Objective To identify subtypes of lower-grade gliomas based on multi-omics data integration with consensus clustering ensemble(MICCE)method,and further assess prognosis risk across different subtypes and explore differentially expressed biomarkers and pathways.Methods We applied the consensus clustering ensemble method to integrate the subtype results of seven multi-omics data integration methods(SNF,joint SNF,CIMLR,ConsensusClusterPlus,MoCluster,NEMO,iClusterBayes)for mRNA,miRNA,and DNA methylation data from LGG patients,identifying a robust molecular subtyping.Then we performed survival analysis based on the subtype results,and Cox proportional risk models were fitted to assess the prognosis of patients with different subtypes.Differentially expressed genes(DEmiRNAs,DEmRNAs and DMGs)between different subtypes were screened,and GO(gene ontology)analysis and KEGG enrichment analysis were performed for overlapping genes among DEmiRNAs target genes,DEmRNAs,and DMGs.Ultimately,immune infiltration analysis and pathway activity analysis were conducted to quantify the biological differences among different subtypes.Results Patients were classified into three subtypes:a high-risk cluster,a moderate-risk cluster,and a low-risk cluster.The results showed that the high-risk cluster were 7.70 times more likely to die than patients in low-risk cluster.A total of 2512 DEmRNAs,14 DEmiRNAs and 255 DMGs were screened,the combined analysis genes yielded 665 genes which are regulated by mRNA,miRNA and DNA methylation and enriched 62 GO items and 52 KECG pathways with statistical differences.The analysis of immune infiltration and pathway activity indicates that there are two immune cells and four signaling pathways with statistically significant differences.Conclusion MICCE can effectively identify high-risk patients of LGG.Subsequent analysis reveals differential genes and pathways related to the progression of LGG with different subtypes,providing important clues for the personalized treatment of LGG.

Objective To explore the association of maternal smoking,alcohol consumption and betel nut consumption during pre-pregnancy/early pregnancy with risk of preterm birth in offspring.Methods This first-trimester cohort study was conducted among pregnant women who attended their first prenatal care and were between 8 and 14 gestational weeks in seven maternal and child health hospitals in Hunan Province between August,2014 and December,2019.Information on exposure and preterm birth was collected by using an epidemiological questionnaire combined with the hospital's medical record system.The logistic regression was used to analyze the association of maternal smoking,alcohol consumption,betel nut consumption as well as their interactions and additive effects with risk of preterm birth.Results Among 34104 singleton pregnancies,the incidence of preterm birth was 11.8％(95％CI:11.5％～12.2％).Multivariate logistic regression analyses showed that maternal active smoking(OR=1.246,95％CI:1.016～1.527),passive smoking(OR=1.095,95％CI:1.010～1.187),alcohol consumption(OR=1.619,95％CI:1.372～1.911)and betel nut consumption(OR=1.381,95％CI:1.098～1.737)during pre-pregnancy/early pregnancy were significantly associated with risk of preterm birth.Results from the interaction analysis showed that there were interactions between active smoking and alcohol consumption(OR=0.260,95％CI:0.127～0.530)and between passive smoking and alcohol consumption(OR=1.558,95％CI:1.048～2.322)in the risk of preterm birth.Additive effect analysis showed that the risk of preterm birth increased with the increase of risk behaviors including active smoking,passive smoking,alcohol consumption and betel nut consumption(the range of OR:1.163 to 2.259).Conclusion Maternal active smoking,passive smoking,alcohol consumption,and betel nut consumption during pre-pregnancy/early pregnancy significantly increased the risk of preterm birth.Additionally,women with more of the above risk behaviors had a higher risk of preterm birth,suggesting the importance of keeping healthy behaviors before/during pregnancy to prevent preterm birth.

