Objective: To identify the spatio-temporal clustering analysis of mumps in Wenzhou City, Zhejiang Province from 2010 to 2023, so as to provide the basis for improving mumps prevention and control strategies. Methods: Data of mumps cases in Wenzhou City from 2010 to 2023 were collected from the Monitoring and Reporting Management System of Chinese Disease Prevention and Control Information System. The spatio-temporal clustering characteristics of mumps incidence were identified using spatial autocorrelation analysis and spatio-temporal scan analysis. Results: A total of 20 455 mumps cases were reported in Wenzhou City from 2010 to 2023, with an average annual incidence of 17.54/105. There were 12 919 male and 7 536 female cases, with a male-to-female ratio of 1.71∶1. The children aged 5-<10 years had the highest incidence of mumps at 135.29/105. The incidence of mumps showed a downward trend from 46.82/105 in 2010 to 3.59/105 in 2023 (P<0.05). The incidence of mumps peaked from May to July and from November to January during 2010 and 2012, the winter peak became less evident after 2013, and no seasonal trends were observed after 2020. Spatial autocorrelation analysis showed there was a positive spatial correlation of mumps of other years, with the exception of 2018 (all Moran's I >0, all P<0.05). Lucheng District, Longwan District, Ouhai District, Cangnan County and Rui'an City were high-high clustering sites. Spatio-temporal scan analysis showed that the primary clustering area was centered in Nanbaixiang Street, Ouhai District, covering 50 towns (streets), with the clustering time from April 2010 to August 2013; the secondary clustering area was centered in Zaoxi Town, Cangnan County, covering 24 towns (streets), with the clustering time from January 2010 to June 2013. Conclusions The incidence of mumps in Wenzhou City from 2010 to 2023 showed a downward trend. The urban areas, Cangnan County and Rui'an City were the clustering areas.

ObjectiveTo explore the clinical safety of Feilike Mixture （FLK） in the real world. MethodsThe safety of all children who received FLK from 29 institutions in 12 provinces between January 21，2021 and December 25，2021 was evaluated through prospective centralized surveillance and a nested case control study. ResultsA total of 3 035 juveniles were included. There were 29 research centers involved，which are distributed across 12 provinces，including one traditional Chinese medicine （TCM） hospital and 28 general hospitals. The average age among the juveniles was （4.77±3.56） years old，and the average weight was （21.81±12.97） kg. Among them，119 cases （3.92%） of juveniles had a history of allergies. Acute bronchitis was the main diagnosis for juveniles，with 1 656 cases （54.46%）. FLK was first used in 2 016 cases （66.43%），and 142 juvenile patients had special dosages，accounting for 4.68%. Among them，92 adverse drug reactions （ADRs） occurred，including 73 cases of gastrointestinal system disorders，10 cases of metabolic and nutritional disorders，eight cases of skin and subcutaneous tissue diseases，two cases of vascular and lymphatic disorders，and one case of systemic diseases and various reactions at the administration site. The manifestations of ADRs were mainly diarrhea，stool discoloration，and vomiting，and no serious ADRs occurred. The results of multi-factor analysis indicated that special dosages （the use of FLK）［odds ratio （OR） of 2.642， 95% confidence interval （CI） of 1.105-6.323］，combined administration： spleen aminopeptide （OR of 4.978， 95%CI of 1.200-20.655），and reason for combined administration： anti-infection （OR of 1.814， 95%CI of 1.071-3.075） were the risk factors for ADRs caused by FLK. Conclusion92 ADRs occurred among 3 035 juveniles using FLK. The incidence of ADRs caused by FLK was 3.03%，and the severity was mainly mild or moderate. Generally，the prognosis was favorable after symptomatic treatment such as drug withdrawal or dosage reduction，suggesting that FLK has good clinical safety.

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 evaluate the incremental vaccine effectiveness (VE) of two dose of the mumps containing vaccine (MuCV) in chidren, so as to provide a basis for optimizing mumps immunization strategies. Methods: A 1∶2 frequency matched case-control study was conducted by using reported mumps cases in childcare centers or schools from Lu an, Hefei, Ma anshan and Huainan cities of Anhui Province from September 1, 2023 to June 30, 2024, as a case group(383 cases). And healthy children in the same classroom were selected as a control group(766 cases). The MuCV immunization histories of participants were collected to estimate the incremental VE of the second dose of MuCV against mumps. Group comparisons were performed using the Chi square test or t-test. For matched case-control pairs, the Cox regression model was employed to calculate the odds ratio (OR) with 95% confidence interval (CI) for two dose MuCV vaccination and to estimate the incremental vaccine effectiveness (VE). Results: There were no statistically significant differences between the case and control groups regarding gender, age, dosage of MuCV vaccination and the time interval since the last dose vaccination( χ 2/t=0.05, 0.20, 0.94, -0.02, P >0.05). The proportions of the case and control groups vaccinated with two doses of MuCV were 26.63% and 29.37%, respectively, and the overall incremental VE of the second dose of MuCV was 40.73% (95% CI=3.03%-63.77%, P <0.05). Subgroup analyses revealed that the incremental VE for children with a period of ≥1 year between the two doses of MuCV was 54.13% (95% CI=1.90%-78.56%, P <0.05), while for children with a period of <1 year, it was 30.63% (95% CI=-28.59%-62.58%, P >0.05). The incremental VE of the second dose of MuCV was 30.36% (95% CI=-25.95%-61.50%, P >0.05) in kindergarten children and 66.73% (95% CI=14.92%-86.99%, P <0.05) in elementary and secondary school students. The incremental VE was 28.78% (95% CI=-27.46%-60.21%, P >0.05) within five years of the last dose of MuCV vaccination and 66.07% (95% CI=-41.56%-91.87%, P >0.05) for vaccinations administered beyond five years. Conclusions The second dose of MuCV may offer additional protection for children; however, extending the interval between two dose of MuCV (<1 year) has shown limited incremental protective effects. Therefore, it is crucial to consider optimizing current immunization strategies for mumps.

