ObjectiveTo evaluate the clinical efficacy of Huayu Tongluo Formula （化瘀通络方， HTF） in patients with mid-stage diabetic kidney disease of blood stasis syndrome and explore its potential mechanisms. MethodsA multi-center， randomized， double-blind， placebo-controlled clinical trial was conducted. Ninety patients of mid-stage diabetic kidney disease of blood stasis syndrome were divided into a control group of 46 cases and a treatment group of 44 cases. Both groups received conventional western medicine treatment， the treatment group additionally taking HTF， while the control group taking a placebo of the formula. The treatment was administered once daily for 24 weeks. The primary outcomes included 24-hour urine total protein （24 h-UTP）， serum albumin （Alb）， glycated hemoglobin （HbA1c）， and serum creatinine （Scr）.The secondary outcomes included changes in levels of endothelin-1 （ET-1）， nitric oxide （NO）， vascular endothelial growth factor （VEGF）， and traditional Chinese medicine （TCM） syndrome scores before and after treatment. Clinical efficacy was evaluated based on TCM syndrome scores and overall disease outcomes. Adverse reactions and endpoint events were recorded. ResultsIn the treatment group after treatment， 24 h-UTP， ET-1， and VEGF levels significantly decreased （P＜0.05）， Alb and NO levels significantly increased （P＜0.05）； while the TCM syndrome scores for edema， lumbar pain， numbness of limbs， dark purple lips， dark purple tongue or purpura， and thin， rough pulse all significantly decreased （P＜0.05）. In the control group， no significant changes were observed in any of the indicators after treatment （P＞0.05）.Compared with the control group， the treatment group showed significant reductions in 24 h-UTP， ET-1， and VEGF levels， and increases in Alb and NO levels （P＜0.05）. The TCM syndrome scores for edema， lumbar pain， dark purple tongue or purpura， and thin， rough pulse were all lower in the treatment group than in the control group （P＜0.05）. The total effective rate of TCM syndrome in the treatment group was 59.09% （26/44）， and the overall clinical effective rate was 45.45% （20/44）. In the control group， these rates were 15.22% （7/46） and 8.7% （4/46）， respectively， with the treatment group showing significantly better outcomes （P＜0.05）. A total of 7 adverse events occurred across both groups， with no significant difference （P＞0.05）. No endpoint events occurred during the study. ConclusionOn the basis of conventional treatment of Western medicine， HTF can further reduce urinary protein levels and improve clinical symptoms in patients with mid-stage diabetic kidney disease of blood stasis syndrome. The mechanism may be related to its effects on endothelial function.

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 assess the situation and influencing factors of occupational burnout among the staff at the Center for Disease Control and Prevention (CDC) in Sichuan Province. Methods A total of 1 038 CDC staff members in Sichuan Province were selected as the study subjects using the stratified random sampling method. Occupational burnout of the staff was assessed using the Maslach Burnout Inventory General Survey via an online questionnaire. Results The detection rate of occupational burnout was 42.3% (439/1 038). Binary logistic regression analysis result showed that, after controlling for confounding factors such as education level and alcohol consumption, CDC staffs aged at 20-<31, 31-<41, and 41-<51 years were at higher risk of occupational burnout compared with those ≥51 years (all P<0.05). CDC staffs with 5-<10 or ≥10 years of service had higher occupational burnout risk compared with those with <5 years (both P<0.05). CDC staffs with poor or fair health status, irregular diet, and poor sleep quality had higher risk of occupational burnout compared with those healthy, have regular diet, and good sleep quality (all P<0.05). The risk of occupational burnout increased with higher overtime frequency (all P<0.05). Conclusion Occupational burnout among CDC staffs in Sichuan Province is relatively high. Age, years of service, health status, diet, sleep quality, and overtime frequency are key influencing factors.

