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.

Objective To explore the effect of scutellarin on lipopolysaccharide(LPS)induced neuroinflammation in BV-2 microglia cells.Methods BV-2 microglia were cultured and randomly divided into 6 groups:control group(Ctrl),cyclic GMP-AMP synthetase(cGAS)inhibitor RU320521 group(RU.521 group),LPS group,LPS+RU.521 group,LPS+scutellarin pretreatment group(LPS+S)and LPS+S+RU.521 group.The expressions of cGAS,stimulator of interferon gene(STING),nuclear factor kappa B(NF-κB),phosphorylated NF-κB(p-NF-κB),neuroinflammatory factors PYD domains-containing protein 3(NLRP3)and tumor necrosis factor α(TNF-α)in BV-2 microglia were detected by Western blotting and immunofluorescent double staining(n= 3).Results Western blotting and immunofluorescent double staining showed that compared with the control group,the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in BV-2 microglia increased significantly after LPS induction(P<0.05),while the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in LPS+S group were significantly lower than those in LPS group(P<0.05).Treatment with cGAS pathway inhibitor RU.521 showed similar effects as the pre-treatment group with scutellarin.In addition,the change of NF-κB in each group was not statistically significant(P>0.05).Conclusion Scutellarin inhibits the neuroinflammation mediated by BV-2 microglia cells,which may be related to cGAS-STING signaling pathway.

OBJECTIVE To provide ideas for clinical diagnosis， treatment and pharmaceutical care of rhino-orbito-cerebral mucormycosis （ROCM）. METHODS The diagnosis and treatment of 1 case of ROCM in which clinical pharmacists participated were analyzed. Combined with treatment guidelines， the actual situation of drug accessibility and economy， clinical pharmacists recommend amphotericin B colloidal dispersion in combination with posaconazole to treat fungal infections. The clinical efficacy， liver and kidney function and electrolytes were monitored. To increase the local concentration of amphotericin B deoxycholate （AmB-D）， clinical pharmacists assisted physicians in determining the dosage and formulation of AmB-D for intrathecal injection， intranasal and eye drops based on the results of blood and cerebrospinal fluid examinations. In response to the situation that the plasma trough concentration of posaconazole had not reached the target， clinical pharmacists recommended that Posaconazole oral suspension was replaced with Posaconazole enteric-coated tablets， and provided the patient with therapeutic drug monitoring （TDM）， individualized medication guidance， and long-term follow-up after discharge. RESULTS The clinician adopted the advice of the clinical pharmacists. After treatment， the patient was discharged from the hospital with medicine after her condition improved. CONCLUSIONS Clinical pharmacists develop individualized treatment protocols for ROCM patients by adjusting dose and dosage forms， providing medication monitoring and TDM to ensure the safety of drug use for patients.

This paper explains the mechanism of the mutual switching between physiological sleep and wakefulness from the aspects of the sleep circadian system and the sleep homeostasis system. In the circadian rhythm system, with the suprachiasmatic nucleus as the core, the anatomical connections between the suprachiasmatic nucleusand various systems that affect sleep are summarized, starting from the suprachiasmatic nucleus, passing through the four pathways of the melatonin system, namely, subventricular area of the hypothalamus, the ventrolateral nucleus of the preoptic area, orexin neurons, and melatonin, then the related mechanisms of their regulation of sleep and wakefulness are expounded. In the sleep homeostasis system, with adenosine and prostaglandin D2 as targets, the role of hypnogen in sleep arousal mechanisms in regulation is also expounded.