Objective: To investigate the prevalence and associated factors of depressive symptoms among junior and senior high school students in Beijing from 2019 to 2023, in order to provide a scientific basis for interventions targeting high risk groups. Methods: From 2019 to 2023, a stratified cluster random sampling method was used to select 88 927 junior and senior high school students from 16 districts in Beijing. The Center for Epidemiologic Studies Depression Scale(CES-D) was conducted to assess depressive symptoms. The Chi square test was used to compare the detection rates of depressive symptoms among different student groups, and the trend Chi square test was employed for trend analysis of detection rates across the years. Multivariate Logistic regression analysis was applied to examine the association between the detection of depressive symptoms and related factors among junior and senior high school students. Results: From 2019 to 2023, the prevalence rates of depressive symptoms among junior and senior high school students in Beijing were 20.45%, 18.19%, 16.64%, 17.89% and 18.17%, respectively, with an overall downward trend ( χ 2 trend =27.51, P <0.01). Multivariate Logistic regression analysis revealed that after adjusting for gender, monitoring year, educational stage,family structure,boarding status and has taken a medical leave of absence in the past year unhealthy dietary behaviors ( OR=1.80, 95%CI =1.73-1.87), physical inactivity ( OR=1.24, 95%CI =1.19-1.29), try smoking ( OR=1.46, 95%CI =1.35-1.58), try alcohol( OR=1.96, 95%CI =1.88-2.05), Internet addiction ( OR=3.88, 95%CI =3.57-4.22), and adverse ear related behavior ( OR=1.82, 95%CI =1.71-1.93) were all associated with an increased risk of depressive symptoms among junior and senior high school students (all P <0.05). Conclusions The prevalence depression symptoms among middle school students in Beijing showed a fluctuating downward trend from 2019 to 2023. Targeted interventions should be adopted to reduce the occurrence of depression symptoms among junior and senior high school students.

Objective: To understand the trends of classroom lighting and illumination of primary and secondary schools in Beijing from 2016 to 2023, so as to provide a scientific basis for targeted improvement measures. Methods: A sampling survey was conducted on the lighting and illumination indicators of 8 390 classrooms in primary and secondary schools in Beijing from 2016 to 2023. The survey included classroom daylight factor, window to floor area ratio, average illuminance and illuminance uniformity on the desks, average illuminance and illuminance uniformity on blackboards, as well as classroom lighting and blackboard illumination sources. Intergroup comparisons were performed using the Kruskal-Wallis H test and the Chi square test, and Spearman correlation analysis was used to examine the trend of classroom lighting and illumination changes. Results: Except the window to floor area ratio, the measured values and compliance rates of all lighting and illumination indicators showed an overall upward trend from 2016 to 2023 (daylight factor r = 0.27, χ 2 trend =206.80, average illuminance on the desk surface r =0.30, χ 2 trend =87.97, illuminance uniformity on the desk surface r =0.14, χ 2 trend =73.59, average illuminance on the blackboard r =0.33, χ 2 trend =477.43, illuminance uniformity on the blackboard r = 0.09, χ 2 trend =50.76) (all P ＜0.01). The lighting and illumination indicators of classrooms (included classroom daylight factor, average illuminance and illuminance uniformity on the desks, average illuminance and illuminance uniformity on blackboards) in urban schools, primary schools, and secondary schools from 2016 to 2023 showed an upward trend (urban r =0.23-0.40, χ 2 trend =88.66-392.18; primary school r =0.12-0.36, χ 2 trend =39.50-281.44; secondary schools r =0.06-0.31, χ 2 trend =11.79-213.73) (all P ＜ 0.01 ). The illuminance uniformity on the blackboard in suburban schools showed a downward trend ( r = -0.09, χ 2 trend =31.53, both P ＜0.01). The illuminance uniformity on the desk surface in suburban schools showed no significant change ( r =0.03, χ 2 trend =1.23, both P ＞0.05). The other indicators showed an upward trend (daylight factor r =0.28, χ 2 trend =40.69, average illuminance on the desk surface r =0.24, χ 2 trend =16.35, average illuminance on the blackboard r =0.25, χ 2 trend =118.05, all P ＜0.01). The trends of classroom and blackboard illumination sources were that fluorescent lamps decreased year by year and LED lamps increased by year (classroom illumination sources χ 2 trend =1 059.82, blackboard illumination sources χ 2 trend =1 070.25, both P ＜0.01). Conclusions The classroom lighting and illumination in primary and secondary schools in Beijing has shown an overall improving trend from 2016 to 2023. However, problems remain, such as limited improvement of illuminance uniformity indicators, late start and poor effect of reconstruction in suburban schools. Further improvements are still needed.