Objective To analyze the disease burden of lung cancer attributable to air pollution among the Chinese population from 1990 to 2021,providing a scientific basis for the prevention and control strategies of lung cancer.Methods Data on deaths,mortality rates,disability-adjusted life years(DALY)of lung cancer,and world population data attributable to air pollution in China from 1990 to 2021 were extracted from the global burden of disease study 2021(GBD 2021).The Joinpoint regression model was used to calculate the annual percentage change(APC)and average annual percentage change(AAPC)for analysis of disease burden.The age-period-cohort model was employed to investigate the effects of age,period,and cohort on the trends in lung cancer disease burden attributable to air pollution.Results The population attributable fraction(PAF)of lung cancer in China due to air pollution has shown a trend of decrease,with an average AAPC value of-1.16%(P<0.001).The numbers of death and DALYs generally increased by years,with an AAPC value of 2.14%and 1.54%,respectively(both P<0.001).However,the standardized mortality rate and standardized DALY rate initially increased and then decreased,with an AAPC value of 0.34%and-0.14%,respectively,showing no statistical significance.The mortality rate,standardized mortality rate,DALY rate,and standardized DALY rate of lung cancer attributable to air pollution were higher in males than in females.Age-period-cohort model analysis showed that the net drifts of lung cancer mortality rates attributable to air pollution for the overall population,males,and females were-1.52%,-1.33%,and-1.88%,respectively(all P<0.001).The death risk increased with age.There are higher mortality rates in males across all age groups compared to females,especially in the population over 50 years.The period effect showed a U-shape trend from 1990 to 2004,and a decreasing trend after 2005.There was a decreasing trend of death risk with the progression of birth cohorts.Conclusion From 1990 to 2021,the relative contribution of air pollution to lung cancer in China has gradually decreased,and the risk of lung cancer death attributable to air pollution has generally shown a downward trend.The risk is higher in males than in females,higher in elderly population,and lower with the progression of birth cohorts.Increased attention should be paid to high-risk groups such as males and the elderly population,and early screening and intervention for the disease should be strengthened to reduce the risk of lung cancer.

Objective To explore the impact of urban and rural resident basic medical insurance(URRBMI)integration policy on the health and health inequality of middle-aged and elderly people,and to provide an evidence-based basis for further improving the URRBMI system.Methods Based on data from the China health and retirement longitudinal study(CHARLS)from 2013 to 2018,the impact of the URRBMI integration policy on the self-rated health,disability,and depression of middle-aged and elderly people was investigated using the staggered differences-in-differences method.The health inequality status of middle-aged and elderly people and its change trend were analyzed by the concentration index,and the index was decomposed according to different years and different integration modes to explore the impact of URRBMI on health inequality of middle-aged and elderly people.Results A study was conducted on 7589 middle-aged and elderly individuals,which found that the URRBMI integration policy had a significant positive impact on the self-rated health of this population(OR=1.309,P<0.001).Additionally,the policy had a significant negative impact on the occurrence of depression(OR=0.696,P<0.001).However,it was found to increase the disability score of middle-aged and elderly individuals(β=0.354,P<0.001)and did not improve their disability status.In addition,health disparity exists among middle-aged and elderly people in China,and middle-aged and elderly people with health problems are mainly concentrated in groups with lower socioeconomic status,and health disparity has been increasing over time.Health insurance for urban and rural residents has a certain effect on alleviating health inequality,the contribution rate of disability inequality and depression inequality decreased from 1.21%and 1.20%in 2013 to-0.58%and 0.31%in 2018,respectively,and compared with the one-tier model,the split-tier system has a greater negative impact on the health status of low-income groups.Conclusion The URRBMI integration policy improved the self-rated health and depression status of middle-aged and elderly people,but had a limited effect on improving the disability status.The policy can reduce the health inequality of middle-aged and elderly people to some extent,but the health inequality varies widely within the population.It is recommended to strengthen the top-level design of URRBMI,to shift from a strategy that emphasizes medicine over prevention to one that emphasizes prevention and combines prevention and treatment,and to shift from formal equity based on the elimination of household boundaries to substantive equity based on the elimination of reimbursement policies and other barriers.

Objective To address the limitations of traditional nonparametric methods in analyzing ordinal data,which often overlook nonlinear characteristics and polarization trends,this study explores the application value of dominance analysis and the ANY polarization index in quantifying distributional differences and inequality.Methods Simulated Likert-scale datasets(with varying medians)and real-world data from the National Health and Nutrition Examination Survey(NHANES)were analyzed using dominance analysis and the ANY index.Their performance in revealing distributional heterogeneity and polarization was compared with conventional rank-based tests.Results Dominance analysis effectively distinguished inter-group differences in overall levels(F-dominance)and internal inequality(S-dominance)through cumulative distribution function(CDF).The ANY index captured polarization trends by adjusting parameters α(emphasizing lower percentiles)and β(emphasizing upper percentiles).In NHANES data,the new methods identified significantly higher low-end polarization in males'self-rated health,whereas traditional Mann-Whitney U tests showed no statistical significance.Conclusion Dominance analysis and the ANY index overcome the oversimplified assumptions of conventional methods,offering refined tools for analyzing ordinal data in health policy-making,particularly in optimizing resource allocation and addressing health disparities among vulnerable populations.

