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 explore the effect and potential mechanism of the compatibility of Astragali Radix-Puerariae Lobatae Radix on ferroptosis of liver cells in type 2 diabetes mellitus （T2DM） insulin resistance （IR） rats. METHODS Sixty male SD rats were randomly divided into control group （12 rats） and modeling group （48 rats）. The modeling group was fed with a high- fat diet for 4 consecutive weeks and then given a one-time tail vein injection of 1% streptozotocin to establish T2DM IR model. The model rats were randomly divided into model group， the compatibility of Astragali Radix-Puerariae Lobatae Radix group [QG group， 4.05 g/（kg·d）， intragastric administration]， ferroptosis inhibitor ferrostatin-1 group [Fer-1 group， 5 mg/kg by intraperitoneal injection， once every other day]， the compatibility of Astragali Radix-Puerariae Lobatae Radix+ferroptosis inducer erastin group [QG+erastin group， 4.05 g/（kg·d） by intragastric administration+erastin 10 mg/（kg·d）， intraperitoneal injection]. After 4 weeks of intervention， serum fasting blood glucose （FBG） and fasting insulin （FINS） were measured in each group of rats， and homeostasis model assessment of insulin resistance （HOMA-IR） and the natural logarithm of insulin action index（IAI） were calculated； the serum levels of total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate transaminase （AST） and alanine transaminase （ALT）， Fe2+ and Fe content， glutathione （GSH）， malondialdehyde （MDA） and superoxide dismutase （SOD） levels， NADP+/NADPH ratio and reactive oxygen species （ROS） were determined. The pathological morphology of its liver tissue was observed； the protein expressions of glutathione peroxidase 4 （GPX4）， ferritin heavy chain 1 （FTH1）， long-chain acyl-CoA synthetase 3 （ACSL3）， ACSL4， ferritin mitochondrial （FTMT）， and cystine/glutamate anti-porter （xCT） in the liver tissue of rats were detected. RESULTS Compared with control group， the liver cells in the model group of rats showed disordered arrangement， swelling， deepened nuclear staining， and more infiltration of inflammatory cells， as well as a large number of hepatocyte vacuoles and steatosis； FBG （after medication）， the levels of TC， TG， LDL-C， AST， ALT， FINS， MDA and ROS， HOMA-IR， Fe2+ and Fe content， NADP+/NADPH ratio and protein expression of ACSL4 were significantly increased or up-regulated， while the levels of HDL-C， GSH and SOD， IAI， protein expressions of GPX4， FTH1， ACSL3， FTMT and xCT were significantly reduced or down-regulated （P＜0.01）. Compared with the model group， both QG group and Fer-1 group showed varying degrees of improvement in pathological damage of liver tissue and the levels of the above indicators， the differences in the changes of most indicators were statistically significant （P＜0.01 or P＜0.05）. Compared with QG group， the improvement of the above indexes of QG+erastin group had been reversed significantly （P＜0.01）. CONCLUSIONS The compatibility decoction of Astragali Radix-Puerariae Lobatae Radix can reduce the level of FBG in T2DM IR rats， and alleviate IR degree， ion overload and pathological damage of liver tissue. The above effects are related to the inhibition of ferroptosis.

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.

Piper nigrum L. is an evergreen climbing vine, which belongs to the genus Piperia in the Piperaceae family. Piper nigrum L., which known as the “king of spices”, is used as both food and medicine. The main active substances in Piper nigrum L. are alkaloids mainly composed of amides, and essential oil, as well as phenolic compounds. In this paper, the chemical compositions, especially amide alkaloids, and their biological activities of Piper nigrum L. were summarized. These studies showed that Piper nigrum L., as a medicinal and food plant, had a wide range of biological activities and was deserved further research and in-depth utilization.

Vascular calcification is a highly regulated process of ectopic calcification in cardiovascular system while no effective intervention can be clinically performed up to date.As vascular calcification undergoes a common regulatory mechanism within bone formation,bone morphogenetic protein 7(BMP-7)main-tains contractile phenotype of vascular smooth muscle cells and further inhibits vascular calcification via promoting the process of osteoblast differentiation,reducing ectopic calcification pressure by increasing bone formation and reducing bone resorption.This work systematically reviews the role of BMP-7 in vascular calcifi-cation and the possible mechanism,and their current clinical application as well.The current proceedings may help develope early diagnostic strategy and therapeutic treatment with BMP-7 as a new molecular marker and potential drug target.The expec-tation could achieve early prevention and intervention of vascular calcification and improve poor prognosis on patients.

