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.

Based on the theory of diseased collaterals and the progression pattern of diabetic kidney disease （DKD）， this paper proposes a staged treatment system based on the "three states of kidney collaterals" including collateral distention， collateral bi （痹） and collateral accumulation. In the collateral distention stage， the primary pathogenesis is heat damaging the kidney collaterals and collateral vessel dilation， corresponding to the early state of DKD characterized by hyperfiltration and microalbuminuria； the clearing method is suggested， and Shenling Jiangtang Formula （参苓降糖方） can be used to promote the remission of proteinuria by ameliorating insulin resistance and regulating intestinal microecology. In the collateral bi stage， the core pathogenesis lies in the binding of phlegm and stasis， and the formation of concretions and conglomerations in the kidney collaterals， corresponding to massive proteinuria and the decline of renal function， for which the unblocking method should be taken， using Jinchan Yishen Tongluo Formula （金蝉益肾通络方） and Qingre Xiaozheng Formula （清热消癥方） to alleviate renal pathological damage by regulating the autophagy-lysosome pathway and repairing mitochondrial energy metabolism. The collateral accumulation stage is characterized by the internal retention of turbid toxins and accumulations of concretions in the kidney collaterals， corresponding to renal fibrosis and end-stage renal disease （ESRD）； the dispersion method is suggested， and Yiqi Tongluo Xiezhuo Formula （益气通络泄浊方） and Shenyan Fangshuai Fluid （肾炎防衰液） can be used to inhibit extracellular matrix accumulation and delay the progressive loss of renal function. Collateral deficiency runs through the entire course of the disease and is closely related to cellular senescence and the imbalance of the ubiquitin-proteasome/autophagy system. The concept of the "three states of kidney collaterals" integrates the evolution of traditional Chinese medicine pathogenesis with modern pathological staging， forming a whole-course intervention system of "staging-syndrome differentiation-target-formula"， thereby providing novel insights for prevention and treatment of DKD using integrated traditional Chinese and western medicine.

Objective:To analyze the incidence of adverse treatment outcome of rifampicin-resistant tuberculosis (RR-TB) patients and influencing factors in 9 provinces in China.Methods:The information about the basic characteristics, diagnosis and treatment of RR-TB patients registered from January 1, 2017 to December 31, 2021 in 9 provinces (Beijing, Jilin, Shanghai, Jiangsu, Zhejiang, Hubei, Henan, Yunnan and Guizhou) were collected from the tuberculosis information management sub-system of China Disease Control and Prevention Information System for a descriptive analysis, and the influencing factors of adverse treatment outcome were identified by binary logistic regression analysis.Results:In 18 204 RR-TB patients in this study a total of 6 852 had adverse treatment outcomes (37.64%). Treatment failure occurred in 1 031 patients, 1 272 patients died, 2 284 patients were lost to follow-up, and 2 265 patients were not evaluated. The results of multivariate logistic regression analysis showed that being man (a OR=1.54, 95% CI: 1.43-1.66), age ≥65 years (a OR=2.42, 95% CI: 2.20-2.67), being from other ethnic groups (a OR=1.18, 95% CI: 1.03-1.35), being farmer (a OR=1.22, 95% CI: 1.13-1.32), being retired with honours or being retired (a OR=1.16, 95% CI: 1.00-1.35) and being floating population (a OR=1.23, 95% CI: 1.13-1.34), re-treatment (a OR=1.33, 95% CI: 1.24-1.42), long-term treatment therapy (a OR=3.26, 95% CI: 2.52-4.23), living in central provinces (a OR=2.76, 95% CI: 2.55-2.99), living in western provinces (a OR=2.31, 95% CI: 2.08-2.57) were the influencing factors for adverse treatment outcome of RR-TB. Conclusions:The incidence of adverse outcomes in RR-TB patients in 9 provinces in China was higher from 2017 to 2021. There were many influencing factors associated with adverse treatment outcome in RR-TB patients, especially the area specific factors. The national tuberculosis control program should strengthen the follow-up and treatment management of RR-TB in men, the elderly, floating population, people in central and western provinces and other key groups, actively promote the short-term treatment program and improve the allocation of medical resources to reduce the influencing for adverse treatment outcomes in RR-TB patient.

