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.

BACKGROUND:Many recent studies have shown a close relationship between inflammatory cytokines and osteoporosis and bone mineral density(BMD).However,the causal relationship between inflammatory cytokines and BMD has not been fully revealed. OBJECTIVE:To explore the potential causal relationship between inflammatory cytokines and BMD using a two-sample Mendelian randomization analysis. METHODS:The single nucleotide polymorphisms associated with 41 circulating inflammatory cytokines were selected from the open database of genome-wide association studies(GWAS)as instrumental variables.The GWAS data about BMD were from the Genetic Factors for Osteoporosis Consortium,involving a total of 32 735 individuals of European ancestry.Inverse variance weighting was used as the primary analysis to evaluate the causal effect.Weighted median,MR Egger regression,simple mode,and weighted mode methods were used to supplement the explanation.We used the MR-Egger intercept and MR-PRESSO method to conduct a pleiotropy test,the Cochran's Q test was used to determine whether there was heterogeneity in the results,and the leave-one-out method was used to evaluate the stability of the results.In addition,to more accurately assess the causality,the Bonferroni-corrected test was used to identify inflammatory cytokines that have a strong causal relationship with BMD. RESULTS AND CONCLUSION:(1)According to the results of the inverse variance weighting method,we found a positive causal relationship between interleukin-8 and lumbar spine BMD[β=0.075,95%confidence interval(CI):0.033-0.117,P=0.000 5),while a negative causal relationship between interleukin-17 and lumbar spine BMD(β=-0.083,95%CI:-0.152 to-0.014,P=0.018).There might be a negative causal relationship between tumor necrosis factor b and femoral neck BMD(β=-0.053,95%CI:-0.088 to-0.018,P=0.003),while a positive causal relationship between basic fibroblast growth factor and femoral neck BMD(β=0.085,95%CI:0.016-0.154,P=0.015).There might be a negative causal relationship between macrophage inflammatory protein-1a and total body BMD(β=-0.056,95%CI:-0.105 to-0.007,P=0.025).There was a negative causal relationship between interleukin-5(β=-0.019,95%CI:-0.031 to-0.006,P=0.004),stromal cell-derived factor-1a(β=-0.022,95%CI:-0.038 to-0.005,P=0.010),hepatocyte growth factor(β=-0.021,95%CI:-0.041 to-0.002,P=0.030),interleukin-4(β=-0.016,95%CI:-0.032 to-0.001,P=0.034)and heel BMD,while a positive causal relationship between nerve growth factor(β=0.019,95%CI:0.002-0.036,P=0.033),granulocyte colony-stimulating factor(β=0.011,95%CI:0.000-0.022,P=0.050),and heel BMD.Meanwhile,after the Bonferroni-corrected test,there was a strong positive causal effect between interleukin-8 and lumbar spine BMD(P=0.000 5).And consistent directional effects for all analyses were observed in MR Egger,weighted median,simple mode,and weighted mode methods.(2)Sensitivity analyses revealed no heterogeneity,pleiotropy,or outliers for the causal effect of circulating inflammatory cytokines on BMD.

Objective To explore the value of radiomics and deep transfer learning(DTL)based on gadoxetic acid disodium-enhanced magnetic resonance imaging(MRI)for preoperative prediction of microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was conducted using the MRI and clinicopathological data of 369 HCC patients who underwent surgery and had pathologically confirmed MVI at the Affiliated Hospital of Qingdao University from January 2019 to September 2024.According to the negative and positive manifestations of MVI,these patients were divided into MVI-group(n=219)and MVI+group(n=150);and they were then randomly assigned into the training set(n=258)and the test set(n=111)in a ratio of 7:3.Based on the hepatobiliary phase images,the optimal features were extracted and screened from radiomics features,DTL features,and the fusion features of the two.Nine machine learning models were constructed using 3 algorithms(random forest,multi-layer perceptron,and support vector machine,separately)and trained on radiomics features,DTL features,and the fusion features of the two.The diagnostic efficacy of each model was evaluated using receiver operating characteristic curve,and the optimal model was identified as the output model.Results Among all the constructed models,those based on fused features outperformed models using individual features.The random forest classifier model in the training set had the best performance,with an AUC of 0.998(95%CI:0.996-1.000),and was therefore selected as the output model in this study.Conclusion Radiomics and DTL models based on gadoxetic acid disodium-enhanced MRI can effectively predict the MVI in HCC.Among these,the random forest classifier model utilizing fused features in the training set exhibits the best performance.

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 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 explore the value of radiomics and deep transfer learning(DTL)based on gadoxetic acid disodium-enhanced magnetic resonance imaging(MRI)for preoperative prediction of microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was conducted using the MRI and clinicopathological data of 369 HCC patients who underwent surgery and had pathologically confirmed MVI at the Affiliated Hospital of Qingdao University from January 2019 to September 2024.According to the negative and positive manifestations of MVI,these patients were divided into MVI-group(n=219)and MVI+group(n=150);and they were then randomly assigned into the training set(n=258)and the test set(n=111)in a ratio of 7:3.Based on the hepatobiliary phase images,the optimal features were extracted and screened from radiomics features,DTL features,and the fusion features of the two.Nine machine learning models were constructed using 3 algorithms(random forest,multi-layer perceptron,and support vector machine,separately)and trained on radiomics features,DTL features,and the fusion features of the two.The diagnostic efficacy of each model was evaluated using receiver operating characteristic curve,and the optimal model was identified as the output model.Results Among all the constructed models,those based on fused features outperformed models using individual features.The random forest classifier model in the training set had the best performance,with an AUC of 0.998(95%CI:0.996-1.000),and was therefore selected as the output model in this study.Conclusion Radiomics and DTL models based on gadoxetic acid disodium-enhanced MRI can effectively predict the MVI in HCC.Among these,the random forest classifier model utilizing fused features in the training set exhibits the best performance.

Objective To explore the the detection of MMP-2,-7,-9,and-12 enzymatic activity using the CEACAM1-derived fluorescent peptide substrate Site 84,investigating the application of substrate Site 84 to distinguishing between MMP-2 and MMP-9 in the gelatinase spectrum of MMPs.Methods The fluorescent enzymatic method was employed to observe the detection of MMP-2,-7,-9,and-12 enzymatic activity using substrate Site 84;further observations were made on the sensitivity and specificity of substrate Site 84 to enzymatic activity of MMP-2 and MMP-9 within the gelatinase spectrum;the kinetic parameters(Km and Kcat)of the enzymatic reaction between substrate Site 84 and MMP-2 were obtained.Results Using Site 84 as a substrate,enzymic kinetics curves for MMP-12,-7,-2 were obtained,but no enzymatic activity curve for MMP-9 was observed.Furthermore,Site 84 specifically detected the enzymatic activity of MMP-2 within the gelatinase spectrum,capable of detecting low concentration(0.6 μM)of MMP-2 enzymatic activity,but no obvious enzymatic reaction was observed for high concentration(6 μM)of MMP-9;the kinetics parameters for the enzymatic reaction between Site 84 and MMP-2 were Km=315 μM,Kcat/Km=2 565/MS.Conclusion The CEACAM1-derived substrate Site 84 serves as a novel fluorescent peptide substrate,enabling the acquisition of enzymatic activity curves for MMP-12,-7 and-2,and specifically detecting the enzymatic activity of MMP-2 within the MMP gelatinase spectrum.

Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.