Objective: To investigate the prevalence of overweight and obesity among children and adolescents in Yangzhou City, and its association with screen behaviors, so as to provide scientific evidence for weight management among students. Methods: In May 2025, an electronic questionnaire survey was conducted among children and adolescents in Yangzhou City. A total of 3 722 participants were selected from grades 4 to 12 in 18 primary and secondary schools (108 classes) by using stratified cluster random sampling. The Chi square test was used to compare the differences in the detection rates of overweight and obesity among children and adolescents with 5 types of screen behaviors (watching TV, playing electronic games, scrolling short videos, screen based learning, electronic socializing) in different time groups each day (never, >0~<2 h, ≥2 h). Multivariate Logistic regression analysis was performed to examine the associations of five types of screen behaviors, presence of electronic devices in the bedroom, and screen use during meals on the weight status of children and adolescents. Results: The prevalence of overweight and obesity among children and adolescents was 37.3%. For all five types of screen behaviors, the differences in the distribution of overweight and obesity detection rates among children and adolescents across the three time spent categories were statistically significant ( χ 2=30.76- 70.78 , all P <0.01). After adjusting for confounding factors, multivariate Logistic regression analysis revealed that frequent or always using screens during meals( OR =1.63, 95％ CI =1.14~2.31), playing video games ( OR =1.28, 95% CI =1.11-1.48), browsing short videos ( OR =1.29, 95％ CI=1.09-1.54), and screen based learning ( OR =1.26, 95％ CI =1.10-1.44) were significantly associated with overweight and obesity among children and adolescents (all P <0.05). Conclusions Excessive screen use is positively correlated with the incidence of overweight and obesity in children and adolescents. Targeted interventions on screen behaviors among children and adolescents are therefore warranted.

Objective: To analyze the prevalence and determinants of comorbid overweight/obesity and elevated blood pressure among primary and secondary school students in Yangzhou City, and to explore the association between sleep patterns, eating behavior and the comorbidity of overweight/obesity and elevated blood pressure, so as to provide reference for developing prevention strategies targeting common comorbidities in students. Methods: By using stratified cluster random sampling, a total of 8 735 primary and secondary school students were selected from 36 schools in six counties of Yangzhou from October to November 2023. Students underwent physical examinations and a questionnaire survey was conducted using the questionnaire on students health status and influencing factors. The Chi square test was used to compare the detection rate of comorbid overweight/obesity and elevated blood pressure in different groups of primary and secondary school students. The Logistic regression model was used to explore the association between sleep and dietary behaviors and their combined effects and coexistence. Results: The detection rate of comorbid overweight/obesity and elevated blood pressure among primary and secondary school students in Yangzhou was 9.85%, which was higher among boys (12.14%) than girls (7.59%)( χ 2=50.86, P <0.01). After controlling for gender, residence, educational stage, parental education, smoking, drinking, and moderate to vigorous exercise, multivariate Logistic regression analysis showed that irregular breakfast consumption and inadequate daily sleep were associated with a higher risk of comorbidities compared with regular breakfast consumption and adequate daily sleep among overall and primary school students (overall: OR =1.52, 95% CI =1.18- 1.96 , primary school students: OR =2.79, 95% CI =1.61-4.82)(both P <0.05). From the perspective of primary school students of different genders, the risk of comorbidities in girls who consumed breakfast irregularly and had inadequate daily sleep was 3.59 times higher than that in girls who consumed breakfast irregularly and had inadequate daily sleep (95% CI =1.65-7.82, P <0.01). Conclusion The sleep patterns and breakfast behaviors of primary and secondary school students are found to be associated with comorbid overweight/obesity and elevated blood pressure, especially in primary school girls.

OBJECTIVES: To develop an E-selectin-targeting nanomedicine delivery system that competitively inhibits E-selectin-neutrophil ligand binding to block neutrophil adhesion to vessels and suppress their recruitment to the lesion sites. METHODS: Doxorubicin hydrochloride (DOX)-loaded liposomes (IEL-Lip/DOX) conjugated with E-selectin-affinity peptide IELLQARC were developed using a post-insertion method. Two formulations [2-1P: Mol(PC): Mol(DPI)=100:1; 2-3P: 100:3] were prepared and their modification density and in vitro release characteristics were determined. Their targeting efficacy was assessed in a cell model of LPS-induced inflammation, a mouse model of acute lung injury (ALI), a rat femoral artery model of physical injury-induced inflammation, and a zebrafish model of local inflammation. RESULTS: The prepared IEL-Lip/DOX 2-1P and 2-3P had peptide modification densities of 4.76 and 7.57 pmoL/cm², respectively. Compared with unmodified liposomes, IEL-Lip/DOX exhibited significantly reduced 48-h cumulative release rates at pH 5.5. In the inflammation cell model, IEL-Lip/DOX showed increased uptake by activated inflammatory endothelial cells, and 2-1P exhibited a higher trans-endothelial ability. In ALI mice, the fluorescence intensity of IEL-Lip/Cy5.5 increased significantly in lung tissues by 53.71% [Z-(2-1P)] and 93.41% [Z-(2-3P)], and 2-1P had an increased distribution by 24.19% in the inflammatory lung tissue compared to normal mouse lung tissue. In rat femoral artery models, 2-1P had greater injured/normal vessel fluorescence intensity contrast. In the zebrafish models, both 2-1P and 2-3P showed increased aggregation at the site of inflammation. CONCLUSIONS This E-selectin-targeting nanomedicine delivery system efficiently targets activated inflammatory endothelial cells to increase drug concentration at the inflammatory site, which sheds light on new strategies for treating neutrophil-mediated inflammatory diseases and practicing the concept of "one drug for multiple diseases".
Animals ; Liposomes ; Rats ; Nanomedicine ; E-Selectin ; Drug Delivery Systems ; Inflammation/drug therapy* ; Mice ; Doxorubicin/analogs & derivatives* ; Zebrafish ; Acute Lung Injury/drug therapy*