Objective:To explore the influencing factors of self-management of first-treatment pulmonary tuberculosis patients based on the random forest model, and to provide a theoretical basis for clinical staff to improve the self-management of pulmonary tuberculosis patients by providing efficient, high-quality, and individualized interventions.Methods:A cross-sectional study was used to select pulmonary tuberculosis patients who were hospitalized in the Department of Respiratory Medicine of the Fourth People′s Hospital of the Ningxia Hui Autonomous Region and who met the inclusion and exclusion criteria from December 2023 to February 2024 by using the convenience sampling method as the study subjects. General information questionnaire, Chronic Disease Patients′ Self-health Management Ability Assessment Scale, Perceived Social Support Scale, Hospital Anxiety and Depression Scale, and a tuberculosis prevention and treatment knowledge questionnaire were used to conduct the survey, and Spearman′s correlation was used to analyze the correlation between variables, and multivariate linear regression and a random forest model were used to analyze the influencing factors of self-management.Results:A total of 204 first-treatment pulmonary tuberculosis patients, 111 males and 93 females, 64 patients aged 18-44 years, 59 patients aged 45-59 years, and 81 patients ≥60 years were finally investigated. The total self-management score of tuberculosis patients was 162.00 (148.00, 176.75), and the total self-management score was positively correlated with the total perceived social support score, family support, friend support, and other support, respectively ( r values were 0.307-0.400, all P<0.01), negatively correlated with the anxiety and depression scores, respectively ( r=-0.195, -0.313, both P<0.01), and positively correlated with the total score of knowledge of tuberculosis control ( r=0.257, P<0.01); the results of multiple linear stepwise regression analysis showed that literacy, family support, other support, anxiety, and knowledge of tuberculosis control were the influencing factors of self-management ability ( t values were -2.89-2.98, all P<0.05), which explained a total of 23.1% of the total variance; and the random forest model ranked the importance of the influencing factors in the order of high to low were other support, family support, knowledge of tuberculosis control, literacy, and anxiety. Conclusions:The self-management of first-treatment pulmonary tuberculosis patients is at an intermediate level, In order to improve the self-management ability of first-treatment pulmonary tuberculosis patients, clinical personnel should establish a "patient-centered" self-management education concept, pay attention to the construction of their social support system, provide adequate, continuous, individualized knowledge education and information support, promote their psychological health, and reduce their negative emotions.

Objective To explore the clinical efficacy of Liuwei Dihuang Capsules for type 2 diabetes mellitus(T2DM)with yin deficiency syndrome and the effects on intestinal flora and inflammatory factors.Methods Totally 60 patients of T2DM with yin deficiency syndrome in Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2022 to June 2023 were selected as the study objects,and were divided into control group and observation group according to the method of block randomization,with 30 cases in each group.Both groups received basic treatment.The control group was given a simulated agent of Liuwei Dihuang Capsules,while the observation group was given Liuwei Dihuang Capsules.The treatment course for both groups was 4 weeks.Clinical efficacy,blood glucose levels[fasting plasma glucose(FPG),2-hour postprandial plasma glucose(2 hPG),glycated albumin(GA)],serum insulin levels[fasting insulin(FINS)and insulin resistance index(HOMA-IR)],changes in gut microbiota,and serum inflammatory cytokine levels[interleukin(IL)-6,tumor necrosis factor(TNF)-α]of both groups were compared.Results The total effective rate of the observation group(76.67%)was better than that of the control group(50.00%)(P<0.05).Compared with before treatment,the FPG,2 hPG,GA,FINS and HOMA-IR decreased in the observation group,while the FPG,2 hPG and FINS decreased in the control group(P<0.05);after treatment,the Shannon index of the observation group increased after treatment(P<0.05),and the diversity of the microbiota increased;the abundance of the microbial communities such as Coprococcus 3,Cutibacterium,Pseudomonas,Faecalibaculum,Dubosiella and Mucispirillum significantly increased(P<0.05);the abundance of Sphingomonas,Corynebacterium 1,Ileibacterium,Ruminiclostridium and other microbiota communities significantly decreased(P<0.05).Compared with before treatment,the levels of IL-6 and TNF-α in both groups were significantly reduced after treatment(P<0.01,P<0.05).After treatment,the levels of IL-6 and TNF-α in the observation group were significantly lower than those in the control group(P<0.05).Conclusion Liuwei Dihuang Capsules can effectively reduce blood glucose levels in patients of T2DM with yin deficiency syndrome,improve insulin resistance,increase gut microbiota diversity,increase beneficial bacterial abundance,reduce harmful bacterial abundance,and alleviate inflammatory cytokine levels.

