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 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.

Caloric restriction (CR) is a well-established dietary intervention known to extend healthy lifespan and exert positive effects on aging-related diseases, including cardiovascular conditions. Sirtuins, a family of nicotinamide adenine dinucleotide (NAD +)-dependent histone deacetylases, have emerged as key regulators of cellular metabolism, stress responses, and the aging process, serving as energy status sensors in response to CR. However, the mechanism through which CR regulates Sirtuin function to ameliorate cardiovascular disease remains unclear. This review not only provided an overview of recent research investigating the interplay between Sirtuins and CR, specifically focusing on their potential implications for cardiovascular health, but also provided a comprehensive summary of the benefits of CR for the cardiovascular system mediated directly via Sirtuins. CR has also been shown to have considerable impact on specific metabolic organs, leading to the production of small molecules that enter systemic circulation and subsequently regulate Sirtuin activity within the cardiovascular system. The direct and indirect effects of CR offer a potential mechanism for Sirtuin modulation and subsequent cardiovascular protection. Understanding the interplay between CR and Sirtuins will provide new insights for the development of interventions to prevent and treat cardiovascular diseases.

Aim To preliminarily evaluate the effects of hypobaric hypoxia on organism damage in rats with post-traumatic stress disorder(PTSD),with a view to laying a foundation for drug research in plateau PTSD.Methods The rats were randomly divided into four groups,namely,the control(Control)group,the sin-gle-prolonged stress(SPS)group,the hypobaric hy-poxia(HH)group and the single-prolonged stress combined with hypobaric hypoxia(SPS+HH)group.The PTSD model was firstly constructed using the SPS method for rats in the SPS and SPS+HH groups.On the second day,rats in the HH group and SPS+HH group were placed in a low-pressure hypoxia chamber at a simulated altitude of 6000 m for 14 days.General condition,behavior,blood tests,and histomorphology were examined in order to evaluate the damage caused by low pressure hypoxia in PTSD rats.Results The body mass of rats in the SPS+HH group was signifi-cantly reduced;the feces were partly hard and lumpy,and some of them were seen to have high viscosity.Anxiety-like and depression-like behaviors were ob-served in all groups except in the control group,in which hypobaric hypoxia aggravated the behavioral ab-normalities in SPS rats.Rats in both the SPS and SPS+HH groups had coagulation dysfunction and abnor-mally increased blood viscosity,which was significantly abnormal in the SPS+HH group;erythrocytes,hemo-globin,and erythrocyte specific volume in whole blood of rats in the SPS+HH group were significantly in-creased compared with those of rats in the SPS group;and serum TP,LDH and GLU levels were abnormal in rats in the SPS+HH group.Dilated and congested blood vessels were seen in hippocampal tissue,conges-ted central veins were seen in hepatic tissue,and dilat-ed and congested liver sinusoids with mild granuloma-tous degeneration of hepatocytes were seen in rats of the SPS+HH group.Conclusion Hypobaric hypoxia exacerbates depression-like and anxiety-like behaviors in PTSD rats,as well as hematological indices and his-tomorphometric abnormalities in PTSD rats.

Objective To investigate the curative effect of suture anchor(SA)repair combined with open reduction and internal fixation(ORIF)on patients with ankle joint fracture(AF)combined with deltoid ligament injury(DLI).Methods A total of 138 patients with AF combined with DLI admitted to our hospital from January 2020 to September 2022 were selected and divided into the control group(69 cases)and the observation group(69 cases)according to the random number table method.Patients in the control group were treated with ORIF,while patients in the observation group were treated with SA repair on the basis of the control group.The clinical efficacy,American Orthopedic Foot and Ankle Society(AOFAS)score,visual analogue scale(VAS)score,talus inclination angle,medial malleolar space of affected side,bone metabolic indexes[serum bone gla protein(BGP),β-collagen degradation product(β-CTX)]levels and the incidence of complications before and 3 months after treatment were compared between the two groups.Results The total effective rate in the observation group was higher than that in the control group,and the difference was statistically significant(P<0.05).Compared with before treatment,the talus inclination angle,medial malleolar space of affected side,VAS score,β-CTX level 3 months after treatment of patients in the two groups were reduced,while the AOFAS score and BGP level were increased,and the differences were statistically significant(P<0.05).After treatment,the AOFAS score and BGP level in the observation group were higher than those in the control group,while the talus inclination angle and medial malleolar space of affected side were smaller than those in the control group,and the VAS score and β-CTX level were lower than those in the control group,with statistically significant differences(P<0.05).The total incidence of complications in the observation group was lower than that in the control group,and the difference was statistically significant(P<0.05).Conclusion SA repair has a definite therapeutic effect on AF combined with DLI,which can improve patients' symptoms and promote the recovery of ankle joint function and bone metabolism.