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 explore the influence of environment temperature on the incidence of testicular torsion.Methods:We collected the clinical data on 172 cases of testicular torsion diagnosed in the Second Hospital of Hebei Medical University from De-cember 2013 to December 2020.According to the local environment temperature on the day of onset,we divided the patients into groups A(below 0℃),B(0-10℃),C(10-20℃)and D(above 20℃),and compared the incidence rates of testicular torsion among the four groups,followed by correlation analysis.Results:The incidence rate of testicular torsion was 12.8％(n=22)in group A,35.5％(n=61)in B,34.9％(n=60)in C and 16.9％(n=29)in D,the highest at 0-10℃ in group B,with sta-tistically significant difference among the four groups(x2=29.07,P＜0.001).Spearman correlation analysis indicated that the inci-dence of testicular torsion was negatively correlated with the environment temperature(r=-0.261,P＜0.01),with no statistically significant difference among different seasons(x2=5.349,P＞0.05),but higher in autumn and winter than in the other two sea-sons.Conclusion:The incidence of testicular torsion is negatively correlated with the environment temperature,elevated when the temperature decreases,but has no statistically significant difference among different seasons,though relatively higher in autumn and winter.

Objective:To explore the effects of RNF113A on the proliferation,migration,in-vasion,apoptosis,and autophagy of hepatocellular carcinoma cells.Methods:Hep3B cells were divided into control group and RNF113A overexpression group(RNF113A-OE),HepG2 was divided into control group and RNF113A knockdown group(si-RNF113A),CCK-8 assay was used to detect changes in cell viability,clone formation assay was used to detect changes in cell proliferation abili-ty,Transwell assay was used to detect changes in cell invasion ability,wound healing assay was used to detect changes in cell migration ability,and flow cytometry was used to detect changes in cell apoptosis ability,Western blot experiments were used to detect changes in protein expression of autophagy related genes and AMPK signaling pathway related genes.Results:Compared with the control group,the proliferation,cloning,invasion,and migration abilities of Hep3B cells in the RNF113A-OE group were improved,while apoptosis and autophagy abilities were decreased,and the AMPK signaling pathway was inhibited;In the si-RNF113A group,the proliferation,cloning,in-vasion,and migration abilities of HepG2 cells were significantly reduced,while apoptosis and au-tophagy abilities were increased,and the activation of the AMPK signaling pathway was promoted.Conclusion:RNF113A promotes the proliferation,cloning,invasion,and migration of hepatocel-lular carcinoma cells,and inhibited apoptosis and autophagy by inhibiting the AMPK signaling path-way.

Meridian-tendon is a central concept in meridian theory of TCM, and its basic research has been increasingly emphasized. While there is no unified understanding of the essence of meridian-tendon, the concept that function of fascia could partially reflect the functions of meridian-tendons has reached consensus in the academic community. This article suggests that under the guidance of meridian-tendon theory, based on previous research foundation of fascia, focusing on adopting fascia research methods, the mechanisms of tender point hyperalgesia and abnormal proliferation related to meridian lesions should be adopted to explain yitong weishu (taking the worst painful sites of muscle spasm as the points), and the mechanisms of meridian intervention efficacy should be adopted to explain yizhi weishu (feelings from patients and acupuncture operators). Furthermore, this article provides an analysis of the future trends in basic research of meridian tendons.
Humans ; Meridians ; Acupuncture Therapy ; Acupuncture ; Tendons ; Pain ; Research Design ; Acupuncture Points

Humans ; Mycobacterium tuberculosis/genetics* ; Microspheres ; Antitubercular Agents/pharmacology* ; Mutation ; Microbial Sensitivity Tests ; Fluoroquinolones ; Drug Resistance, Bacterial/genetics* ; Tuberculosis, Multidrug-Resistant/microbiology*

In 2021, a total of 151 pregnant women were selected from the suburb of Shanghai. A questionnaire survey was conducted to obtain data about maternal age, gestational week, total annual household income, education level and passive smoking among pregnant women and one spot urine was collected. The concentrations of eight neonicotinoid pesticides and four metabolites in urine were measured by ultra-high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry. The differences in detection frequencies and concentrations of neonicotinoid pesticides and their metabolites among pregnant women with different characteristics were compared, and the influencing factors of the detection of neonicotinoid pesticides in urine were analyzed. The results showed that at least one neonicotinoid pesticide was detected in 93.4% (141 samples) of urine samples. The detection frequencies of N-desmethyl-acetamiprid, clothianidin, thiamethoxam, and N-desmethyl-clothianidin were high, about 78.1% (118 samples), 75.5% (114 samples), 68.9% (104 samples), and 44.4% (67 samples), respectively. The median concentration of the sum of all neonicotinoid pesticides was 2.66 μg/g. N-desmethyl-acetamiprid had the highest detection concentration with a median concentration of 1.04 μg/g. A lower urinary detection frequency of imidacloprid and its metabolites was seen in pregnant women aged 30-44 years [OR (95%CI): 0.23 (0.07-0.77)]. A higher detection frequency of clothianidin and its metabolites was seen in pregnant women with per capita annual household income≥100 000 yuan [OR (95%CI): 6.15 (1.56-24.28)]. There was widespread exposure to neonicotinoid pesticides and their metabolites in pregnant women from the suburb of Shanghai, which might pose potential health risks to pregnant women, and maternal age and household income were potential influencing factors of the exposure to neonicotinoid pesticides.
Humans ; Female ; Pregnancy ; Pesticides/analysis* ; Pregnant Women ; China ; Neonicotinoids/analysis* ; Insecticides