Objective This study aimed to explore the feasibility of using a long short-term memory(LSTM)model based on empirical mode decomposition(EMD)and singular spectrum analysis(SSA)to predict the incidence of pulmonary tuberculosis in Shanxi Province.The goal was to provide a reliable prediction method to support the prevention and control of tuberculosis epidemics in the region.Methods Collecting and collating monthly data on the reported incidence of tuberculosis nationwide from January 2007 to December 2018.The LSTM、EMD-LSTM、SSA-LSTM models were established using the reported monthly incidence of tuberculosis reported in Shanxi Province from January 2007 to December 2017 as the training set and using the reported monthly incidence of tuberculosis from January to December 2018 as the test set.Mean squared error(MSE),mean absolute error(MAE),root mean squared error(RMSE),and mean absolute percentage error(MAPE)were used to evaluate the prediction effect of the models to determine the best model.Results The MSE,MAE,RMSE and MAPE of the EMD-LSTM model in predicting the incidence trend of pulmonary tuberculosis in the next year were 0.036,0.140,0.189 and 0.045,respectively.Compared with the LSTM model,the prediction performance increased by 66.36%,38.33%,42.38%and 41.56%,respectively.Compared with the SSA-LSTM model,it improved by 28.00%,9.68%,15.25%and 16.67%,respectively.Conclusion Compared with the single LSTM model,the fitting and prediction performance of EMD-LSTM and SSA-LSTM models are improved effectively.However,the prediction effect of EMD-LSTM model is better than that of SSA-LSTM model.Therefore,the EMD-LSTM model is more suitable for predicting the incidence trend of pulmonary tuberculosis in Shanxi Province,and can provide a theoretical basis for tuberculosis prevention and control policies.

Objective In this study,clinical data of Alzheimer's disease(AD)patients,structural magnetic resonance imaging(sMRI)data,and positron emission tomography(PET)data were used to construct an auxiliary diagnostic model with good classification effects,so as to formulate a personalized treatment plan at the early stage of the patients,which is of great significance for the prevention and treatment of AD.Methods In this study,a total of 401 study subjects containing complete sMRI images and PET images were selected from the ADNI-1(Alzheimer's disease neuroimaging initiative-1,ADNI-1)database.We used statistical parameters mapping(SPM)and voxel-based morphometric(VBM)analysis of MATLAB to perform pre-processing operations such as spatial normalization and skull stripping on sMRI images and PET images of the study subjects.With the help of the brain atlas was used to segment the brain tissue structure.After that,the segmented gray matter was extracted from the corresponding brain regions based on anatomical automatic labeling,and the feature values of all brain regions were obtained.Then the extracted brain region feature values are then subjected to fisher score,support vector machine-recursive feature elimination(SVM-RFE)and least absolute shrinkage and selection operator(LASSO),a hybrid filtered-wrapped-embedded feature selection method with three different principles,to realize the dimensionality reduction of high-dimensional image data.Finally,the PAC-Bayesian strategy boosting based multi-view learning(PB-MVBoost)model is constructed based on multi-view decision fusion for clinical,sMRI and PET data.And it is compared with the traditional machine learning models support vector machine(SVM),decision tree(DT),K-nearest neighbor(KNN),random forests(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)which are constructed after concatenating views.It is compared with multi-view multi-kernel learning models(AverageMKL,EasyMKL)and multi-view confusion matrix boosting,which is also the same multi-view decision fusion.Results Among all the multi-view fusion models of AD-MCI,the PB-MVBoost model based on decision fusion has the best performance(accuracy=0.98,F1-score=0.97,precision=0.98,recall=0.96,MSE=0.07).Among all the multi-view fusion models of MCI-NC,the model performance of PB-MVBoost based on decision fusion was the best(accuracy=0.99,F1-score=0.98,precision=0.99,recall=0.98,MSE=0.05).Conclusion In the classification of AD-MCI and MCI-NC,the distinction and calibration degree of PB-MVBoost model were optimized,indicating that the auxiliary diagnosis model of Alzheimer's disease constructed by PB-MVBoost classifier based on decision fusion performed the best,which could improve the recognition of patients with mild cognitive impairment and then assist clinical diagnosis.