Objective:To evaluate the effect of exogenous short-chain fatty acids (SCFAs) preconditioning on the expression of zonula occludens-1 (ZO-1) in lung tissues of rats undergoing extracorporeal circulation (ECC).Methods:Thirty-six clean-grade healthy adult male Sprague-Dawley rats, weighing 320-420 g, were divided into sham operation group (S group), ECC group (E group) and SCFAs group, with 12 rats in each group. Seven days before the ECC, short-chain fatty acids dissolved in 2 ml of normal saline was given by gavage daily in SCFAs group, while the equal volume of normal saline was given by gavage in S group and E group. On the 8th day, E group and SCFAs group underwent arteriovenous catheterization and ECC for 1 h, while S group only underwent catheterization without ECC. Lung tissues were collected to observe the pathological results and detect the expression of ZO-1 (by Western blot), and the wet/dry lung weight ratio was calculated.Results:Compared with S group, the wet/dry lung weight ratio was significantly increased ( P<0.05), the expression of ZO-1 protein in lung tissue was down-regulated ( P<0.05), and the pathological damage of lung tissues was aggravated in E group and SCFAs group. Compared with E group, the wet/dry lung weight ratio was significantly decreased, the expression of ZO-1 protein in lung tissues was up-regulated ( P<0.05), and the pathological damage of lung tissues was significantly alleviated in SCFAs group. Conclusions:The mechanism by which SCFAs preconditioning attenuates lung injury may be related to up-regulation of ZO-1 expression in lung tissues of rats undergoing ECC.

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.

BACKGROUND:Studies have found that massage can reduce blood sugar,promote myogenic factor expression,and increase skeletal muscle content.The extracellular matrix is an important component of skeletal muscle,and association between massage and extracellular matrix and their mechanism of action are still unclear.OBJECTIVE:To explore the effect of massage on extracellular matrix collagen deposition in type 2 diabetic sarcopenia rats.METHODS:Totally 24 Wistar male rats were randomly divided into blank group,model group,and massage group.High-fat diet combined with the streptozotocin method was used to establish a type 2 diabetes mellitus and sarcopenia model.After successful model establishment,the massage group used abdominal massage combined with hind limbs.After 8 weeks of treatment,the fasting blood glucose and serum insulin levels of the rats were measured.The skeletal muscle mass was detected by dual-energy X-ray.The exhaustion time was measured by small animal treadmill.The sliding angle was measured by inclined board test.The pathological changes of skeletal muscle tissue were observed by hematoxylin-eosin staining.The skeletal muscle collagen deposition was observed by Masson staining.The mRNA and protein expressions of type Ⅰ and type Ⅲ collagen in skeletal muscle were detected by qPCR and western blot assay.RESULTS AND CONCLUSION:(1)Compared with the model group,the blood glucose(P＜0.05)and serum insulin(P＜0.01)decreased in the massage group.(2)Compared with the model group,the skeletal muscle mass,running exhaustion time,and the angle of inclined plate experiment were increased in massage group(P＜0.05).(3)Compared with the model group,the skeletal muscles of the massage group were arranged neatly,muscle atrophy was improved,and collagen fiber deposition was reduced.(4)Compared with the model group,the expression levels of type Ⅰ and type Ⅲ collagen mRNA and protein in skeletal muscle were decreased in the massage group(P＜0.05).(5)The results suggest that massage can enhance insulin sensitivity,lower blood sugar,improve skeletal muscle mass,strength and function,and diminish collagen deposition in rats with type 2 diabetes,and may be a potential target for massage to exert its therapeutic effects.

Objective:To analyze the incidence of adverse treatment outcome of rifampicin-resistant tuberculosis (RR-TB) patients and influencing factors in 9 provinces in China.Methods:The information about the basic characteristics, diagnosis and treatment of RR-TB patients registered from January 1, 2017 to December 31, 2021 in 9 provinces (Beijing, Jilin, Shanghai, Jiangsu, Zhejiang, Hubei, Henan, Yunnan and Guizhou) were collected from the tuberculosis information management sub-system of China Disease Control and Prevention Information System for a descriptive analysis, and the influencing factors of adverse treatment outcome were identified by binary logistic regression analysis.Results:In 18 204 RR-TB patients in this study a total of 6 852 had adverse treatment outcomes (37.64%). Treatment failure occurred in 1 031 patients, 1 272 patients died, 2 284 patients were lost to follow-up, and 2 265 patients were not evaluated. The results of multivariate logistic regression analysis showed that being man (a OR=1.54, 95% CI: 1.43-1.66), age ≥65 years (a OR=2.42, 95% CI: 2.20-2.67), being from other ethnic groups (a OR=1.18, 95% CI: 1.03-1.35), being farmer (a OR=1.22, 95% CI: 1.13-1.32), being retired with honours or being retired (a OR=1.16, 95% CI: 1.00-1.35) and being floating population (a OR=1.23, 95% CI: 1.13-1.34), re-treatment (a OR=1.33, 95% CI: 1.24-1.42), long-term treatment therapy (a OR=3.26, 95% CI: 2.52-4.23), living in central provinces (a OR=2.76, 95% CI: 2.55-2.99), living in western provinces (a OR=2.31, 95% CI: 2.08-2.57) were the influencing factors for adverse treatment outcome of RR-TB. Conclusions:The incidence of adverse outcomes in RR-TB patients in 9 provinces in China was higher from 2017 to 2021. There were many influencing factors associated with adverse treatment outcome in RR-TB patients, especially the area specific factors. The national tuberculosis control program should strengthen the follow-up and treatment management of RR-TB in men, the elderly, floating population, people in central and western provinces and other key groups, actively promote the short-term treatment program and improve the allocation of medical resources to reduce the influencing for adverse treatment outcomes in RR-TB patient.

