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 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.

Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired clonal hematopoietic stem cell disease caused by abnormal expression of glycosylphosphatidylinositol (GPI) on the cell membrane due to mutations in the phosphatidylinositol glycan class A(PIGA) gene. It is commonly characterized by intravascular hemolysis, repeated thrombosis, and bone marrow failure, as well as multiple systemic involvement symptoms such as renal dysfunction, pulmonary hypertension, swallowing difficulties, chest pain, abdominal pain, and erectile dysfunction. Due to the rarity of PNH and its strong heterogeneity in clinical manifestations, multidisciplinary collaboration is often required for diagnosis and treatment. Peking Union Medical College Hospital, relying on the rare disease diagnosis and treatment platform, has invited multidisciplinary clinical experts to form a unified opinion on the diagnosis and treatment of PNH, and formulated the Expert Consensus of Multidisciplinary Diagnosis and Treatment for Paroxysmal Nocturnal Hemoglobinuria (2024), with the hope of promoting the standardization of PNH diagnosis and treatment.

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.