Objective: To investigate the visual acuity and correction conditions of children and adolescents in Shanghai, so as to provide a scientific basis for developing intervention measures to prevent myopia and protect vision among children and adolescents. Methods: From October to December 2022, a stratified cluster random sampling survey was conducted, involving 47 034 students from 16 municipal districts in Shanghai, covering kindergartens (≥5 years), primary schools, middle schools, general high schools and vocational high schools. According to the Guidelines for Screening Refractive Errors in Primary and Secondary School Students, the Standard Logarithmic Visual acuity Chart was used to examine naked vision and corrected vision of students, and general information was collected. The distribution and severity of visual impairment in different age groups were analyzed, and χ 2 tests and multivariate Logistic regression were used to explore factors associated with visual impairment. Results: The detection rate of visual impairment among children and adolescents was 76.2%, with a higher rate among females (78.8%) than males ( 73.8 %), higher among Han ethic students ( 76.2 %) than minority students (71.2%), and higher among urban students (76.7%) than suburban students (75.8%), all with statistically significant differences ( χ 2=162.6, 10.4, 5.5, P <0.05). The rate of visual impairment initially decreased and then increased with age, reaching its lowest at age 7 (53.8%) and peaking at age 17 (89.6%) ( χ 2 trend = 3 467.0 , P <0.05). Severe visual impairment accounted for the majority, at 56.6%, and there was a positive correlation between the severity of visual impairment and age among children and adolescents ( r =0.45, P <0.05). Multivariate Logistic regression showed that age, BMI, gender, ethnicity and urban suburban status were associated with visual impairment ( OR =1.18, 1.01, 1.38 , 0.79, 0.88, P <0.05). Among those with moderate to severe visual impairment, the rate of spectacle lens usage was 62.8%, yet only 44.8 % of those who used spectacle lens had fully corrected visual acuity. Females (64.9%) had higher spectacle lens usage rates than males (60.6%), and general high school students had the highest spectacle lens usage (83.9%), and there were statistically significant differences in gender and academic stages ( χ 2=57.7, 4 592.8, P <0.05). Conclusions The rate of spectacle lens usage among students with moderate to severe visual impairment is relatively low, and even after using spectacle lens, some students still do not achieve adequate corrected visual acuity. Efforts should focus on enhancing public awareness of eye health and refractive correction and improving the accessibility of related health services.