ObjectiveTo investigate the mechanism of Baihuan Xiaoyao Decoction （Xiaoyaosan added with Lilii Bulbus and Albiziae Cortex） in alleviating depression-like behaviors of juvenile rats by regulating the polarization of microglia. MethodSixty juvenile SD rats were randomized into normal control， model， fluoxetine， and low-， medium-， and high-dose （5.36， 10.71， 21.42 g·kg^-1， respectively） Baihuan Xiaoyao decoction groups. The rat model of juvenile depression was established by chronic unpredictable mild stress （CUMS）. The sucrose preference test （SPT） was carried out to examine the sucrose preference of rats. Forced swimming test （FST） was carried out to measure the immobility time of rats. The open field test （OFT） was conducted to measure the total distance， the central distance， the number of horizontal crossings， and the frequency of rearing. Morris water maze （MWM） was used to measure the escape latency and the number of crossing the platform. The immunofluorescence assay was employed to detect the expression of inducible nitric oxide synthase （iNOS， the polarization marker of M1 microglia） and CD206 （the polarization marker of M2 microglia）. Real-time polymerase chain reaction was employed to determine the mRNA levels of iNOS， CD206， pro-inflammatory cytokines ［tumor necrosis factor （TNF）-α， interleukin （IL）-1β， and IL-6］ and anti-inflammatory cytokines （IL-4 and IL-10） in the hippocampus. Western blotting was employed to determine the protein levels of iNOS and CD206 in the hippocampus. The levels of IL-4 and IL-6 in the hippocampus were detected by enzyme-linked immunosorbent assay. ResultCompared with the normal control group， the model rats showed a reduction in sucrose preference （P<0.05）， an increase in immobility time （P<0.05）， decreased motor and exploratory behaviors （P<0.05）， and weakened learning and spatial memory （P<0.05）. In addition， the model rats showed up-regulated mRNA and protein levels of iNOS and mRNA levels of IL-1β， IL-6， and TNF-α （P<0.05）. Compared with the model group， Baihuan Xiaoyao decoction increased the sucrose preference value （P<0.05）， shortened the immobility time （P<0.01）， increased the motor and exploratory behaviors （P<0.05）， and improved the learning and spatial memory （P<0.01）. Furthermore， the decoction down-regulated the positive expression and protein level of iNOS， lowered the levels of TNF-α， IL-1β， and IL-6 （P<0.01）， promoted the positive expression of CD206， and elevated the levels of IL-4 and IL-10 （P<0.01） in the hippocampus of the high dose group. Moreover， the high-dose Baihuan Xiaoyao decoction group had higher sucrose preference value （P<0.01）， shorter immobility time （P<0.01）， longer central distance （P<0.01）， stronger learning and spatial memory （P<0.01）， higher positive expression and protein level of iNOS （P<0.01）， lower levels of TNF-α， IL-1β， and IL-6 （P<0.05， P<0.01）， lower positive expression and mRNA level of iNOS （P<0.05）， and higher levels of IL-4 and IL-10 （P<0.05， P<0.01） than the fluoxetine group. ConclusionBaihuan Xiaoyao decoction can improve the depression-like behavior of juvenile rats by inhibiting the M1 polarization and promoting the M2 polarization of microglia in the hippocampus.

Objective Vascular dementia(VD)is a clinical syndrome caused by various cerebrovascular diseases,including ischemic,hemorrhagic,and acute and chronic hypoxic cerebrovascular diseases,leading to impaired brain function and affecting patients'cognitive ability,daily life,and work abilities.Vascular dementia is a preventable and reversible form of dementia,second only to Alzheimer's disease as the second common cause of dementia.At present,the relevant pathogenesis of vascular dementia is not clear,and there is no clear treatment method.However,its pathogenesis may be related to neuroinflammation,oxidative stress,neuronal damage and white matter lesions.Its main risk factors include genetic factors,hypercholesterolemia,diabetes,hypertension,etc.Neuroinflammatory response plays a major role in the process of secondary brain injury caused by cerebral ischemia,and inflammatory factors lead to an inflammatory cascade reaction that exacerbates damage to the nervous system.Inhibiting the inflammatory pathway and reducing the expression of inflammatory factors can improve the symptoms of vascular dementia patients and animal models,indicating that neuroinflammation may play an important role in the pathogenesis of vascular dementia.This article explores the effects of Buyang Huanwu Decoction on inflammatory factors from the perspective of summarizing relevant literature in recent years.It mainly reviews the pharmacological effects of Buyang Huanwu Decoction on treating vascular dementia,the relationship between inflammatory factor levels and vascular dementia,and the prevention and treatment of vascular dementia by regulating inflammatory factor levels.

Objective Lentivirus-mediated interference with synaptic nuclear protein γ(SNCG)in human oral squamous cell carcinoma was established to study the role of SNCG in the migration and invasion of oral squamous cell carcinoma.Methods Oral cancer CAL27 cells were infected with LV-shNC and LV-shSNCG constructed by lentivirus vector,respective,and then selected with puromycin to obtain cell lines stably interfering with SNCG,which were named NC group and experimental group,respectively.Detect the expression of SNCG through real-time quantitative polymerase chain reaction(qRT-PCR)and Western blot experiments;Transwell and scratch experiments were used to detect changes in migration and invasion ability.Results Compared with the NC group,the experimental group showed an 80％reduction in SNCG mRNA expression(P＜0.01).The relative expression level of SNCG protein was also decreased in the experimental group compared to the NC group(P＜0.01).In the NC group and the experimental group,the migration area percentages at 36 hours were 0.54±0.06 and 0.40±0.02,respectively;and at 48 hours were 0.83±0.01 and 0.47±0.05,respectively.The experimental group showed decrease in migration area compared to the NC group,and these differences were statistically significant(P＜0.05,P＜0.001).Compared to the NC group,the migration and invasion cell numbers in the experimental group(98.00±13.49 and 88.00±5.72)were significantly reduced to(48.00±2.16 and 49.00±2.94),and these differences were statistically significant(P＜0.01,P＜0.001).Conclusion Interference of SNCG expression can significantly reduce the migration and invasion ability of oral squamous cell carcinoma cells.