2+and ROS,lipid peroxidation,MDA levels,and ACSL4 protein expression de-creased in the ZEN group(P＜0.05).Compared with ZEN group,the cell viability,the levels of T-AOC,SOD and GSH,the protein expression of Nrf2 and GPX4 in quercetin groups were increased(P＜0.05),while the LDH release rate,the levels of Fe2+,ROS,lipid peroxidation,and MDA as well as the protein expression of ACSL4 were decreased(P＜0.05).Compared with the quercetin group,the protein expression of Nrf2 and GPX4 and the level of GSH in ML385+quercetin group reduced(P＜0.05),the LDH release rate and the levels of Fe2+and MDA increased(P＜0.05).In summary,quercetin could inhibit ZEN-induced ferroptosis of IPEC-J2 cells,and its mechanism may be related to the induction of Nrf2/GPX4 signaling pathway.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

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 temporal distribution of other infectious diarrhea (OID) in Shenzhen and provide evidence for the prevention and control of OID.Methods:The incidence data of OID in Shenzhen from 2011 to 2023 were collected. The seasonal and trend decomposition using loess (STL), seasonal index method, concentration degree and circular distribution method were used to analyze the incidence trend and temporal distribution of OID.Results:A total of 477 611 cases of OID were reported in Shenzhen from 2011 to 2023, with an average annual incidence rate of 260.19/100 000 showing a fluctuating upward trend. The seasonal index method indicated that October-January was period with high incidence of OID in Shenzhen and the seasonal intensity began to decrease in 2020. STL revealed an obvious incidence peak in winter. The concentration method showed that OID had a certain seasonality before 2018 except 2016, but the seasonality was not obvious after 2018. The circular distribution results showed that r was 0.05, mean angle ā was 1.92° and angular standard deviation s was 141.93° ( Z=1 033.37, P<0.001), with the peak on January 1 st and the high incidence period from August 11 th to May 25 th. Conclusions:OID had a certain degree of seasonality in Shenzhen, with an obvious incidence peak in winter. Since the seasonal intensity of OID decreased after 2018, the surveillance, early warning and risk assessment of OID should be continued, and prevention and control measures should be adjusted timely according to the change in the characteristics of the epidemic.

Objectives:To analyze the epidemiological characteristics and incidence trends of chronic hepatitis B in Shenzhen from 2005 to 2023, develop a prediction models with performance evaluation, explore its associations with social demographic index (SDI) and inform targeted prevention strategy development.Methods:Based on surveillance data of infectious diseases, descriptive epidemiological methods were applied to analyze the spatiotemporal and population distribution characteristics. A multifactorial prediction model integrating the SDI was established, and its predictive performance was evaluated by using data from 2020-2023. Model accuracy was evaluated by using root mean square error and mean absolute percentage error ( MAPE). The association between SDI and incidence rates was assessed through generalized linear models. Results:A total of 235 703 chronic hepatitis B cases were reported cumulatively in Shenzhen from 2005-2023, with an annual average incidence rate of 98.84/100 000. Long-term trends revealed a significant increase in the incidence from 2005 to 2019. The incidence rate was 2.48 times higher in men than in women, and the majority of cases occurred in age group 20-50 years. The cases were mainly workers in manufacturing and services. Seasonal incidence peaks were observed in March and during May to November. The overall SDI exhibited a consistent upward trend, and the positive correlation between SDI and incidence rate was observed in central urban districts (Futian and Nanshan). In contrast, industrial zones (Guangming and Bao'an) saw a significant decline in incidence rates due to intensified prevention interventions despite the increase of SDI level. Model predictions indicated that the multivariate long short-term memory (LSTM) deep learning model integrating SDI parameters outperformed both the spatiotemporal covariate- enhanced model and the augmented Bayesian structural time series model, with MAPE of 4.71%, 7.66% and 10.30%, respectively. Conclusion:SDI is a key social determinant associated with hepatitis B transmission risks, and dynamic thresholds can be established to develop tiered early warning mechanisms. It is suggested to integrate multisource SDI data into the LSTM framework, implement targeted interventions such as "rapid antibody screening in key areas + vaccination boosters for high-risk populations" and improve the timeliness of epidemic response through hybrid models to reduce disease burden level.

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 temporal distribution of other infectious diarrhea (OID) in Shenzhen and provide evidence for the prevention and control of OID.Methods:The incidence data of OID in Shenzhen from 2011 to 2023 were collected. The seasonal and trend decomposition using loess (STL), seasonal index method, concentration degree and circular distribution method were used to analyze the incidence trend and temporal distribution of OID.Results:A total of 477 611 cases of OID were reported in Shenzhen from 2011 to 2023, with an average annual incidence rate of 260.19/100 000 showing a fluctuating upward trend. The seasonal index method indicated that October-January was period with high incidence of OID in Shenzhen and the seasonal intensity began to decrease in 2020. STL revealed an obvious incidence peak in winter. The concentration method showed that OID had a certain seasonality before 2018 except 2016, but the seasonality was not obvious after 2018. The circular distribution results showed that r was 0.05, mean angle ā was 1.92° and angular standard deviation s was 141.93° ( Z=1 033.37, P<0.001), with the peak on January 1 st and the high incidence period from August 11 th to May 25 th. Conclusions:OID had a certain degree of seasonality in Shenzhen, with an obvious incidence peak in winter. Since the seasonal intensity of OID decreased after 2018, the surveillance, early warning and risk assessment of OID should be continued, and prevention and control measures should be adjusted timely according to the change in the characteristics of the epidemic.

Objectives:To analyze the epidemiological characteristics and incidence trends of chronic hepatitis B in Shenzhen from 2005 to 2023, develop a prediction models with performance evaluation, explore its associations with social demographic index (SDI) and inform targeted prevention strategy development.Methods:Based on surveillance data of infectious diseases, descriptive epidemiological methods were applied to analyze the spatiotemporal and population distribution characteristics. A multifactorial prediction model integrating the SDI was established, and its predictive performance was evaluated by using data from 2020-2023. Model accuracy was evaluated by using root mean square error and mean absolute percentage error ( MAPE). The association between SDI and incidence rates was assessed through generalized linear models. Results:A total of 235 703 chronic hepatitis B cases were reported cumulatively in Shenzhen from 2005-2023, with an annual average incidence rate of 98.84/100 000. Long-term trends revealed a significant increase in the incidence from 2005 to 2019. The incidence rate was 2.48 times higher in men than in women, and the majority of cases occurred in age group 20-50 years. The cases were mainly workers in manufacturing and services. Seasonal incidence peaks were observed in March and during May to November. The overall SDI exhibited a consistent upward trend, and the positive correlation between SDI and incidence rate was observed in central urban districts (Futian and Nanshan). In contrast, industrial zones (Guangming and Bao'an) saw a significant decline in incidence rates due to intensified prevention interventions despite the increase of SDI level. Model predictions indicated that the multivariate long short-term memory (LSTM) deep learning model integrating SDI parameters outperformed both the spatiotemporal covariate- enhanced model and the augmented Bayesian structural time series model, with MAPE of 4.71%, 7.66% and 10.30%, respectively. Conclusion:SDI is a key social determinant associated with hepatitis B transmission risks, and dynamic thresholds can be established to develop tiered early warning mechanisms. It is suggested to integrate multisource SDI data into the LSTM framework, implement targeted interventions such as "rapid antibody screening in key areas + vaccination boosters for high-risk populations" and improve the timeliness of epidemic response through hybrid models to reduce disease burden level.