Objective:To explore the influencing factors of self-management of first-treatment pulmonary tuberculosis patients based on the random forest model, and to provide a theoretical basis for clinical staff to improve the self-management of pulmonary tuberculosis patients by providing efficient, high-quality, and individualized interventions.Methods:A cross-sectional study was used to select pulmonary tuberculosis patients who were hospitalized in the Department of Respiratory Medicine of the Fourth People′s Hospital of the Ningxia Hui Autonomous Region and who met the inclusion and exclusion criteria from December 2023 to February 2024 by using the convenience sampling method as the study subjects. General information questionnaire, Chronic Disease Patients′ Self-health Management Ability Assessment Scale, Perceived Social Support Scale, Hospital Anxiety and Depression Scale, and a tuberculosis prevention and treatment knowledge questionnaire were used to conduct the survey, and Spearman′s correlation was used to analyze the correlation between variables, and multivariate linear regression and a random forest model were used to analyze the influencing factors of self-management.Results:A total of 204 first-treatment pulmonary tuberculosis patients, 111 males and 93 females, 64 patients aged 18-44 years, 59 patients aged 45-59 years, and 81 patients ≥60 years were finally investigated. The total self-management score of tuberculosis patients was 162.00 (148.00, 176.75), and the total self-management score was positively correlated with the total perceived social support score, family support, friend support, and other support, respectively ( r values were 0.307-0.400, all P<0.01), negatively correlated with the anxiety and depression scores, respectively ( r=-0.195, -0.313, both P<0.01), and positively correlated with the total score of knowledge of tuberculosis control ( r=0.257, P<0.01); the results of multiple linear stepwise regression analysis showed that literacy, family support, other support, anxiety, and knowledge of tuberculosis control were the influencing factors of self-management ability ( t values were -2.89-2.98, all P<0.05), which explained a total of 23.1% of the total variance; and the random forest model ranked the importance of the influencing factors in the order of high to low were other support, family support, knowledge of tuberculosis control, literacy, and anxiety. Conclusions:The self-management of first-treatment pulmonary tuberculosis patients is at an intermediate level, In order to improve the self-management ability of first-treatment pulmonary tuberculosis patients, clinical personnel should establish a "patient-centered" self-management education concept, pay attention to the construction of their social support system, provide adequate, continuous, individualized knowledge education and information support, promote their psychological health, and reduce their negative emotions.

Objective To investigate the mediating role of anxiety and depression in social sup-port and burnout among primary caregivers of stroke patients.Methods A convenience sampling method was employed to select 506 primary caregivers of stroke patients as research subjects.The Gen-eral Information Questionnaire,Caregiver Burnout Inventory for Stroke Patients,Multidimensional Scale of Perceived Social Support,and Hospital Anxiety and Depression Scale were used for data col-lection.Results The total scores for caregiver burnout,social support,anxiety,and depression a-mong primarycaregivers of stroke patients were(72.83±14.32),(47.79±7.74),(10.49±3.00),and(10.45±3.06),respectively.Correlation analysis revealed a negative correlation between caregiver burnout and social support(r=-0.245,P＜0.001),and positive correlations with anxiety and depression(r=0.178,0.216,P＜0.001).Anxiety and depression partially media-ted the relationship between social support and caregiver burnout,accounting for 7.9％and 10.7％of the total effect,respectively.Conclusion Social support,anxiety,depression,and burnout are closely interrelated.Social support can directly or indirectly influence the occurrence of burnoutthrough anxiety and depression.Medical staff should pay attention to the anxiety and depression status of primary caregivers of stroke patients and enhance social support levels to reduce the incidence of burnout by alleviating anxiety and depression.