Objective To understand the current situation of nutrition literacy of primary and secondary school students in Shijingshan District of Beijing, and analyze its influencing factors, and to put forward targeted suggestions for improving the students’ nutrition literacy and promoting their healthy growth. Methods A multi-stage stratified cluster sampling method was used to select 2480 primary and secondary school students and their parents from 5 primary schools, 3 middle schools and 1 high school in Shijingshan District. The multivariate logistic regression model was used to analyze the factors influencing the attainment rate of nutrition literacy. Results The median score of nutrition literacy of 2480 primary and secondary school students from grades 1 to 12 was 77.86 (in hundred-mark system), the quartile range (IQR) was 16.96, and the attainment rate of nutrition literacy was 42.46%. The cognitive level (45.12%) was higher than the skill level (41.20%) among students from grades 3 to 12. In terms of skills, the attainment rate of food preparation was the lowest, at 30.38%. The scores of nutrition literacy of girls were higher than those of boys, and the scores of primary school students were higher than those of secondary school students. Students with different levels of caregiver’s education, family income, and family food environment had different scores of nutrition literacy, and the differences were statistically significant (P<0.05). Multivariate logistic regression analysis showed that the attainment rate of nutrition literacy was closely related to student’s gender and study stage, caregiver’s education level, and family food environment. Conclusion The nutrition literacy of primary and secondary school students in Shijingshan District still needs to be improved, especially in the aspect of skills. Targeted nutrition education should be carried out.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: To investigate the clinical characteristics of induced labor in twin pregnancy and the related factors of induced labor failure. Methods: The clinical data of twin pregnant women who underwent induced labor in Peking University Third Hospital from January 2016 to December 2022 were retrospectively analyzed. According to whether they had labor or not after induction, pregnant women were divided into the success group (pregnant women who had labor after induction, 72 cases) and the failure group (pregnant women who did not have labor after induction, 30 cases). Logistic regression was used to analyze the related factors of induction failure in twin pregnant women. Results: The parity and cervical Bishop score in the failure group were significantly lower than those in the success group, while the proportion of dichorionic diamniotic twins, assisted reproductive technology pregnancy and cervical Bishop score <6, postpartum hospital stay and total hospital stay in the failure group were significantly higher than those in the success group (all P<0.05). The proportion of induced labor by artificial rupture of membranes ± oxytocin intravenous infusion in the success group was 72.2% (52/72), which was significantly higher than that in the failure group (46.7%, 14/30; P=0.030). There were no significant differences between the two groups in the gestational age at delivery, the incidence of severe postpartum hemorrhage and blood transfusion, the amount of postpartum hemorrhage, the neonatal weight of two fetuses, the incidence of neonatal asphyxia, and the proportion of neonates admitted to the neonatal intensive care unit (all P>0.05). There were no severe perineal laceration and hysterectomy in all pregnant women. Multivariate logistic regression analysis showed that primipara (OR=3.064, 95%CI: 1.112-8.443; P=0.030) and cervical Bishop score <6 (OR=5.208, 95%CI: 2.008-13.508; P=0.001) were the independent risk factors for induction failure in twin pregnancy. Conclusions: Elective induction of labor in twin pregnancy is safe and feasible. It is helpful to improve the success rate of induction of labor by strictly grasping the timing and indications of termination of pregnancy, choosing the appropriate method of induction according to the condition of the cervix, and actively promoting cervical ripening .
Infant, Newborn ; Pregnancy ; Female ; Humans ; Pregnancy Trimester, Third ; Pregnancy, Twin ; Postpartum Hemorrhage/etiology* ; Retrospective Studies ; Labor, Induced/methods* ; Cervical Ripening

Aural vertigo frequently encountered in the otolaryngology department of traditional Chinese medicine （TCM） mainly involves peripheral vestibular diseases of Western medicine， such as Meniere's disease， benign paroxysmal positional vertigo， vestibular neuritis， and vestibular migraine， being a hot research topic in both TCM and Western medicine. Western medical therapies alone have unsatisfactory effects on recurrent aural vertigo， aural vertigo affecting the quality of life， aural vertigo not relieved after surgery， aural vertigo with complex causes， and children's aural vertigo. The literature records and clinical practice have proven that TCM demonstrates unique advantages in the treatment of aural vertigo. The China Association of Chinese medicine sponsored the "17th youth salon on the diseases responding specifically to TCM： Aural vertigo" and invited vertigo experts of TCM and Western medicine to discuss the difficulties and advantages of TCM diagnosis and treatment of aural vertigo. The experts deeply discussed the achievements and contributions of TCM and Western medicine in the diagnosis and treatment of aural vertigo， the control and mitigation of the symptoms， and the solutions to disease recurrence. The discussion clarified the positioning and advantages of TCM treatment and provided guidance for clinical and basic research on aural vertigo.

Six compounds were isolated from the roots of Ephedra sinica Stapf using various chromatographic techniques such as silica gel column chromatography, thin layer chromatography and semi-preparative HPLC. Their chemical structures were identified by analysis of physicochemical properties and spectral data, and determined as (Z)-docosanylferulate (1), (E)-docosanylferulate (2), bis (2-ethylheptyl) phythalate (3), 2,2′-oxybis (1,4-di-tert-butylbenzene) (4), diisobutyl phthalate (5), bis (2-ethylhexyl) phthalate (6). Among them, compound 1 is a new compound, compounds 2-4 were first isolated from Ephedra. A corticosterone-induced PC-12 cell injury model was used for compound activity screening. The results showed that compounds 1 and 5 significantly improved corticosterone-induced PC-12 cell injury and significantly increased 5-HT₇ receptor protein expression in the cells, indicating potential antidepressant activity.