ObjectiveTo investigate the mechanism by which Jianpi Xiao'ai prescription （JPXAP） inhibits colorectal cancer progression by regulating the inducible nitric oxide synthase-arginase 1 （iNOS-ARG1） metabolic axis and inducing mitochondrial reactive oxygen species （mito-ROS）-mediated mitochondrial structural and functional impairment. MethodsAn arginine metabolism disorder model of human colorectal cancer HCT116 cells was established by combined treatment with recombinant human interferon-γ （IFN-γ， 10 μg·L^-1） and N（ω）-hydroxy-L-arginine （Nor-NOHA， 200 μmol·L^-1） for 24 h， followed by intervention with 5%， 10%， or 20% JPXAP-containing serum. Cell proliferation was assessed using cell counting kit-8 （CCK-8）， 5-ethynyl-2′-deoxyuridine （EdU） staining， and colony formation assays. Cell invasion and migration were evaluated using Transwell chamber and wound healing assays. Mitochondrial membrane potential （MMP） and ROS levels were assessed by JC-1 and MitoSOX staining， respectively. Mitochondrial ultrastructure was observed by transmission electron microscopy （TEM）. The expression of iNOS， ARG1， and mitochondrial dynamics-related proteins， including mitofusin 2 （MFN2） and dynamin-related protein 1 （DRP1）， was analyzed by Western blot and immunofluorescence. The levels of L-arginine， citrulline， and urea were determined by colorimetric methods and enzyme-linked immunosorbent assay （ELISA）. ResultsCompared with the blank group， the model group exhibited significantly upregulated iNOS expression， downregulated ARG1 expression， a decreased ARG1/iNOS ratio， reduced L-arginine and urea levels， and increased citrulline levels （P<0.05）. Meanwhile， mito-ROS accumulation was significantly increased， the JC-1 red/green fluorescence ratio was decreased， and mitochondria showed swelling and cristae disruption， indicating that metabolic disorder induced mitochondrial injury. Compared with the model group， all JPXAP-treated groups further decreased the ARG1/iNOS ratio， enhanced nitric oxide （NO） and reactive nitrogen species accumulation， further reduced L-arginine and urea levels， and increased citrulline levels （P<0.01）. EdU-positive rate， colony formation rate， wound healing rate， and Transwell invasion number all decreased significantly with increasing serum concentration （P<0.01）. Mito-ROS levels were further elevated， and the JC-1 red/green ratio further decreased. TEM revealed aggravated mitochondrial swelling and vacuolization. MFN2 expression was downregulated and DRP1 expression was upregulated （P<0.01），in a dose-dependent manner. ConclusionJPXAP further activates NO-mediated oxidative/nitrosative stress under arginine metabolism imbalance， inducing mito-ROS accumulation， MMP collapse， and mitochondrial dynamics imbalance， thereby inhibiting colorectal cancer cell proliferation and migration. These findings reveal an antitumor mechanism of JPXAP based on coordinated targeting of the "metabolism-mitochondria" axis.

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

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:To explore the relationship and underlying mechanisms between servant leadership and satisfaction of medical staff in tertiary hospitals, and to provide references for improving satisfaction of medical personnel.Methods:From January to June 2023, a questionnaire survey was conducted among on-duty medical staff at a tertiary hospital in Guangzhou using a simple random sampling method. Data corresponding to four key variables: servant leadership, hospital management level, affective commitment, and satisfaction of medical staff were collected. SPSS 25.0 software was used to perform independent samples t-tests and one-way analysis of variance (ANOVA) to examine group differences, and Pearson correlation analysis was conducted to explore the relationships among multiple variables. Amos 24.0 software was employed to construct a structural equation model to conduct confirmatory factor analysis of the four key variables, analyze potential mediating effects, and use multi-group analysis to examine differences in path parameters and structure among groups. Results:A total of 632 valid questionnaires were obtained. The satisfaction score of medical staff was (4.50±0.66)(maximum score was 5 points). Age, years of work experience, and job category had statistically significant effects on satisfaction of medical staff ( F = 5.799, 6.483, 7.671; P = 0.001). All four key variables were significantly positively correlated ( P<0.001). Servant leadership, hospital management level, and affective commitment all had direct positive effects on satisfaction of medical staff, with path coefficients of 0.207, 0.386, and 0.345, respectively ( P <0.05, critical ratio>1.96). Hospital management level and affective commitment each had independent partial mediating effects between servant leadership and satisfaction of medical staff (path coefficients of 0.353 and 0.067, respectively; P = 0.007, 0.018). They also jointly exerted a chain mediating effect (path coefficient of 0.243, P = 0.013). Differences in path effects among different job categories (clinical doctors, nurses, and administrative support staff) were statistically significant ( χ2 = 43.344, df = 24, P = 0.009). Conclusions:The servant leadership in tertiary hospitals can directly influence the satisfaction of medical staff, as well as indirectly influence it through emotional commitment and hospital management level. Moreover, the mechanisms of influence vary among medical staff of different professional categories. Tertiary hospitals should introduce and promote servant leadership styles, enhance the servant leadership behaviors of management personnel, and strengthen the synergistic effects of servant leadership, hospital management level, and affective commitment. Differential adjustment mechanisms should be implemented for different job categories.

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: 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.