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 explore the association between sleep quality and dry eye symptoms in adolescents,so as to provide the evidence for reducing the prevalence of dry eye symptoms. Methods: The study population was adolescents aged 12-24 years from the Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, which was conducted from 20 June to 31 August 2022. A stratified random sampling and quota sampling method was used to select 6 456 adolescents within mainland China. The Ocular Surface Disease Index (OSDI) and Brief version of the Pittsburgh Sleep Quality Index (B-PSQI) were used to assess dry eye symptoms and sleep quality. Multiple Logistic regression model was used to explore the relationship between sleep quality and dry eye symptoms in adolescents. The influence of gender on the association was explored by using interaction terms. Results: A total of 2 815 adolescents reported having dry eye symptoms, with a prevalence of 43.6%. Logistic regression analysis results showed an increased risk of exacerbation of dry eye symptoms in adolescents with poor sleep quality. The OR (95% CI ) for mild, moderate, and severe dry eye symptoms groups were 1.39(1.16-1.67), 1.52(1.28-1.81), and 2.35(2.02-2.72), respectively, compared with the ocularly normal group ( P <0.05). There was a significant interaction between sleep quality and gender on dry eye symptoms in adolescents ( P <0.01). Conclusions Sleep quality is associated with dry eye symptoms in adolescents, and those with poor sleep quality have a higher risk of dry eye symptoms. The effect of sleep quality on dry eye symptoms is greater in boys.

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 feasibility and value of participatory bilingual teaching in nuclear medicine teaching.Methods:With the same chapter of Nuclear Medicine as the teaching contents, the traditional method of cramming bilingual teaching was used for the clinical medical students in the class of 2014 of the seven-year program and the five-year program, and the method of participatory bilingual teaching was used for the students in the class of 2015. The methods of periodical achievement test, questionnaire survey, and final examination were used to evaluate the teaching effect of the above two teaching modes.Results:In the periodical achievement test, the participatory bilingual teaching group had significantly higher scores than the cramming teaching group in terms of mean score, case analysis, and English questions [seven-year program: (82.13±10.72), (35.74±4.13), and (23.03±3.40) vs. (79.21±11.31), (33.86±5.23), and (22.12±2.75), P<0.05; five-year program: (78.66±12.75), (34.30±5.59), and (22.45±2.91) vs. (75.29±10.81), (32.70±6.04), and (21.36±3.09), P<0.05]. The questionnaire survey showed that participatory bilingual teaching had a better degree of satisfaction than cramming teaching. The participatory bilingual teaching group had a better score than the cramming teaching group in the final examination, but with no significant difference between the two groups. Conclusions:In the teaching of nuclear medicine, the participatory bilingual teaching mode can significantly improve teaching effect and achieve the teaching goal efficiently.

Cerebral hyperperfusion syndrome (CHS) is a rare but serious complication after cerebral revascularization, which may lead to catastrophic consequences. The mechanism of CHS is not fully understood, and it may be related to cerebral autoregulation dysfunction and the increase of blood pressure after operation. Timely detection and treatment of cerebral hyperperfusion can avoid CHS. This article reviews the pathogenesis, diagnosis, clinical manifestations, prevention and treatment of CHS.

Major depressive disorder(MDD)is a prevalent con-dition associated with substantial impairment and low remission rates.Traditional antidepressants demonstrate delayed effects,low cure rate,and inadequate therapeutic effectiveness for man-aging treatment-resistant depression(TRD).Several studies have shown that ketamine,a non-selective N-methyl-D-aspartate receptor(NMDAR)antagonist,can produce rapid and sustained antidepressant effects.Ketamine has demonstrated efficacy for reducing suicidality in TRD patients.However,the pharmaco-logical mechanism for ketamine's antidepressant effects remains incompletely understood.Previous research suggests that the an-tidepressant effects of ketamine may involve the monoaminergic,glutamatergic and dopaminergic systems.This paper provides an overview of the pharmacological mechanism for ketamine's anti-depressant effects and discuss the potential directions for future research.

Aim To investigate the regulatory mecha-nism of butin on the proliferation,migration,cycle blockage and pyroptosis related inflammatory factors in human fibroblast-like synoviocytes of rheumatoid arthri-tis(HFLS-RA).Methods Cell proliferation,migra-tion and invasion were studied using cell migration and invasion assays.Cell cycle was detected by flow cytom-etry,and the expression of the pyroptosis-associated in-flammatory factors IL-1β,IL-18,caspase-1 and caspase-3 was detected by ELISA,RT-qPCR and West-ern blot.Results Migration and invasion experiments showed that the cell proliferation rate of the butin group was lower than that of the blank control group(P＜0.05).Cell cycle analysis demonstrated that in the G0/G1 phase,the DNA expression was elevated in the medium and high-dose groups of butin(P＜0.05),while in the G2 and S phases,the DNA expression was reduced in the medium and high-dose groups of butin(P＜0.05).The results of ELISA,RT-qPCR and Western blot assay revealed that the expression of IL-1β,IL-1 8,caspase-1,and caspase-3 decreased in the butin group compared with the IL-1β+caspase-3 in-hibitor group(P＜0.05).Conclusions Butin inhib-its HFLS-RA proliferation by inhibiting the synthesis of inflammatory vesicles by caspase-1 in the pyroptosis pathway,thereby reducing the production and release of inflammatory factors such as IL-1β and IL-18 down-stream of the pathway,and also inhibits HFLS-RA pro-liferation by exerting a significant blocking effect in the G1 phase,which may be one of the potential mecha-nisms of butin in the treatment of RA.