ObjectiveTo explore and verify the key pathway of Zuogui Jiangtang Jieyu prescription in the treatment of diabetes with depression by means of gene set enrichment analysis （GSEA） and short time-series expression miner （STEM）. MethodSD rats were randomly divided into six groups， including a normal group， a model group， high， medium， and low dose groups of Zuogui Jiangtang Jieyu prescription， and a positive drug group. The model of diabetes with depression was established by high-fat feeding， streptozotocin （STZ） injection， and chronic mild unpredictable stress. The high， medium， and low dose groups of Zuogui Jiangtang Jieyu prescription were orally administered at 20.52， 10.26， and 5.13 g·kg^-1 respectively. The positive drug group was orally administered 0.18 g·kg^-1 metformin and 1.8 g·kg^-1 fluoxetine. The rats in the normal group and model group were administered with an equal volume of distilled water. After 28 days， the animals were tested for depressive-like behaviors and cognitive function using the forced swimming test and Morris water maze. Fasting blood glucose was measured using blood glucose test strips. Cholesterol and triglyceride levels were measured using enzyme-linked immunosorbent assay （ELISA）. Three hippocampus samples were randomly selected from the normal group， the model group， the high dose group of Zuogui Jiangtang Jieyu prescription for high-throughput transcriptome sequencing. Differential gene analysis， GSEA analysis， and STEM analysis were used to screen the key pathways and target genes of Zuogui Jiangtang Jieyu prescription in the treatment of diabetes with depression. Key target genes were validated using real-time fluorescence quantitative PCR （Real-time PCR）. The expression of the signal protein mediated by the target genes was detected by Western blot. ResultCompared with the results in the normal group， the fasting blood glucose， cholesterol， and triglyceride levels in the model group were significantly increased （P<0.01）. Moreover， the immobility time in the forced swimming test was significantly increased， while the time to climb the platform was significantly prolonged， and the search distance in the target quadrant was significantly reduced in the Morris water maze test （P<0.05，P<0.01）. Compared with the model group， the high dose of Zuogui Jiangtang Jieyu prescription significantly reduced the levels of blood glucose， cholesterol， and triglycerides in rats with diabetes with depression （P<0.05，P<0.01）， reduced the immobility time in the forced swimming test， shortened the stage time in the Morris water maze test， and increased the search distance ratio in the target area （P<0.05，P<0.01）. Transcriptome sequencing differential analysis showed that the normal group had 1 366 differentially expressed genes compared to the model group， while the model group had 1 149 differentially expressed genes compared to the high dose group of Zuogui Jiangtang Jieyu prescription， with 581 intersecting genes. The GSEA results showed that there were 9 sets of differentially expressed genes between the normal group and the model group， and 43 sets of differentially expressed genes between the model group and the high dose group of Zuogui Jiangtang Jieyu prescription， with 7 intersecting gene sets. STEM analysis showed that according to the analysis order of the normal group， model group， and high dose group of Zuogui Jiangtang Jieyu prescription， two significantly different trend clustering groups were obtained. One key gene set for axonal guidance， as well as key target signal elements Sema3c， Sema7a， Robo3， Epha8， and Epha7， were identified through synthesizing the three analysis results. Real-time PCR validated that compared with the results in the normal group， the mRNA expression of Robo3， Sema7a， and Epha7 in the hippocampus of the model rats was significantly reduced （P<0.05， P<0.01）. Compared with the model group， the high dose of Zuogui Jiangtang Jieyu prescription significantly increased the mRNA expression of Robo3， Sema7a， and Epha7 （P<0.05， P<0.01）. Western blot results showed that compared with the results in the normal group， the Sema7a， ITGB1， and FAK protein expression in the hippocampus of the model group was significantly reduced （P<0.01）. Compared with the model group， the high dose of Zuogui Jiangtang Jieyu prescription significantly increased the protein expression of Sema7a， ITGB1， and FAK in the hippocampus （P<0.05，P<0.01）. ConclusionZuogui Jiangtang Jieyu prescription may treat diabetes with depression by regulating axonal guidance based on the Sema7a/ITGB1 signaling pathway.

Mitral valve replacement is one of the most common heart valve surgeries in China. In recent years, with the increase in degenerative valve diseases, older patients, and the progress of anti-calcification technology of biological valves, the proportion of mitral valve biological valve replacement has been increasing year by year. After the damage of traditional mitral valve biological valves, re-operation of valve replacement with thoracotomy is required. However, the adhesion between the heart and sternum, as well as the damage caused by cardiopulmonary bypass and cardiac arrest, can cause significant trauma to elderly patients and those with multiple organ dysfunction, leading to increased mortality and complication rates. In recent years, interventional valve surgery, especially transcatheter valve-in-valve surgery, has developed rapidly. This procedure can correct the damaged mitral valve function without stopping the heart, but there are still many differences between its technical process and conventional aortic valve replacement surgery. Therefore, organizing and writing multicenter expert recommendations on the technical process of transcatheter valve-in-valve surgery for damaged mitral valve biological valves is of great significance for the training and promotion of this technology.