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:This study discusses the job preferences of Center for Disease Control and Prevention(CDC)staffs at the prefectural-level,and provides a basis for the development of an effective incentive mechanism.Method:This study used a combination of stratified sampling and purposive sampling to research online 455 staffs from six prefectural-level CDCs in Shandong Province,analyzed the data using a mixed logit model and latent class model,and calculated willingness to pay and relative importance.Result:In the mixed logit model,income,benefit level,establishment,workload,recognition and respect from the public,personal career development opportunities,and training opportunities all had significant influences(P<0.05)on the job selection preferences of the CDC staffs,with hygiene factors such as establishment(β =2.636)and income(β =0.083)having a greater degree of influence than motivation factors.The latent class model shows that relatively young CDC staffs with lower monthly incomes value income more;older CDC staffs with higher monthly incomes value establishment more.Conclusion:Prefectural-level CDC staffs prefer jobs with establishment,higher incomes,very good benefit levels,recognition and respected from the public,lower workloads,many opportunities for personal career advancement and abundant training opportunities.It is recommended that the total number of establishments be rationally controlled and dynamically adjusted to balance the differences between working conditions within and outside the establishment and that the financial input to CDC be increased and the pay performance system be improved;that attention be paid to both hygiene factors and motivation factors,and that a variety of measures work together to incentivize CDC staffs development;and that differentiated incentives be adopted for different categories of CDC staffs.

G protein-coupled receptor (GPR) 40, as one of GPRs family, plays a potential role in regulating glucose and lipid metabolism. To study the effect of GPR40 novel agonist SZZ15-11 on hyperglycemia and hyperlipidemia and its potential mechanism, spontaneous type 2 diabetic KKA^y mice, human hepatocellular carcinoma HepG2 cells and murine mature adipocyte 3T3-L1 cells were used. KKA^y mice were divided into four groups, vehicle group, TAK group, SZZ (50 mg·kg^-1) group and SZZ (100 mg·kg^-1) group, with oral gavage of 0.5% sodium carboxymethylcellulose (CMC), 50 mg·kg^-1 TAK875, 50 and 100 mg·kg^-1 SZZ15-11 respectively for 45 days. Fasting blood glucose, blood triglyceride (TG) and total cholesterol (TC), non-fasting blood glucose were tested. Oral glucose tolerance test and insulin tolerance test were executed. Blood insulin and glucagon were measured via enzyme-linked immunosorbent assay (ELISA). After mice′s execution, liver tissue was harvested to test TG and TC content. Then pathological morphology of liver was observed through hematoxylin-eosin (HE) staining, and the lipid metabolism relative signal pathway was analyzed by Western blot and RT-PCR. The experiments were approved by the Institutional Animal Care and Use Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College. At the same time, Akt phosphorylation level in HepG2 cells and adiponectin in 3T3-L1 cells treated with TNFα were measured with Western blot. The results show that SZZ15-11 not only decreased blood glucose and lipid, improved insulin sensitivity, but also increased fasting blood glucagon and promoted insulin secretion after glucose loading in KKA^y mice. Additionally, SZZ15-11 alleviated hepatic steatosis and liver dysfunction in KKA^y mice. In liver tissue, SZZ15-11 increased AMPKα phosphorylation level and cholesterol metabolism relative gene Abcg8 transcription. In HepG2 cells, SZZ15-11 increased Akt phosphorylation level. In adipocyte 3T3-L1, SZZ15-11 recovered the decreased adiponectin expression by TNFα. This study proved that GPR40 agonist SZZ15-11 could be a candidate compound for regulating glucolipid metabolic disorder.

Objective To establish a rapid screening method for 34 emerging contaminants in surface water by ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS).Methods The pretreatment conditions of solid phase extraction(SPE)were op-timized by orthogonal experimental design and the surface water samples were concentrated and ex-tracted by Oasis? HLB and Oasis? MCX SPE columns in series.The extracts were separated by Kine-tex? EVO C18 column,with gradient elution of 0.1%formic acid aqueous solution and 0.1%formic acid methanol solution.Q-TOF-MS'fullscan'and'targeted MS/MS'modes were used to detect 34 emerging contaminants and to establish a database with 34 emerging contaminants precursor ion,prod-uct ion and retention times.Results The 34 emerging contaminants exhibited good linearity in the con-centration range respectively and the correlation coefficients(r)were higher than 0.97.The limit of de-tection was 0.2-10 ng/L and the recoveries were 81.2%-119.2%.The intra-day precision was 0.78%-18.70%.The method was applied to analyze multiple surface water samples and 6 emerging contaminants were detected,with a concentration range of 1.93-157.71 ng/L.Conclusion The method is simple and rapid for screening various emerging contaminants at the trace level in surface water.

Objective To evaluate the safety and efficacy of splenectomy with distal pancreatectomy during cytoreductive surgery in epithelial ovarian cancer(EOC).Methods A total of 17 patients from Zhongshan Hospital,Fudan University and the First Affiliated Hospital of University of Science and Technology of China(Anhui Provincial Hospital)received splenectomy with distal pancreatectomy during cytoreductive surgery in EOC were recruited.Their clinicopathological characteristics,postoperative complications and survival situation were retrospective analyzed.Results Of the 17 patients,there were 13 primary cases and 4 recurrent cases.Eleven cases(64.7%)had preoperative imaging finding with metastatic lesions in the splenic hilum,among whom 6 cases had distal pancreas metastasis during the operation.The drainage was placed in the splenic fossa for the measurement of amylase levels in drain fluid and was removed after 8(3-12)days.There were 4 patients had postoperative pancreatic fistula(POPF)of grade A,3 patients had POPF of grade B and no POPF of grade C occurred.The 2 patients with POPF of grade B improved after percutaneous drainage,and the rest recovered with somatostatin,antibiotic drugs and medicines without perioperative mortality.The interval between surgery to chemotherapy was 17.5(13-37)days.The median follow-up time was 14(4-64)months and the median progression-free survival was 10(5-32)months.Conclusion Splenectomy with distal pancreatectomy as part of cytoreduction surgery in EOC is needed for optimal resection,and the complication of pancreatic fistula could be managed conservatively.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.