Objective Based on the multi-dimensional postoperative outcomes of patients with percutaneous coronary intervention(PCI)the prediction model of adverse outcomes in patients with coronary heart disease after PCI was constructed by combining multi-label deep forest algorithm.Methods 521 patients diagnosed with coronary heart disease and undergoing PCI from the Second Hospital of Shanxi Medical University were collected.Multi-label ReliefF algorithm was used to filter features,and multi-label-random oversampling algorithm was used to deal with data imbalance.Finally,multi-label deep forest algorithm(MLDF)was used to build a prediction model.Results Multi-label ReliefF was used to filter the characteristics.The results showed that B-type natriuretic peptide,creatine kinase isoenzyme,hemoglobin,homocysteine,C-reactive protein and serum indirect bilirubin were important factors affecting the prognosis of PCI.The MLROS algorithm improved the imbalance of multi-tag data to a certain extent,and the mean IR of the whole tag was reduced from 3.937 to 2.668.Conclusion In this study,the multi-label deep forest algorithm was combined with the adverse outcome of patients after PCI.At the same time,considering the problem of multi-tag feature selection and data imbalance,and fully considering the actual clinical situation,patients may have multiple outcomes at the same time after PCI,which is more in line with the requirements of modern medicine.

Objective To investigate the prevalence of metabolic syndrome(MS)and its influencing factors among adult residents in Zhifu district of Yantai city,and to provide data support and scientific basis for relevant departments to formulate policies and measures.Methods A total of 2412 residents in Zhifu district were selected by multi-stage stratified sampling method,and their basic information,personal health status,height,weight,blood pressure,fasting blood glucose,blood lipids and other indicators were collected by questionnaire survey,physical measurement and laboratory examination.The screening of MS was based on the diagnostic criteria of MS recommended by Chinese Diabetes Society(CDS).Results Among the adult residents in Zhifu district,the number of MS patients was 522,with a crude prevalence rate of 21.6%.The prevalence of MS in men was 26.9%higher than that in women(16.9%)(χ2=35.939,P<0.001).With the increase of age,the prevalence rate of MS showed an overall upward trend(linear trend test χ2=150.557,P<0.001).With the increase of education level,the prevalence rate of MS showed an overall downward trend(linear trend test χ2=81.670,P<0.001).The results of multivariate logistic regression analysis further showed that male and age increase were high risk factors for MS;higher education level,unmarried,engaged in static occupation,and increased activity were protective factors for MS.Conclusion The prevalence rate of MS in adult residents in Zhifu district of Yantai is basically equal to the national average level,suggesting that the government and health service institutions at all levels should take health exercise,smoking cessation and alcohol restriction as the entry point for health education and early diagnosis and treatment;meanwhile,targeted interventions should be carried out according to different characteristics of the population.

Objective This study aimed to explore the underlying mechanisms between parental neglect and depression of adolescents.Methods This study utilized data from the 2020 China Family Panel Studies(CFPS)and included 1902 adolescents aged 10～15.Correlation analysis,multivariable logistic regression,structural equation modeling,and the bootstrap method were employed to examine the relationships between parental neglect,social trust,and depressive symptoms in adolescents.Results This study found that the prevalence of depressive symptoms among adolescents was 10.9%.Parental neglect was associated with an increased risk of depressive symptoms(OR:1.772,95%CI:1.310～2.636),and social trust acted as a mediator between parental neglect and depressive symptoms(mediating effect:17.20%,P<0.001).Additionally,gender moderated the latter half of the mediation model(β:0.127,95%CI:0.016～0.236,P<0.001).Conclusion The experiences of parental neglect are closely related to higher levels of depressive symptoms among adolescents,with social trust serving as a mediating factor.This mediating effect of social trust is particularly significant among females.It is crucial to cultivate a supportive environment and develop a harmonious family atmosphere.