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Tumor immunotherapy has attracted worldwide attention in cancer treatment because of its obvious advantages such as strong specificity and long curative effect.It is found that activation of STING signaling pathway in cells is one of directions to effec-tively realize tumor immunotherapy.However,due to low response rate of related drugs,difficult degradation,certain toxic and side effects,its clinical application has been seriously hindered.Nano-drug delivery system can achieve targeted drug delivery,improve drug stability,delivery rate,osmotic effect and long-term retention effect,reduce drug side effects,and show significant advantages in tumor immunotherapy.In this paper,research progress of nano-drug delivery system activating STING pathway in tumor immunother-apy in recent years is reviewed,and many nano-drug delivery systems that can activate STING signal pathway and their application ex-amples after loading drugs are listed,including nucleotide-based drug delivery system,non-nucleotide-based drug delivery system and metal-based drug delivery system,providing reference for application of nano-drugs in tumor immunotherapy.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Tumor immunotherapy has attracted worldwide attention in cancer treatment because of its obvious advantages such as strong specificity and long curative effect.It is found that activation of STING signaling pathway in cells is one of directions to effec-tively realize tumor immunotherapy.However,due to low response rate of related drugs,difficult degradation,certain toxic and side effects,its clinical application has been seriously hindered.Nano-drug delivery system can achieve targeted drug delivery,improve drug stability,delivery rate,osmotic effect and long-term retention effect,reduce drug side effects,and show significant advantages in tumor immunotherapy.In this paper,research progress of nano-drug delivery system activating STING pathway in tumor immunother-apy in recent years is reviewed,and many nano-drug delivery systems that can activate STING signal pathway and their application ex-amples after loading drugs are listed,including nucleotide-based drug delivery system,non-nucleotide-based drug delivery system and metal-based drug delivery system,providing reference for application of nano-drugs in tumor immunotherapy.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Objective To observe the effect of Danshen injection (DSI) on pulmonary fibrosis in silicosis mice, and to analyze the differential metabolic pathway on pulmonary fibrosis in silicosis using DSI by urine metabolomics. Methods The specific pathogen free C57BL/6J mice were randomly divided into control group, silicosis model group, DSI prevention group and DSI treatment group. The mice in the last three groups were given 1 mL silica suspension with a mass concentration of 50 g/L by the one-time non-exposed tracheal method, and the mice in the control group were not given any treatment. Subsequently, mice in the DSI prevention group and the DSI treatment group were given intraperitoneal injection of DSI with a dose of 5 mL/kg body weight from 24 hours after exposure to dust and from the 29th day after exposure to dust, respectively, once per day until the 56th day after exposure. Mice in the other two groups were not treated. After DSI intervention, the lung histopathological changes of mice in all groups were evaluated. The components of mouse urine metabolites were analyzed using ultra-high performance liquid chromatography-quadrupole-time-of-fight mass spectrometry method. Human Metabolome Database was used to screen the potential differential metabolites (DMs). The related metabolic pathways were analyzed using MetaboAnanlyst 5.0 Web analytics platform. Results The result of hematoxylin-eosin staining and Van Gieson staining of mouse lung tissues showed that the pulmonary alveolar structure destroyed, typical fibrotic nodules appeared, collagen fiber deposition increased, and clumpy accumulation in the silicosis model group, compared with the control group. Compared with the silicosis model group, the degree of pulmonary alveolar inflammation and fibrosis in the lung tissues of mice in the DSI prevention group was obviously reduced to close to the control group, while pulmonary alveolar inflammation and fibrosis in the lung tissues of mice in the DSI treatment group were also reduced, although the outcome was not as good as that in the DSI prevention group. The result of urine metabolomics analysis identified four DMs in the model group and control group, seven DMs were identified in the DSI prevention group and silicosis model group, seven DMs were identified in the DSI treatment group and silicosis model group. A total of three DMs pathways related to pulmonary fibrosis in silicosis model group and the protective effect of DSI prevention group were identified, including D-arginine and D-ornithine metabolism, folic acid biosynthesis and metabolism, pantothenate and succinyl coenzyme A biosynthesis pathways (all P<0.01). Conclusion DSI treatment in any time point can interfere the process of pulmonary fibrosis in the silicosis mice, while the interference is more effective in the DSI group treated right after dust-exposure. DSI interferes with the urinary metabolism pathway of silicosis mice, and the D-arginine and D-ornithine metabolism, folic acid biosynthesis and metabolism, pantothenate and succinyl coenzyme A biosynthesis pathways may participate in the inhibiting process of early pulmonary fibrosis in silicosis mice by DSI.