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.

BACKGROUND:At present,the biological functions and molecular changes of bone marrow mesenchymal stem cells in the tumor microenvironment of acute myeloid leukemia are still unclear. OBJECTIVE:To explore the changes in the biological function of bone marrow mesenchymal stem cells in acute myeloid leukemia and the role of acute myeloid leukemia conditioned medium by bioinformatics and experiment. METHODS:Differential genes were screened from GEO data sets,and enrichment analysis was performed.The protein-protein interaction network was constructed and the Hub gene was obtained.Bone marrow mesenchymal stem cells from patients with acute myeloid leukemia and healthy donors were cultured.Bone marrow mesenchymal stem cells from healthy donors were treated with acute myeloid leukemia conditioned culture solution.Each group was subjected to the adipogenic differentiation,osteogenic differentiation,staining of β-galactosidase,detection of the cell cycle,and validation of Hub genes. RESULTS AND CONCLUSION:(1)Gene expression data of bone marrow mesenchymal stem cells from acute myeloid leukemia patients and healthy donors were obtained from GSE84881,and 184 up-regulated genes and 140 down-regulated genes were screened.(2)The biological functions of enrichment mainly include cell cycle,adipocyte differentiation,cell metabolism,and MYC pathway.According to the Degree algorithm,10 up-regulated Hub genes and 10 down-regulated Hub genes were selected.(3)The cell in vitro experiment found that:compared with the control group,the surface antigen of acute myeloid leukemia mesenchymal stem cells did not change,but it showed enhanced lipid differentiation ability,weakened osteogenic differentiation ability,increased β-galactosidase positive cell number,altered cell morphology,arrested cell cycle,increased LGALS3 expression,and decreased MYC expression.Mesenchymal stem cells from healthy donors showed similar changes after being cultured in acute myeloid leukemia conditioned medium.(4)The results show that biological function of mesenchymal stem cells is altered in the acute myeloid leukemia microenvironment,which provides new insights into the interaction between mesenchymal stem cells and tumor cells.

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of national bloodstream infection Bacterial Resistant Investigation Collaborative System（BRICS）were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:During the study period，9 035 strains of Gram-negative bacteria were collected from 51 hospitals，of which 7 895（87.4%）were Enterobacteriaceae and 1 140（12.6%）were non-fermenting bacteria. The top 5 bacterial species were Escherichia coli（ n=4 510，49.9%）， Klebsiella pneumoniae（ n=2 340，25.9%）， Pseudomonas aeruginosa（ n=534，5.9%）， Acinetobacter baumannii complex（ n=405，4.5%）and Enterobacter cloacae（ n=327，3.6%）. The ESBLs-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus spp. were 47.1%（2 095/4 452），21.0%（427/2 033）and 41.1%（58/141），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（58/4 510）and 13.1%（307/2 340）；62.1%（36/58）and 9.8%（30/307）of CREC and CRKP were resistant to ceftazidime/avibactam combination，respectively. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 59.5%（241/405），while less than 5% of Acinetobacter baumannii complex was resistant to tigecycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 18.4%（98/534）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of main Gram-negative bacteria resistance among different regions，with statistically significant differences in the prevalence of CRKP and CRPA（ χ2=20.489 and 20.252， P<0.001）. The prevalence of CREC，CRKP，CRPA，CRAB，ESBLs-producing Escherichia coli and Klebsiella pneumoniae were higher in provinicial hospitals than those in municipal hospitals（ χ2=11.953，81.183，10.404，5.915，12.415 and 6.459， P<0.01 or <0.05），while the prevalence of CRPA was higher in economically developed regions（per capita GDP ≥ 92 059 Yuan）than that in economically less-developed regions（per capita GDP <92 059 Yuan）（ χ2=6.240， P=0.012）. Conclusions:The proportion of Gram-negative bacteria in bloodstream infections shows an increasing trend，and Escherichia coli is ranked in the top，while the trend of CRKP decreases continuously with time. Decreasing trends are noted in ESBLs-producing Escherichia coli and Klebsiella pneumoniae. Low prevalence of carbapenem resistance in Escherichia coli and high prevalence in CRAB complex have been observed. The composition ratio and antibacterial spectrum of bloodstream infections in different regions of China are slightly different，and the proportion of main drug resistant bacteria in provincial hospitals is higher than those in municipal hospitals.