Objective:To evaluate the effect of exogenous short-chain fatty acids (SCFAs) preconditioning on the expression of zonula occludens-1 (ZO-1) in lung tissues of rats undergoing extracorporeal circulation (ECC).Methods:Thirty-six clean-grade healthy adult male Sprague-Dawley rats, weighing 320-420 g, were divided into sham operation group (S group), ECC group (E group) and SCFAs group, with 12 rats in each group. Seven days before the ECC, short-chain fatty acids dissolved in 2 ml of normal saline was given by gavage daily in SCFAs group, while the equal volume of normal saline was given by gavage in S group and E group. On the 8th day, E group and SCFAs group underwent arteriovenous catheterization and ECC for 1 h, while S group only underwent catheterization without ECC. Lung tissues were collected to observe the pathological results and detect the expression of ZO-1 (by Western blot), and the wet/dry lung weight ratio was calculated.Results:Compared with S group, the wet/dry lung weight ratio was significantly increased ( P<0.05), the expression of ZO-1 protein in lung tissue was down-regulated ( P<0.05), and the pathological damage of lung tissues was aggravated in E group and SCFAs group. Compared with E group, the wet/dry lung weight ratio was significantly decreased, the expression of ZO-1 protein in lung tissues was up-regulated ( P<0.05), and the pathological damage of lung tissues was significantly alleviated in SCFAs group. Conclusions:The mechanism by which SCFAs preconditioning attenuates lung injury may be related to up-regulation of ZO-1 expression in lung tissues of rats undergoing ECC.

Objective:To explore the clinical application value of serum N-glycan profiles for evaluating the severity of liver tissue inflammation in patients with chronic hepatitis B (CHB).Methods:A total of 221 CHB patients who underwent liver biopsy at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2018 to December 2020 were retrospectively enrolled. The Scheuer scoring system was used to assess the histological inflammation grade of the liver tissue. Serum N-glycan levels were measured using DNA sequencer-assisted N-glycan fingerprinting (NGFP). Using the upper limit of the alanine aminotransferase (ALT) reference value (40 U/L) as a cutoff, logistic regression models were developed to construct diagnostic models under two scenarios: normal ALT or abnormal ALT. Models based on serum N-glycan levels and serum N-glycan levels combined with routine laboratory indicators, were used to non-invasively evaluation of various pathological grades of liver tissue inflammation in CHB patients. The DeLong test was used to compare the diagnostic efficacy of the models by analyzing the areas under the receiver operating characteristic curve (AUC). Glycosylation-related gene expression differences associated with varying degrees of liver inflammation were analyzed using the Gene Expression Omnibus (GEO) database.Results:In CHB patients with normal ALT level, the relative abundances of N-glycan structure peak 1 (NGA2F) and peak 2 (NGA2FB) increased with higher liver inflammation grades, while the relative abundance of peak 5 (NA2) decreased ( P<0.05). The AUCs of the HIS-G model (HIS-G A) and its enhanced version (HIS-G A Plus) for identifying significant inflammation and necrosis (≥G2, indicating the initiation of antiviral therapy) were 0.805 (95% CI 0.690-0.899) and 0.904 (95% CI 0.821-0.960), respectively. In CHB patients with ALT>40 U/L, the relative abundances of peaks 1 (NGA2F), 2 (NGA2FB), and 3 (NG1A2F) increased with higher liver inflammation grades, while the relative abundances of peaks 8 (NA3) and 11 (NA4) decreased ( P<0.05). The AUCs of the HIS-G model (HIS-G B) and its enhanced version (HIS-G B Plus) for identifying significant inflammation (≥G2) were 0.810 (95% CI 0.727-0.889) and 0.838 (95% CI 0.754-0.901), respectively. With increasing liver inflammation grades, the expression levels of four glycosyltransferase genes (CHST4, FUT8, SLC51B, and ST8SIA4) were significantly upregulated ( P<0.05). Conclusions:Serum N-glycan biomarker models can be used to assist in evaluating the severity of liver tissue inflammation in CHB patients with both normal and abnormal ALT levels.

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.