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.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.
Humans ; Muscle, Skeletal ; Electromyography/methods* ; Electroencephalography/methods* ; Brain ; Brain Mapping

The gene GeDRP1E encoding dynamin-related protein 1E in Gastrodia elata was cloned by specific primers which were designed based on the transcriptome data of G. elata. Bioinformatics analysis on GeDRP1E gene was carried out by using ExPASy, ClustalW, MEGA, etc. Positive transgenic Arabidopsis plant and potato minituber were obtained with the genetic transformation system of Arabidopsis and potato. The plant height and seed setting rate of transgenic Arabidopsis, and agronomic characters, such as size, weight and starch content of potato minituber of transgenic potato were tested and analyzed. And GeDRP1E gene function was preliminarily investigated. The results showed that the open reading frame of GeDRP1E gene was 1 899 bp in length and 632 amino acids residues were encoded, with a relative molecular weight of 69.90 kDa and a molecular formula of C₃₀₇₉H₄₉₇₃N₈₈₃O₉₃₃S₁₉. It was predicted that the theoretical isoelectric point was 7.27, the instability coefficient was 43.34, and the average hydrophilicity index was -0.259, which was indicative of an unstable hydrophilic protein. GeDRP1E has no transmembrane structure and signal peptide, and was localized in the cytoplasm. The phylogenetic tree showed that GeDRP1E was highly homologous with DRP1E proteins of other plant species, among which GeDRP1E had the highest homology with DcDRP1E (XP_020689662.1) in Dendrobium candidum, reaching 90.05%. GeDRP1E plant expression vector pCambia1300-35Spro-GeDRP1E was constructed by double digests, and Arabidopsis complementary mutant and potato overexpression strain of GeDRP1E gene were obtained by Agrobacterium-mediated gene transformation. Compared with the Arabidopsis AtDRP1E mutant, the height and seed setting rate of the GeDRP1E complementation mutant were rescued. The minituber of GeDRP1E overexpression potato had larger size, heavier weight and higher starch content, comparing to wild-type potato. It was preliminarily induced that GeDRP1E was involved in mitochondrial morphology regulation, which related to the growth and development of Arabidopsis plants and potato miniature. The research results laid a foundation for further elucidating the molecular mechanisms underlying the growth and development of G. elata tuber development.

Objective To understand the respiratory protection competency of staff in hospitals.Methods Staff from six hospitals of different levels and characteristics in Beijing were selected,including doctors,nurses,medical technicians,and servicers,to conduct knowledge assessment on respiratory protection competency.According to exposure risks of respiratory infectious diseases,based on actual cases and daily work scenarios,content of respira-tory protection competency assessment was designed from three aspects:identification of respiratory infectious di-seases,transmission routes and corresponding protection requirements,as well as correct selection and use of masks.The assessment included 6,6,and 8 knowledge points respectively,with 20 knowledge points in total,all of which were choice questions.For multiple-choice questions,full marks,partial marks,and no mark were given respective-ly if all options were correct,partial options were correct and without incorrect options,and partial options were correct but with incorrect options.Difficulty and discrimination analyses on question of each knowledge point was conducted based on classical test theory.Results The respiratory protection competency knowledge assessment for 326 staff members at different risk levels in 6 hospitals showed that concerning the 20 knowledge points,more than 60％participants got full marks for 6 points,while the proportion of full marks for other questions was relatively low.Less than 10％participants got full marks for the following 5 knowledge points:types of airborne diseases,types of droplet-borne diseases,conventional measures for the prevention and control of healthcare-associated infec-tion with respiratory infectious diseases,indications for wearing respirators,and indications for wearing medical protective masks.Among the 20 knowledge questions,5,1,and 14 questions were relatively easy,medium,and difficult,respectively;6,1,4,and 9 questions were with discrimination levels of ≥0.4,0.30-0.39,0.20-0.29,and ≤0.19,respectively.Conclusion There is still much room for hospital staff to improve their respiratory protection competency,especially in the recognition of diseases with different transmission routes and the indications for wearing different types of masks.

Objective:To analyze the methylation characteristics of the lymphocyte-specific protein-tyrosine kinase (LCK) promoter region in the peripheral blood circulation of rheumatoid arthritis (RA) patients and its correlation with clinical indicators.Methods:Targeted methylation sequencing was used to compare the methylation levels of 7 CpG sites in the LCK promoter region in the peripheral blood of RA patients with healthy controls (HC) and osteoarthritis (OA) patients. Correlation analysis and ROC curve construction were performed with clinical information.Results:Non-parametric tests revealed that compared with HC [0.53(0.50, 0.57)] and OA patients [0.59(0.54, 0.62), H=47.17, P<0.001], RA patients [0.63(0.59, 0.68)] exhibited an overall increase in methylation levels. Simultaneously, when compared with the HC group [0.38(0.35, 0.41), 0.59(0.55, 0.63), 0.60(0.55, 0.64), 0.59(0.55, 0.63), 0.58(0.53, 0.62), 0.45(0.43, 0.49), 0.57(0.54, 0.61)], the RA group [0.46(0.42, 0.49), 0.70(0.65, 0.75), 0.70(0.66, 0.76), 0.70(0.65, 0.75), 0.69(0.64, 0.74), 0.55(0.51, 0.59), 0.68(0.63, 0.73)] showed a significant elevation in methylation levels at CpG sites cg05350315_60, cg05350315_80, cg05350315_95, cg05350315_101, cg05350315_104, cg05350315_128, and cg05350315_142, with statistically significant differences ( Z=-5.63, -5.89, -5.91, -5.89, -5.98, -5.95, -5.95, all P<0.001). Compared with the OA group [0.65(0.59, 0.69), 0.65(0.60, 0.69), 0.64(0.58, 0.68), 0.50(0.45, 0.54), 0.63(0.58, 0.67)], the RA group [0.70(0.66, 0.76), 0.70(0.65, 0.75), 0.69(0.64, 0.74), 0.55(0.51, 0.59), 0.68(0.63, 0.73)] exhibited a significant increase in methylation levels at CpG sites cg05350315_95, cg05350315_101, cg05350315_104, cg05350315_128, and cg05350315_142, with statistically significant differences ( Z=-3.56, -3.52, -3.60, -3.67, -3.62; P=0.036, 0.042, 0.031, 0.030, 0.030). Furthermore, Pearson correlation coefficient analysis revealed a positive correlation between the overall methylation level in this region and C-reactive protein (CRP) ( r=0.19, P=0.004) and erythrocyte sedimentation rate ( r=0.14, P=0.035). The overall methylation level of the LCK promoter region in the CRP (low) group [0.63 (0.58, 0.68)] was higher than that in the CRP (high) group [0.65(0.61, 0.70)], with statistically significant differences ( Z=2.60, P=0.009). Finally, by constru-cting a ROC curve, the discriminatory efficacy of peripheral blood LCK promoter region methylation levels for identifying RA patients, especially seronegative RA patients, from HC and OA groups was validated, with an AUC value of 0.78 (95% CI: 0.63, 0.93). Conclusion:This study provides insights into the methylation status and methylation haplotype patterns of the LCK promoter region in the peripheral blood of RA patients. The overall methylation level in this region is positively correlated with the level of inflammation and can be used to differentiate seronegative RA patients from the HC and OA patients.