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 determine the content of the released nickel ion through the 7 extraction media to extract the Ni-Ti wires and to plot the curve of the released nickel ion so as to identify a leaching medium that can be substituted for blood for in vitro Ni release evaluation. Methods:The release of Ni through microwave digestion/inductively coupled plasma mass spectrometry (ICP-MS) in the goat serum was determined. Because of the high content of Ni release, it could be determined by diluting the extraction medium, and other extraction media could be determined directly. Ni release standard curves were plotted by the release amount and different time point variables. Though the different extraction media Ni release curves confirm the specificity of extraction media instead of blood.Results:By analyzing the Ni release curves of seven leaching media, it was found that none of these seven extraction media was suitable for the evaluation of Ni release in in vitro leaching media. Considering the safety of the leaching medium and the simplicity of preparation, hydrochloric acid solution was chosen as the leaching medium, but the concentration needed to be diluted accordingly. Finally, a hydrochloric acid solution was created as an alternative to blood for the in vitro study of Ni release from Ni-Ti alloy cardiovascular products, with a volume fraction of 0.005%. Conclusions:The in vitro leaching medium that can replace blood was found to be hydrochloric acid for the time being, but its concentration was too high, resulting in too much Ni release as well, which deviated from the actual situation. Therefore, the hydrochloric acid solution was diluted step by step, and the Ni release curve was examined until it was close to the clinical release level, and the actual concentration was determined, thus laying a solid foundation for the subsequent evaluation of the safety and risk.

ObjectiveTo investigate the effects of thyme herbal tea （BLX） on the proliferation of human coronavirus OC43 （HCoV-OC43） in vitro and in vivo. MethodThe chemical composition of BLX was analyzed by UPLC-MS. The cytotoxicity of BLX in HRT-18 cells and the effect of BLX treatment on the proliferation of HCoV-OC43 in cells were analyzed. Copies of viral gene were detected by real-time PCR. The effect of BLX treatment on the life cycle of HCoV-OC43 was detected by time-of-addition assay. The maximum tolerated dose of BLX and the influences of BLX on the body weight and survival time of suckling mice infected with HCoV-OC43 were determined. The expression of viral protein in the brain and lung tissue was analyzed by immunohistochemistry. ResultThere were 11 chemical components identified in BLX by UPLC-MS. BLX showed the 50% cytotoxic concentration （CC₅₀） of （13 859.56±319） mg·L^-1， the median inhibitory concentration （IC₅₀） of （1 439.09±200） mg·L^-1， and the selection index of 8.26-11.44 for HCoV-OC43 in HRT-18 cells. Compared with the cells infected with HCoV-OC43， BLX at the concentrations of 1 500， 1 000， 500 mg·L^-1 inhibited the proliferation of this virus （P<0.05， P<0.01）. BLX exhibited antiviral effect in the early stage of virus infection， and the inhibition role in the attachment stage was more significant than that in the entry stage （P<0.05）. In the suckling mice infected with HCoV-OC43， BLX at 1200 and 600 mg·kg^-1·d^-1 alleviated the symptoms， prolonged the survival period， reduced the death rate， and down-regulated the mRNA level of nucleocapsid protein in the mice. Moreover， BLX at 1 200 mg·kg^-1·d^-1 down-regulated the expression of nucleocapsid protein in the brain （P<0.01） and the lung （P<0.01）. ConclusionBLX contained multiple antiviral ingredients. It inhibited the proliferation of HCoV-OC43 both in vitro and in vivo by interference with viral attachment. This study provides theoretical reference for the treatment of acute respiratory tract infection with HCoV-OC43 and for further development and application of BLX.

AIM: To investigate the efficacy and safety of ZKY001 eye drops with different concentrations in the treatment of corneal epithelial defects(CED)after primary pterygium excision.METHODS: This was a multicenter, randomized, double-blinded, placebo-controlled phase II clinical trial. From March 15, 2022 to November 14, 2022, patients with primary pterygium who had undergone surgery were recruited from 12 tertiary hospitals across China. Using block randomization, 178 patients(178 eyes)were randomly assigned to 3 groups in a 1:1:1 ratio: 0.002% ZKY001 group(n=59), 0.004% ZKY001 group(n=59), and placebo group(n=60, receiving ZKY001 sham eye drops). Subjects in each group received 1 drop of the study drug 4 times per day for 4 d. The percentage of CED area recovery from baseline, the first complete healing time of CED area, the number of first complete healing cases of CED, and changes in visual analogue scale(VAS)scores for eye discomfort including eye pain, foreign body sensation, tearing and photophobia were observed.RESULTS: In terms of improvement in CED, there were no statistically significant differences among the three groups including the first healing time of CED, the percentage improvement in CED area compared to baseline, and the percentage of first healing cases at different follow-up visits(all P>0.05). Numerically, the first healing time of CED was shorter in the test groups compared to the placebo group(67.87±21.688 h for the 0.002% ZKY001 group, 61.48±22.091 h for the 0.004% ZKY001 group, and 68.85±20.851 h for the placebo group). On D1 morning, the percentage improvement in CED area compared to baseline was maximally different from the placebo group, and the numerical difference advantage was maintained at subsequent follow-up visits. The number of first healing cases in the CED area at different follow-up visits was higher in the test groups than the placebo group. In terms of improvement in ocular discomfort, the total VAS scores were lower in the test groups compared to the placebo group, mainly due to reductions in foreign body sensation and pain scores. At D3, the 0.004% ZKY001 group showed statistically significant improvement in foreign body sensation(P<0.017). In terms of safety, the overall incidence of adverse events was low(9.0%)and similar among groups.CONCLUSION: The use of ZKY001 eyedrops after primary pterygium surgery can safely improve the CED repair, and alleviate postoperative symptoms caused by CED.