Objective:To develop a robotic digestive endoscope system (RDES) and to evaluate its feasibility, safety and control performance by experiments.Methods:The RDES was designed based on the master-slave control system, which consisted of 3 parts: the integrated endoscope, including a knob and button robotic control system integrated with a gastroscope; the robotic mechanical arm system, including the base and arm, as well as the endoscopic advance-retreat control device (force-feedback function was designed) and the endoscopic axial rotation control device; the control console, including a master manipulator and an image monitor. The operator sit far away from the endoscope and controlled the master manipulator to bend the end of the endoscope and to control advance, retract and rotation of the endoscope. The air supply, water supply, suction, figure fixing and motion scaling switching was realized by pressing buttons on the master manipulator. In the endoscopy experiments performed on live pigs, 5 physicians each were in the beginner and advanced groups. Each operator operated RDES and traditional endoscope (2 weeks interval) to perform porcine gastroscopy 6 times, comparing the examination time. In the experiment of endoscopic circle drawing on the inner wall of the simulated stomach model, each operator in the two groups operated RDES 1∶1 motion scaling, 5∶1 motion scaling and ordinary endoscope to complete endoscopic circle drawing 6 times, comparing the completion time, accuracy (i.e. trajectory deviation) and workload.Results:RDES was operated normally with good force feedback function. All porcine in vivo gastroscopies were successful, without mucosal injury, bleeding or perforation. In beginner and advanced groups, the examination time of both RDES and ordinary endoscopy tended to decrease as the number of operations increased, but the decrease in time was greater for operating RDES than for operating ordinary endoscope (beginner group P=0.033; advanced group P=0.023). In the beginner group, the operators operating RDES with 1∶1 motion scaling or 5∶1 motion scaling to complete endoscopic circle drawing had shorter completion time [1.68 (1.40, 2.17) min, 1.73 (1.47, 2.37) min VS 4.13 (2.27, 5.16) min, H=32.506, P<0.001], better trajectory deviation (0.50±0.11 mm, 0.46±0.11 mm VS 0.82±0.26 mm, F=38.999, P<0.001], and less workload [42.00 (30.00, 50.33) points, 43.33 (35.33, 54.00) points VS 52.67 (48.67, 63.33) points, H=20.056, P<0.001] than operating ordinary endoscope. In the advanced group, the operators operating RDES with 1∶1 or 5∶1 motion scaling to complete endoscopic circle drawing had longer completion time than operating ordinary endoscope [1.72 (1.37, 2.53) min, 1.57 (1.25, 2.58) min VS 1.15 (0.86, 1.58) min, H=13.233, P=0.001], but trajectory deviation [0.47 (0.13, 0.57) mm, 0.44 (0.39, 0.58) mm VS 0.52 (0.42, 0.59) mm, H=3.202, P=0.202] and workload (44.62±21.77 points, 41.24±12.57 points VS 44.71±17.92 points, F=0.369, P=0.693) were not different from those of the ordinary endoscope. Conclusion:The RDES enables remote control, greatly reducing the endoscopists' workload. Additionally, it gives full play to the cooperative motion function of the large and small endoscopic knobs, making the control more flexible. Finally, it increases motion scaling switching function to make the control of endoscope more flexible and more accurate. It is also easy for beginners to learn and master, and can shorten the training period. So it can provide the possibility of remote endoscopic control and fully automated robotic endoscope.