Objective To compare the effectiveness of TiRobot assisted F screw technique and inverted triangle parallel nail internal fixation in the treatment of unstable femoral neck fractures.Methods A retrospective analysis was conducted on 72 patients with unstable femoral neck fractures who were treated with percutaneous cannulated screw fixation assisted with TiRobot Orthopaedic robot from December 2019 to April 2021.Among them,37 patients were treated with F screw internal fixa-tion,including 16 males and 21 females,aged47 to 64years old with an average of(53.87±5.28)years old;According to Pauwels classification,there were 1 case of type Ⅰ,19 cases of type Ⅱ,17 cases of type Ⅲ;8 cases of combined medical diseases;17 cases of falling,8 cases of traffic accident and 12 cases of falling from height;The time from injury to operation was 29 to 49 hours with average of(35.00±7.34)hours.Another 35 cases used internal fixation with an inverted triangle parallel nail,including 13 males and 22 females with an average age of 46 to 63 years old(52.36±5.05)years old;According to the Pauwels injury classifi-cation:there were 2 cases of type Ⅰ,21 cases of type Ⅱ,12 cases of type Ⅲ;6 cases of medical diseases,15 cases of falling in-jury,9 cases of traffic accident,11 cases of falling injury;The time from injury to operation was 30 to 45 hours with an average of(33.00±6.83)h.The intraoperative blood loss,operation time,intraoperative fluoroscopy times,follow-up time,fracture healing time,postoperative complications were observed and compared between the two groups.The hip joint function was e-valuated by Harris score at 6 months and 12 months after operation.Results There was no significant difference in operation time,intraoperative blood loss,intraoperative fluoroscopy times and other intraoperative data between two groups(P＞0.05).Both groups were followed up regularly,and the follow-up time was 12 to 16 months.The fracture healing time and Harris score of the F screw internal fixation group were better than those of the inverted triangle parallel nail internal fixation group(P＜0.05).There was 1 case of femoral neck shortening in the F screw internal fixation group,1 case of nonunion,1 case of nail withdrawal,and 1 case of lower extremity deep vein thrombosis in the inverted triangle internal fixation group.The incidence of complications in the F screw internal fixation group was lower than that in the inverted triangle parallel nail internal fixation group(P＜0.05).Conclusion Percutaneous cannulated F screw technique using Tirobot navigation positioning system is a safe and effective treatment for patients with unstable femoral neck fractures.It can significantly shorten the fracture healing time,reduce the incidence of postoperative complications,significantly improve hip joint function,and improve the quality of life.

Objective:To explore the application value and damage control of minimally inva-sive techniques in the treatment of acute incarcerated inguinal hernias in the elderly.Methods:In this study,62 elderly patients with acute incarcerated inguinal hernias admitted to the department of emergency surgery at the First Affiliated Hospital of Anhui Medical University from June 2018 to June 2023 were selected as the research subjects.After obtaining informed consent from the pa-tient's family for both treatment modalities,they were randomly divided into open surgery group and laparoscopic surgery group.Differences in clinical efficacy,perioperative indicators,post-operative complications,and prognostic follow-up of the two groups of patients were observed.Seven cases of elderly patients aged above 80 had many underlying diseases and poor tolerance during surgery.After treatment of lesions in the hernia contents,only damage control surgery for hernia sac high ligation was performed.Results:In comparison to patients treated with laparo-scopic surgery,there were statistically significant differences(P＜0.05)in the open surgery group in clinical efficacy(efficacy,ineffectiveness,and overall effectiveness),perioperative indicators(length of stay,recovery time of digestive tract function,and VAS pain score),post-operative complica-tions,and prognostic follow-up(local mass,chronic pain,and ratio of second-stage hernia sur-gery).Seven patients treated according to injury control strategies all recovered and discharged from hospital after surgery.Conclusion:Emergency laparoscopic surgery for detecting incarcer-ated inguinal hernias in the elderly is safe and feasible.At the same time,it is essential to correctly assess the patient's vital signs during surgery.If necessary,surgery should be simplified to provide opportunities for follow-up treatment.