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: 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.

Objective To investigate the clinical characteristics of female migraine patients with patent foramen ovale(PFO)and design a risk prediction model for PFO in female migraine patients(migraineur patients PFO risk prediction model,MPRPM).Methods Female migraine patients who visited Zhongshan Hospital,Fudan University from Jun 1,2019 to Dec 31,2022 were included.Preoperative information and follow-up results after discontinuation of medication were collected.Patients were divided into PFO-positive and PFO-negative groups based on transesophageal echocardiography results.A multivariate Logistic regression model and a random forest model were constructed,and the random forest model was validated multidimensionally.Key features were selected based on the mean decrease accuracy(MDA)to construct MPRPM.Results A total of 305 female patients were included in the study,with 204 patients in the PFO-positive group and 101 patients in the PFO-negative group.Multivariate Logistic regression analysis showed that age at migraine onset,attack frequency,severe impact on life during attacks,exercise-related headaches,menstruation-induced headaches,aura migraines,and a history of cryptogenic stroke were predictive factors for PFO positivity.The random forest model effectively predicted the incidence of PFO in female migraine patients,with an AUC of 0.895(95%CI:0.847-0.943).MPRPM demonstrated a sensitivity of 71.6%and specificity of 91.1%(AUC:0.862,95%CI:0.818-0.906,P<0.001).The optimal cut-off value was 2.5 points.Patients correctly classified by the model showed a higher rate of symptom improvement compared to incorrectly classified patients(94.3%vs.82.0%,P=0.023).Conclusion We identified predictive factors for PFO in migraine patients.MPRPM can provide guidance in the diagnostic process and therapeutic decision-making for female migraine patients,assist in patient triage,and reduce the healthcare burden.

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.