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 explore the relationship and internal path between activities of daily living(ADL),sleep quality and mental health of community elderly people in Shanghai.Methods A questionnaire survey was conducted among community residents aged 60 years and older seeing doctors in community health care center of five streets in Shanghai during Sept to Dec,2021 using convenience sampling.Activities of Daily Living(ADL),Pittsburgh Sleep Quality Index(PSQI)and 10-item Kessler Psychological Distress Scale(K10)were adopted in the survey.Single factor analysis,correlation analysis and multiple linear regression were used to analyze the data.The effect relationship between the variables was tested using Bootstrap's mediated effects test.Results A total of 1 864 participants were included in the study.The average score was 15.53±4.47 for ADL,5.60±3.71 for PSQI and 15.50±6.28 for K10.The rate of ADL impairment,poor sleep quality,poor and very poor mental health of the elderly were 23.6%,27.3%,11.9%and 4.9%,respectively.ADL and sleep quality were all positively correlated with mental health(r=0.321,P＜0.001;r=0.466,P＜0.001);ADL was positively correlated with sleep quality(r=0.294,P＜0.001).Multiple linear results of factors influencing mental health showed that ADL(β= 0.457,95%CI:0.341-0.573),sleep quality(β =0.667,95%CI:0.598-0.737)and mental health were positively correlated(P＜0.001).Sleep quality partially mediated the relationship between ADL and mental health(95%CI:0.078-0.124)with an effect size of 33.0%.Conclusion Sleep quality is a mediator between ADL and mental health among community elderly people.Improving ADL and sleep quality may improve mental health in the population.

Objective:To construct a representative index system for evaluating pediatric orthopedic nursing quality, providing a basis for hospital pediatric orthopedic nursing quality assessment and monitoring.Methods:From April to July 2023, using the "structure-process-outcome" three-dimensional quality structure model as the theoretical framework, a literature review was conducted, and an item pool was formulated. Through two rounds of Delphi method expert consultations, the hierarchical analysis method was finally employed to determine the indicators and their weights at each level.Results:The effective recovery rates of the questionnaire of the two rounds of expert consultations were 100% (20/20), the authority coefficients of experts were 0.87 and 0.88, the coefficients of variation were 0.00 to 0.27 and 0.00 to 0.24. The Kendell harmony coefficients of the second and third indicators in the two rounds of inquiry were 0.140, 0.166 and 0.192, 0.161(all P<0.05). The final pediatric orthopedic nursing quality evaluation index system included 3 primary indicators, 21 secondary indicators and 83 tertiary indicators. Among the primary indicators, the weight of process quality was the highest at 0.493 4, followed by outcome quality at 0.310 8, and the lowest was structural quality at 0.195 8. In the secondary indicators, "assessment criteria of limb blood circulation" had the highest weight at 0.099 8. Conclusions:The constructed pediatric orthopedic nursing quality evaluation index system covers key aspects and is more operationally feasible. It provides better guidance for nursing interventions and quality control.

A major impedance to neuronal regeneration after peripheral nerve injury(PNI)is the activation of various programmed cell death mechanisms in the dorsal root ganglion.Ferroptosis is a form of pro-grammed cell death distinguished by imbalance in iron and thiol metabolism,leading to lethal lipid peroxidation.However,the molecular mechanisms of ferroptosis in the context of PNI and nerve regeneration remain unclear.Ferroportin(Fpn),the only known mammalian nonheme iron export protein,plays a pivotal part in inhibiting ferroptosis by maintaining intracellular iron homeostasis.Here,we explored in vitro and in vivo the involvement of Fpn in neuronal ferroptosis.We first delineated that reactive oxygen species at the injury site induces neuronal ferroptosis by increasing intracellular iron via accelerated UBA52-driven ubiquitination and degradation of Fpn,and stimulation of lipid peroxidation.Early administration of the potent arterial vasodilator,hydralazine(HYD),decreases the ubiquitination of Fpn after PNI by binding to UBA52,leading to suppression of neuronal cell death and significant ac-celeration of axon regeneration and motor function recovery.HYD targeting of ferroptosis is a promising strategy for clinical management of PNI.

BACKGROUND:Myocardial patches are used as an effective way to repair damaged myocardium,and there is controversy over which cells to use to make myocardial patches and how to maximize the therapeutic effect of myocardial patches in vivo. OBJECTIVE:To find out the best way to make myocardial patches by overviewing the cellular sources of myocardial patches and strategies for perfecting them. METHODS:The first author searched PubMed and Web of Science databases by using"cell sheet,cell patch,cardiomyocytes,cardiac progenitor cells,fibroblasts,embryonic stem cell,mesenchymal stem cells"as English search terms,and searched CNKI and Wanfang databases by using"myocardial patch,biological 3D printing,myocardial"as Chinese search terms.After enrollment screening,94 articles were ultimately included in the result analysis. RESULTS AND CONCLUSION:(1)The cellular sources of myocardial patches are mainly divided into three categories:somatic cells,monoenergetic stem cells,and pluripotent stem cells,respectively.There are rich sources of cells for myocardial patches,but not all of them are suitable for making myocardial patches,e.g.,myocardial patches made from fibroblasts and skeletal myoblasts carry a risk of arrhythmogenicity,and mesenchymal stem cells have a short in vivo duration of action and ethical concerns.With the discovery of induced multifunctional stem cells,a reliable source of cells for making myocardial patches is available.(2)There are two methods of making myocardial patches.One is using cell sheet technology.The other is using biological 3D printing technology.Cell sheet technology can preserve the extracellular matrix components intact and can maximally mimic the cell growth ring in vivo.However,it is still difficult to obtain myocardial patches with three-dimensional structure by cell sheet technology.Biologicasl 3D printing technology,however,can be used to obtain myocardial patches with three-dimensional structures through computerized personalized design.(3)The strategies for perfecting myocardial patches mainly include:making myocardial patches after co-cultivation of multiple cells,improving the ink formulation and scaffold composition in biological 3D printing technology,improving the therapeutic effect of myocardial patches,suppressing immune rejection after transplantation,and perfecting the differentiation and cultivation protocols of stem cells.(4)There is no optimal cell source or method for making myocardial patches,and myocardial patches obtained from a particular cell or technique alone often do not achieve the desired therapeutic effect.Therefore,researchers need to choose the appropriate strategy for making myocardial patches based on the desired therapeutic effect before making them.

The biological clock(also known as the circadian rhythm)is the fundamental reliance for all organisms on Earth to adapt and survive in the Earth's rotation environment.Circadian rhythm is the most basic regulatory mechanism of life activities,and plays a key role in maintaining normal physiological and biochemical homeostasis,disease occurrence and treatment.Recent studies have shown that the biologi-cal clock plays an important role in the development of oral tissues and in the occurrence and treatment of oral diseases.Since there is cur-rently no guiding literature on the research methods of biological clock in stomatology,researchers mainly conduct research based on pub-lished references,which has led to controversy about the research methods of biological clock in stomatology,and there are many confusions about how to rationally apply the research methods of circadia rhythms.In view of this,this expert consensus summarizes the characteristics of the biological clock and analyzes the shortcomings of the current biological clock research in stomatology,and organizes relevant experts to summarize and recommend 10 principles as a reference for the rational implementation of the biological clock in stomatology research.