Objective:To investigate the factors influencing poor visual function recovery in patients after refractive cataract surgery, and to provide an effective clinical predictive model for improving and preventing poor postoperative visual function in such patients.Methods:A retrospective study was conducted on 314 patients who underwent refractive cataract surgery in Xi'an Ai'ergucheng Eye Hospital Affiliated to Northwest University from January 2021 to June 2024. The best corrected visual acuity (BCVA) was used to evaluate the postoperative visual function of patients. Patients were divided into a good visual function recovery group of 252 cases and a poor visual function recovery group of 62 cases based on their visual function recovery. The differences in general clinical data, surgical-related information and aqueous humor cytokines between the two groups were compared. Correlation tests and Logistic regression analysis were used to identify factors closely associated with poor visual function recovery in patients after refractive cataract surgery. The predictive efficacy of these factors for poor visual function recovery was evaluated using the receiver operating characteristic (ROC) curve, area under the curve (AUC) and decision curve.Results:The age, proportion of diabetes, proportion of severe cataracts, preoperative fasting blood glucose (FBG) and preoperative glycosylated hemoglobin (HbA 1c) levels in the poor visual function recovery group were significantly higher than those in the good visual function recovery group: 66 (64, 68) years old vs. 64 (61, 66) years old, 32.3% (20/62) vs. 17.9% (45/252), 48.4% (30/62) vs. 32.1% (81/252), (5.73 ± 0.94) mmol/L vs. (5.29 ± 0.84) mmol/L, (6.39 ± 0.76)% vs. (5.86 ± 0.64)%, and the differences were statistically significant ( P<0.05). The operation time, proportion of intraoperative vitreous leakage and phacoemulsification time in the poor visual function recovery group were significantly higher than those in the good visual function recovery group: (34.23 ± 4.13) min vs. (32.55 ± 2.20) min, 45.2% (28/62) vs. 26.2% (66/252), (19.81 ± 2.96) min vs. (18.62 ± 1.49) min, and the differences were statistically significant ( P<0.05). The levels of aqueous humor interleukin (IL)-1β and vascular endothelial growth factor (VEGF)-A in the poor visual function recovery group were significantly higher than those in the good visual function recovery group: (20.17 ± 3.71) ng/L vs. (18.54 ± 2.16) ng/L, (130.11 ± 15.54) ng/L vs. (122.35 ± 6.74) ng/L, and the differences were statistically significant ( P<0.05). Spearman correlation analysis, univariate and multivariate Logistic regression analysis confirmed that age ( OR = 1.762, 95% CI 1.430 to 2.172), preoperative FBG ( OR = 2.272, 95% CI 1.387 to 3.721), preoperative HbA 1c ( OR = 2.823, 95% CI 1.517 to 5.254), diabetes ( OR = 5.413, 95% CI 1.162 to 25.222), intraoperative vitreous leakage ( OR = 4.751, 95% CI 1.877 to 8.309) and aqueous humor IL-1β ( OR = 1.195, 95% CI 1.031 to 1.386) were important risk factors for poor visual function recovery after refractive cataract surgery ( P<0.05). ROC and decision curve analysis found that the combined application of these risk factors had a high predictive efficacy for poor visual function recovery in patients after refractive cataract surgery, with an AUC (95% CI) of 0.885 (0.839 to 0.931) ( P<0.05). Conclusions:Older age, higher preoperative FBG and HbA 1c levels, diabetes, intraoperative vitreous overflow and higher levels of aqueous humor IL-1β are important factors contributing to poor visual function recovery in patients undergoing refractive cataract surgery. The model constructed based on these indicators has a high predictive efficacy for postoperative visual function recovery in patients undergoing refractive cataract surgery.

Objective:To investigate the factors influencing poor visual function recovery in patients after refractive cataract surgery, and to provide an effective clinical predictive model for improving and preventing poor postoperative visual function in such patients.Methods:A retrospective study was conducted on 314 patients who underwent refractive cataract surgery in Xi'an Ai'ergucheng Eye Hospital Affiliated to Northwest University from January 2021 to June 2024. The best corrected visual acuity (BCVA) was used to evaluate the postoperative visual function of patients. Patients were divided into a good visual function recovery group of 252 cases and a poor visual function recovery group of 62 cases based on their visual function recovery. The differences in general clinical data, surgical-related information and aqueous humor cytokines between the two groups were compared. Correlation tests and Logistic regression analysis were used to identify factors closely associated with poor visual function recovery in patients after refractive cataract surgery. The predictive efficacy of these factors for poor visual function recovery was evaluated using the receiver operating characteristic (ROC) curve, area under the curve (AUC) and decision curve.Results:The age, proportion of diabetes, proportion of severe cataracts, preoperative fasting blood glucose (FBG) and preoperative glycosylated hemoglobin (HbA 1c) levels in the poor visual function recovery group were significantly higher than those in the good visual function recovery group: 66 (64, 68) years old vs. 64 (61, 66) years old, 32.3% (20/62) vs. 17.9% (45/252), 48.4% (30/62) vs. 32.1% (81/252), (5.73 ± 0.94) mmol/L vs. (5.29 ± 0.84) mmol/L, (6.39 ± 0.76)% vs. (5.86 ± 0.64)%, and the differences were statistically significant ( P<0.05). The operation time, proportion of intraoperative vitreous leakage and phacoemulsification time in the poor visual function recovery group were significantly higher than those in the good visual function recovery group: (34.23 ± 4.13) min vs. (32.55 ± 2.20) min, 45.2% (28/62) vs. 26.2% (66/252), (19.81 ± 2.96) min vs. (18.62 ± 1.49) min, and the differences were statistically significant ( P<0.05). The levels of aqueous humor interleukin (IL)-1β and vascular endothelial growth factor (VEGF)-A in the poor visual function recovery group were significantly higher than those in the good visual function recovery group: (20.17 ± 3.71) ng/L vs. (18.54 ± 2.16) ng/L, (130.11 ± 15.54) ng/L vs. (122.35 ± 6.74) ng/L, and the differences were statistically significant ( P<0.05). Spearman correlation analysis, univariate and multivariate Logistic regression analysis confirmed that age ( OR = 1.762, 95% CI 1.430 to 2.172), preoperative FBG ( OR = 2.272, 95% CI 1.387 to 3.721), preoperative HbA 1c ( OR = 2.823, 95% CI 1.517 to 5.254), diabetes ( OR = 5.413, 95% CI 1.162 to 25.222), intraoperative vitreous leakage ( OR = 4.751, 95% CI 1.877 to 8.309) and aqueous humor IL-1β ( OR = 1.195, 95% CI 1.031 to 1.386) were important risk factors for poor visual function recovery after refractive cataract surgery ( P<0.05). ROC and decision curve analysis found that the combined application of these risk factors had a high predictive efficacy for poor visual function recovery in patients after refractive cataract surgery, with an AUC (95% CI) of 0.885 (0.839 to 0.931) ( P<0.05). Conclusions:Older age, higher preoperative FBG and HbA 1c levels, diabetes, intraoperative vitreous overflow and higher levels of aqueous humor IL-1β are important factors contributing to poor visual function recovery in patients undergoing refractive cataract surgery. The model constructed based on these indicators has a high predictive efficacy for postoperative visual function recovery in patients undergoing refractive cataract surgery.

