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.

Endo-beta-N-acetylglucosaminidase (ENGase) is widely distributed in various organisms. The first reported ENGase activity was detected in Diplococcus pneumoniae in 1971. The protein (Endo D) was purified and its peptide sequence was determined in 1974. Three ENGases (Endo F1-F3) were discovered in Flavobacterium meningosepticum from 1982 to 1993. After that, the activity was detected from different species of bacteria, yeast, fungal, plant, mice, human, etc. Multiple ENGases were detected in some species, such as Arabidopsis thaliana and Trichoderma atroviride. The first preliminary crystallographic analysis of ENGase was conducted in 1994. But to date, only a few ENGases structures have been obtained, and the structure of human ENGase is still missing. The currently identified ENGases were distributed in the GH18 or GH85 families in Carbohydrate-Active enZyme (CAZy) database. GH18 ENGase only has hydrolytic activity, but GH85 ENGase has both hydrolytic and transglycosylation activity. Although ENGases of the two families have similar (β/α)8-TIM barrel structures, the active sites are slightly different. ENGase is an effective tool for glycan detection andglycan editing. Biochemically, ENGase can specifically hydrolyze β‑1,4 glycosidic bond between the twoN-acetylglucosamines (GlcNAc) on core pentasaccharide presented on glycopeptides and/or glycoproteins. Different ENGases may have different substrate specificity. The hydrolysis products are oligosaccharide chains and a GlcNAc or glycopeptides or glycoproteins with a GlcNAc. Conditionally, it can use the two products to produce a new glycopeptides or glycoprotein. Although ENGase is a common presentation in cell, its biological function remains unclear. Accumulated evidences demonstrated that ENGase is a none essential gene for living and a key regulator for differentiation. No ENGase gene was detected in the genomes of Saccharomyces cerevisiae and three other yeast species. Its expression was extremely low in lung. As glycoproteins are not produced by prokaryotic cells, a role for nutrition and/or microbial-host interaction was predicted for bacterium produced enzymes. In the embryonic lethality phenotype of the Ngly1-deficient mice can be partially rescued by Engase knockout, suggesting down regulation of Engase might be a solution for stress induced adaptation. Potential impacts of ENGase regulation on health and disease were presented. Rabeprazole, a drug used for stomach pain as a proton inhibitor, was identified as an inhibitor for ENGase. ENGases have been applied in vitro to produce antibodies with a designated glycan. The two step reactions were achieved by a pair of ENGase dominated for hydrolysis of substrate glycoprotein and synthesis of new glycoprotein with a free glycan of designed structure, respectively. In addition, ENGase was also been used in cell surface glycan editing. New application scenarios and new detection methods for glycobiological engineering are quickly opened up by the two functions of ENGase, especially in antibody remodeling and antibody drug conjugates. The discovery, distribution, structure property, enzymatic characteristics and recent researches in topical model organisms of ENGase were reviewed in this paper. Possible biological functions and mechanisms of ENGase, including differentiation, digestion of glycoproteins for nutrition and stress responding were hypothesised. In addition, the role of ENGase in glycan editing and synthetic biology was discussed. We hope this paper may provide insights for ENGase research and lay a solid foundation for applied and translational glycomics.

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.

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.

Objective:To evaluate the efficacy of oral midazolam solution for preoperative sedation in the pediatric outpatients undergoing root canal treatment under general anesthesia.Methods:One hundred and forty-seven pediatric patients of either sex, aged 2-7 yr, weighing 10-30 kg, of American Society of Anesthesiologists Physical Status classificationⅠ or Ⅱ, were divided into 3 groups ( n=49 each) using a random number table method: oral midazolam solution group (OM group), midazolam injection group (M group), and dexmedetomidine group (D group). In OM group, patients received oral midazolam solution at a dose of 0.5 mg/kg along with a placebo (an equivalent amount of normal saline based on body weight) administered via nasal drops. In M group, patients were given oral midazolam injection at a dose of 0.5 mg/kg along with a placebo via nasal drops. In D group, patients were administered a placebo orally along with dexmedetomidine at a dose of 2 μg/kg via nasal drops. The Induction Compliance Checklist (ICC) scores upon entering the operating room, sedation success rates (ICC score ≤ 3), drug acceptance scores, mask acceptance scores, and separation anxiety scores were recorded. The emergence time, time of stay in postanesthesia care unit (PACU), and occurrence of adverse events such as bradycardia, hypotension, hypoxemia, and laryngospasm during surgery and in PACU were recorded. Results:A total of 143 pediatric patients were finally included in the study, with 48 cases in OM group, 48 cases in M group and 47 cases in D group. Compared with M and D groups, the ICC scores upon entry to the operating room were significantly decreased, the sedation success rates were increased, drug acceptance scores were increased, separation anxiety scores were decreased, and mask acceptance scores were decreased in OM group ( P<0.05). Compared with D group, the ICC scores upon entry to the operating room were significantly decreased, the sedation success rates were increased, and mask acceptance scores were decreased in M group ( P<0.05). There were no statistically significant differences in the emergence time, time of stay in PACU, and incidence of adverse events during surgery and in PACU among the three groups ( P>0.05). Conclusions:Oral midazolam solution provides good effect with less adverse reactions when used for preoperative sedation in the pediatric outpatients undergoing root canal treatment under general anesthesia.

AIM To explore the clinical effects of Modified Sanzi Yangqin Decoction combined with acupuncture on patients with chronic obstructive pulmonary disease at stable stage complicated with sarcopenia.METHODS Ninety-four patients were randomly assigned into control group(47 cases)for 8-week intervention of conventional treatment,and observation group(47 cases)for 8-week intervention of Modified Sanzi Yangqin Decoction,acupuncture and conventional treatment.The changes in clinical effects,TCM syndrome scores,and levels of pulmonary function indices(FVC,FEV1,FEV1/FVC),inflammatory factors(CRP,IL-6,TNF-α),muscle-specific biomarkers(MSTN,IGF-1),relevant scale scores(SARC-F,SPPB)and skeletal muscle mass index were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P<0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,inflammatory factors,MSTN,SARC-F score(P<0.05),and increased pulmonary function indices,IGF-1,SPPB score,skeletal muscle mass index(P<0.05),especially for the observation group(P<0.05).CONCLUSION For the patients with chronic obstructive pulmonary disease at stable stage complicated with sarcopenia,Modified Sanzi Yangqin Decoction combined with acupuncture exhibits significant clinical efficacy.