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.

Colorectal cancer(CRC) is one of the most common malignant tumors worldwide, primarily originating from recurrent inflammatory bowel disease(IBD). Therefore, blocking the inflammation-cancer transformation in the colon has become a focus in the early prevention and treatment of CRC. The inflammation-cancer transformation in the colon involves multiple types of cells and complex pathological processes, including inflammatory responses and tumorigenesis. In this complex pathological process, immune cells(including non-specific and specific immune cells) and non-immune cells(such as tumor cells and fibroblasts) interact with each other, collectively promoting the progression of the disease. In traditional Chinese medicine(TCM), inflammation-cancer transformation in the colon belongs to the categories of dysentery and diarrhea, with the main pathogenesis being cold and heat in complexity. This paper first elaborates on the complex molecular mechanisms involved in the inflammation-cancer transformation process in the colon from the perspectives of inflammation, cancer, and their mutual influences. Subsequently, by comparing the pathogenic characteristics and clinical manifestations between inflammation-cancer transformation and the TCM pathogenesis of cold and heat in complexity, this paper explores the intrinsic connections between the two. Furthermore, based on the correlation between inflammation-cancer transformation in the colon and the TCM pathogenesis, this paper delves into the importance of the interaction between inflammation and cancer. Finally, it summarizes and discusses the clinical and basic research progress in the TCM intervention in the inflammation-cancer transformation process, providing a theoretical basis and treatment strategy for the treatment of CRC with integrated traditional Chinese and Western medicine.
Humans ; Colon/pathology* ; Integrative Medicine ; Animals ; Cold Temperature ; Cell Transformation, Neoplastic/drug effects* ; Medicine, Chinese Traditional ; Hot Temperature ; Inflammation ; Drugs, Chinese Herbal/therapeutic use* ; Colonic Neoplasms/drug therapy*

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

OBJECTIVE: The study aim was to investigate the effects of exposure to multiple environmental organic pollutants on cardiopulmonary health with a focus on the potential mediating role of oxidative stress. METHODS: A repeated-measures randomized crossover study involving healthy college students in Beijing was conducted. Biological samples, including morning urine and venous blood, were collected to measure concentrations of 29 typical organic pollutants, including hydroxy polycyclic aromatic hydrocarbons (OH-PAHs), bisphenol A and its substitutes, phthalates and their metabolites, parabens, and five biomarkers of oxidative stress. Health assessments included blood pressure measurements and lung function indicators. RESULTS: Urinary concentrations of 2-hydroxyphenanthrene (2-OH-PHE) ( β = 4.35% [95% confidence interval ( CI): 0.85%, 7.97%]), 3-hydroxyphenanthrene ( β = 3.44% [95% CI: 0.19%, 6.79%]), and 4-hydroxyphenanthrene (4-OH-PHE) ( β = 5.78% [95% CI: 1.27%, 10.5%]) were significantly and positively associated with systolic blood pressure. Exposures to 1-hydroxypyrene (1-OH-PYR) ( β = 3.05% [95% CI: -4.66%, -1.41%]), 2-OH-PHE ( β = 2.68% [95% CI: -4%, -1.34%]), and 4-OH-PHE ( β = 3% [95% CI: -4.68%, -1.29%]) were negatively associated with the ratio of forced expiratory volume in the first second to forced vital capacity. These findings highlight the adverse effects of exposure to multiple pollutants on cardiopulmonary health. Biomarkers of oxidative stress, including 8-hydroxy-2'-deoxyguanosine and extracellular superoxide dismutase, mediated the effects of multiple OH-PAHs on blood pressure and lung function. CONCLUSION Exposure to multiple organic pollutants can adversely affect cardiopulmonary health. Oxidative stress is a key mediator of the effects of OH-PAHs on blood pressure and lung function.
Humans ; Oxidative Stress/drug effects* ; Male ; Cross-Over Studies ; Female ; Young Adult ; Environmental Pollutants/toxicity* ; Environmental Exposure/adverse effects* ; Biomarkers/blood* ; Adult ; Blood Pressure/drug effects* ; Polycyclic Aromatic Hydrocarbons/urine* ; Beijing

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.