Objective: To explore cyberbullying and risk factors of middle school students, and to provide a reference for cyberbullying prevention in school settings. Methods: A stratified cluster random sampling method was used to select 12 940 students from three junior high schools and four senior high schools in Yixing City of Jiangsu Province, China, to conduct a questionnaire survey from March 1 to May 31, 2019. The Chi -square test was performed to compare differences in the prevalence of cyberbullying among groups with different sociodemographic characteristics, and the multivariate Logistic regression model was employed to analyze the risk factors. A risk predictive nomogram model was constructed and then verified. Results: Middle school students were found to be victims of cyberbullying at a rate of 12.3%. The Logistic regression results showed that alcohol use ( OR =1.93), lack of emotional management ( OR =1.30), feeling unsafe ( OR =1.70), not trusting people ( OR =1.66), increased daily online time ( OR =1.39), higher frequency of using social software or websites ( OR =2.24), poor relationships with family members ( OR =1.46), parental neglect ( OR =1.50), class leadership ( OR =1.30) and poor relationships with classmates ( OR =1.34) were risk factors for middle school students who were victims of cyberbullying ( P <0.05). Based on these 10 independent risk factors, the nomogram prediction model, had good discrimination ( AUC =0.73). Conclusion Cyberbullying is common among middle school students. Internet use, parental neglect and class leadership all have an impact on cyberbullying.

Objective:To explore the mediating effect of smoking, emotional control, and family relationship on the association between relational bullying victimization and perpetration.Method:A total of 11 462 participants were included in the study. Mediating effect model was used to analyze the mediating effect of smoking, emotional control, and family relationship between relational bullying victimization and perpetration.Results:Family relationship (mediation effect value: 0.119, 95% CI: 0.075-0.165, mediation ratio: 8.5%) and smoking (mediation effect value: 0.061, 95% CI: 0.031-0.105, mediation ratio: 4.4%) constitute a separate mediating effect. Family relationship, emotional control, and smoking constitute a chain mediation effect (mediation effect value: 0.007, 95% CI: 0.003-0.013, mediation ratio: 0.5%); family relationship and smoking constitute a chain mediation effect (mediation effect value: 0.036, 95% CI: 0.020-0.056, mediation ratio: 2.6%); emotional control and smoking constitute a chain mediating effect (mediation effect value: 0.007, 95% CI: 0.003-0.013, mediation ratio: 0.5%). Conclusion:Smoking, emotional control, and family relationship partially mediate relational bullying victimization and perpetration.

Objective: To explore the progression rate of cortid maximal plaque area and the risk of new ischemic cardiovascular disease (ICVD) in a rural cohort in Beijing.
Methods: The PRC-USA collaborative study had been regularly conducted in Shijingshan area in Beijing. The carotid ultrasound examination, ICVD risk factor and acute cardiovascular events follow-up were conducted in those participants. A total of 1479 subjects who received at least 2 carotid ultrasound examinations and had no cardiovascular disease before the second ultrasound were studied. They were divided into 5 groups:①Control group, the participants had no plaque detected by 2 ultrasounds; ② New plaque group, new plaque was found at the second ultrasound examination; ③ Plaque regression group; ④ Plaque stabilized group and ⑤ Plaque progression group. The hazard ratio (HR) between the progression rate of corotid maximal plaque area and new ICVD events was estimated by Cox proportional hazard regression analysis .
Results: Compared with Control group, the HR for new ICVD events were higher in groups②,③,④and⑤at 3.5, 5.7, 6.2 and 7.3 respectively, all P<0.05. The increasing trend of HRs remained signiifcant with the adjusted age and gender, P<0.001.
Conclusion: The progression rate of maximal corot id plaque area rate could predict the risk of new ICVD events in clinical practice.