Baihe Dihuangtang is a famous classical formula that has been respected by physicians in the past and is still used today. It was first recorded in ZHANG Zhongjing's Synopsis of the Golden Chamber， and is composed of Lilii Bulbus and Rehmanniae Radix juice. This paper systematically reviewed the research progress of historical evolution， pharmacological activities and clinical applications of Baihe Dihuangtang in recent years， and found that there was no major changes in the composition， decoction method and indications of this formula since the Han dynasty. According to the herbal textual research， the fleshy scaly leaves of Lilium brownii var. viridulum should be selected for Lilii Bulbus in this formula， and the tuberous roots of Rehmannia glutinosa were selected for Rehmanniae Radix. According to the dosage conversion of ancient and modern times， the dosage is 245 g of fresh Lilii Bulbus and 400 g of fresh Rehmanniae Radix， and the ratio of their juice is 1∶1. Its efficacy is to nourish Yin and clear heat， and to tonify the heart and lungs， which is used to treat the heart and lung Yin deficiency syndrome of lily disease. Modern pharmacological studies have shown that the research on the pharmacological effects of Baihe Dihuangtang mainly focuses on anti-depressant， anti-anxiety， improving insomnia and regulating metabolism， and it is mostly used clinically for neurological disorders such as depression， anxiety and insomnia. The quality marker（Q-Marker） of Baihe Dihuangtang were predicted according to the principles of ingredient specificity， component validity， component measurability， formula compatibility， and quality transmissibility and traceability， and it was determined that catalpol， rhmannioside D， regaloside A， regaloside B， regaloside C， and acteoside could be selected as potential Q-Markers of Baihe Dihuangtang， which could provide scientific reference for the establishment of the quality control system and the development of compound preparation of this famous classical formula.

Objective To assess early clinical safety and efficacy of transfemoral transcatheter aortic valve replacement (TF-TAVR) for pure aortic regurgitation (PAR). Methods The clinical data of PAR patients who underwent TAVR in Wuhan Asia Heart Hospital and Wuhan Asia General Hospital from January 2018 to October 2022 were retrospectively analyzed. Patients were divided into a TF-TAVR group and a transapical transcatheter aortic valve replacement (TA-TAVR) group. The clinical data of the patients were analyzed. Results A total of 54 patients were enrolled, including 34 males and 20 females with an average age of 74.43±6.87 years. The preoperative N-terminal pro-B-type natriuretic peptide level was lower [808.50 (143.50, 2 937.00) pg/mL vs. 2 245.00 (486.30, 7 177.50) pg/mL, P=0.015], and the left ventricular end-diastolic diameter (56.00±6.92 mm vs. 63.07±10.23 mm, P=0.005) and sinus junction diameter (32.47±4.41 mm vs. 37.65±8.08 mm, P=0.007) were smaller in the TF-TAVR group. There was no death in the two groups during the hospitalization. Only 1 new death within postoperative 1 month in the TF-TAVR group (cerebral hemorrhage). A total of 2 new deaths in the TF-TAVR group (1 patient of sudden cardiac death and 1 of multiple organ failure), and there was no death in the TA-TAVR group within postoperative 3 months. There was 1 new death in the TA-TAVR group (details unknown), and there was no death in the TF-TAVR group within postoperative 6 months. There was no statistical difference between the two groups in the all-cause mortality and the cumulative survival rate during the follow-up period (P>0.05). The incidence of high atrioventricular block was 36.0% in the TF-TAVR group and 10.3% in the TA-TAVR group (P=0.024). There were no significant differences between the two groups in the perivalvular leakage (≥moderate), valve in valve, a second valve implantation, valve migration, cerebrovascular events, major vascular complications, complete left bundle branch block, new permanent pacemaker implantation or transferring to surgery (P>0.05). However, the incidence rates of complete left bundle branch block and new permanent pacemaker implantation were higher in the TF-TAVR group, accounting for 56.0% and 40.0%, respectively. Conclusion TF-TAVR is a safe and feasible treatment for PAR patients, which is comparable to TA-TAVR in the early postoperative safety and efficacy.