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.

To investigate body composition and associated factors in adolescents undergoing long-term regular sports training.

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 determine the acetylation level of nucleophosmin(NPM)in female breast cancer and to discuss its function through mutation of modified lysine sites.To construct positive and negative NPM mutants on its acetylated lysine sites and to express them in breast cancer cells.Methods Acetylation level and acetylated lysine sites of NPM in three breast cancer tissues and para-carcinoma tissues were detected by acetylome technology;NPM mutants were constructed by site-directed mutagenesis PCR,specific PCR products were digested by DpnI and transformed into Escherichia coli(E.coli)to obtain specific plasmids for mutants;The accuracy of mutants were verified by double restriction enzyme digestion and sequencing;The mutants were expressed in BT-549 cells by transient transfection and verified by RT-PCR method.Protein expression and acetylation level of NPM were validated by Western blotting;Function of NPM acetylation was analyzed by proteomic detection and bioinformatic analysis.Results The 27th and 32nd lysine of NPM were highly acetylated in breast cancer tissues,which were 2.76 and 2.22 times higher than those in adjacent normal tissues,respectively;The NPM mutants showed the same molecular weight as that of wild type NPM and contained expected mutation sites;Corresponding NPM mRNA levels of BT-549 cells transfected with NPM mutants were significantly increased.With the increase of wild type NPM expression level,NPM acetylation level increased,while decreased after 27th lysine underwent negative mutation.NPM acetylation can significantly change the expression levels of 101 proteins in BT-549 cells,which are enriched in regulation of cellular macromolecule biosynthesis,DNA-template transcription,RNA biosynthesis and RNA metabolism process.Conclusion NPM is highly acetylated in breast cancer and can play a key role in cellular macromolecule biosynthesis,DNA-templated transcription,RNA biosynthesis and RNA metabolism process.

Persistent left superior vena cava(PLSVC)is a common congenital anomaly of systemic venous drainage,often draining into the right atrium without the need for special treatment.Sometimes,PLSVC drains into the left atrium,creating a right-to-left shunt,leading to reduced blood oxygen saturation and paradoxical embolism,requiring intervention.Traditional surgical ligation of PLSVC is the conventional approach for managing abnormal shunting,but it is associated with significant trauma and carries the risk of damaging the phrenic nerve.Here,we present a case of a patient with right heart dysfunction due to an untreated PLSVC-left atrium communication after corrective surgery for complex congenital heart disease,resulting in left-to-right shunting postoperatively.The patient was successfully treated by using a Plug vascular occluder via a transseptal approach to occlude the PLSVC.To our knowledge,this is the first report of successful closure of the left-to-right shunting through the heart chambers via a transseptal approach,indicating that interventional occlusion is an ideal management approach.

Low-level patent foramen ovale nonocclusion(PFO)is a rare type of PFO in which the PFO opening is low during transcatheter closure of PFO and the distance between the PFO left atrial opening and the root of the septal side of the mitral valve is less than 9 mm,and the smallest model of the current double-disk PFO occluder(18/18)commonly used in clinical practice for low-level PFOs can touch the mitral valve,resulting in increased risk of mitral regurgitation or leaflet abrasion.The risk of mitral regurgitation or leaflet abrasion is increased,and transcatheter closure of PFO procedure can only be abandoned when encountered intraoperatively.In this article,we present a case of successful transcatheter closure of a low-level PFO using the Amplatzer ADOⅡ occluder,which provides new ideas and strategies to deel wtih this rare type of PFO.

Objective To compare the bioavailability and bioequivalence of pivastatin calcium dispersive tablets in healthy Chinese subjects.Methods A single dose of pitavastatin calcium(2 mg)was orally administered to the test preparation or reference preparation under fasting and postprandial conditions,respectively.The plasma concentrations of pitavastatin calcium were measured at different time points before and after administration by high performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS).The bioequivalence of the two formulations was evaluated.Results Subjects received pitavastatin calcium test preparation and reference preparation in fasting condition,the Cmax were(47.79±23.99)and(46.03±21.82)ng·L-1;AUC0_,were(96.56±42.64)and(97.96±35.40)ng·h·L-1;AUC0_∞ were(102.09±43.01)and(103.46±35.62)ng·h·L-1,respectively.The 90％confidence intervals of the geometric mean ratios of Cmax,AUC0_t and AUC0-∞ of pitavastin-calcium test formulation and reference formulation were 96.28％-111.16％,94.46％-101.19％and 94.77％-101.31％,respectively.Subjects received pitavastatin calcium test preparation and reference preparation in fasting condition,the Cmax were(27.32±10.68)and(28.58±11.39)ng·L-1;AUC0_t were(82.76±27.58)and(84.06±29.12)ng·h·L-1;AUC0_∞ were(87.88±26.93)and(89.29±29.18)ng·h·L-1,respectively.The 90％confidence intervals of the geometric mean ratios of Cmax,AUC0_t and AUC0_∞ of the test formulation and the reference formulation of pitavastatin calcium were 87.39％-102.10％,94.62％-101.34％and 94.88％-101.47％,respectively.All of them were within the bioequivalence range of 80.00％to 125.00％.Conclusion Two pivastatin calcium dispersion tablets were bioequivalent and safe in healthy Chinese adult subjects.