Objective To investigate the effects of long non coding RNA MAGI2 antisense chain RNA3(LncRNA MAGI2-AS3)on the migration,invasion,and epithelial mesenchymal transition(EMT)of gastric cancer(GCa)cells by regulating the miR-194-5 p/caveolin-1(CAV1)axis.Methods Fifty-two GCa patients who underwent surgical resection in our hospital from August 2022 to December 2023 were selected.Cancer and adjacent tissues were collected,and AGS,MKN45,HGC-27,and GES1 cells were cultured in vitro.The expression of MAGI2-AS3,miR-194-5p,and CAV1 in tissue samples and cell lines was analyzed.AGS cells were randomly separated into AGS group,sh-AGS group sh-MAGI2-AS3 group,miR-NC group,and in miR-194-5p group.The proliferation,apoptosis,migration,and invasion of cells in each group were compared.Immunoblotting was applied to analyze the expression of E-cadherin,CAV1,proliferating cell nuclear antigen(PCNA),N-cadherin,Bax,matrix metalloproteinase 2(MMP2),and vimentin of cells in each group.Dual luciferase assay was applied to analyze the relationship between MAGI2-AS3 and miR-194-5p,and between miR-194-5p and CAV1.Results The expression of MAGI2-AS3 mRNA,CAV1 mRNA,and positive expression rate of CAV1 protein in GCa tissue increased,while the expression of miR-194-5p mRNA decreased(P＜0.05).The expression of MAGI2-AS3 mRNA,CAV1 mRNA,and CAV1 protein in HGC-27,MKN45,and AGS cells was higher than that of GES1 cells,the expression of miR-194-5p mRNA was lower than that of GES1 cells(P＜0.05).Compared with the AGS and sh-AGS groups,the cell absorbance,number of clones,invasion and migration,expression of CAV1,PCNA,N-cadherin,MMP2,and vimentin in sh-MAGI2-AS3 group decreased,the apoptosis rate,expression of E-cadherin,and Bax increased(P＜0.05).Compared with the miR-NC group and sh-MAGI2-AS3 group,the cell absorbance,number of clones,invasion and migration,expression of CAV1,PCNA,N-cadherin,MMP2,and vimentin in in-miR-194-5p group increased,the apoptosis rate,expression of E-cadherin,and Bax reduced(P＜0.05).ENCORI database found that there were multiple binding sites between MAGI2-AS3 and miR-194-5p,and between miR-194-5p and CAV1.Compared with the WT-MAGI2-AS3+miR-NC group,the luciferase activity in the WT-MAGI2-AS3+miR-194-5p group decreased(P＜0.05),while compared with the WT-CAV1+miR-NC group,the luciferase activity in the WT-CAV1+miR-194-5p group decreased(P＜0.05).Conclusion LncRNA MAGI2-AS3 silencing can target miR-194-5p to downregulate CAV1,thereby inhibiting GCa cell migration,invasion,and EMT.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective To investigate the effects of long non coding RNA MAGI2 antisense chain RNA3(LncRNA MAGI2-AS3)on the migration,invasion,and epithelial mesenchymal transition(EMT)of gastric cancer(GCa)cells by regulating the miR-194-5 p/caveolin-1(CAV1)axis.Methods Fifty-two GCa patients who underwent surgical resection in our hospital from August 2022 to December 2023 were selected.Cancer and adjacent tissues were collected,and AGS,MKN45,HGC-27,and GES1 cells were cultured in vitro.The expression of MAGI2-AS3,miR-194-5p,and CAV1 in tissue samples and cell lines was analyzed.AGS cells were randomly separated into AGS group,sh-AGS group sh-MAGI2-AS3 group,miR-NC group,and in miR-194-5p group.The proliferation,apoptosis,migration,and invasion of cells in each group were compared.Immunoblotting was applied to analyze the expression of E-cadherin,CAV1,proliferating cell nuclear antigen(PCNA),N-cadherin,Bax,matrix metalloproteinase 2(MMP2),and vimentin of cells in each group.Dual luciferase assay was applied to analyze the relationship between MAGI2-AS3 and miR-194-5p,and between miR-194-5p and CAV1.Results The expression of MAGI2-AS3 mRNA,CAV1 mRNA,and positive expression rate of CAV1 protein in GCa tissue increased,while the expression of miR-194-5p mRNA decreased(P＜0.05).The expression of MAGI2-AS3 mRNA,CAV1 mRNA,and CAV1 protein in HGC-27,MKN45,and AGS cells was higher than that of GES1 cells,the expression of miR-194-5p mRNA was lower than that of GES1 cells(P＜0.05).Compared with the AGS and sh-AGS groups,the cell absorbance,number of clones,invasion and migration,expression of CAV1,PCNA,N-cadherin,MMP2,and vimentin in sh-MAGI2-AS3 group decreased,the apoptosis rate,expression of E-cadherin,and Bax increased(P＜0.05).Compared with the miR-NC group and sh-MAGI2-AS3 group,the cell absorbance,number of clones,invasion and migration,expression of CAV1,PCNA,N-cadherin,MMP2,and vimentin in in-miR-194-5p group increased,the apoptosis rate,expression of E-cadherin,and Bax reduced(P＜0.05).ENCORI database found that there were multiple binding sites between MAGI2-AS3 and miR-194-5p,and between miR-194-5p and CAV1.Compared with the WT-MAGI2-AS3+miR-NC group,the luciferase activity in the WT-MAGI2-AS3+miR-194-5p group decreased(P＜0.05),while compared with the WT-CAV1+miR-NC group,the luciferase activity in the WT-CAV1+miR-194-5p group decreased(P＜0.05).Conclusion LncRNA MAGI2-AS3 silencing can target miR-194-5p to downregulate CAV1,thereby inhibiting GCa cell migration,invasion,and EMT.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.