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.

Natural products are important sources for the discovery of anti-tumor drugs. Evodiamine is the main alkaloid component of the traditional Chinese herb Wu-Chu-Yu, and it has weak antitumor activity. In recent years, a number of highly active antitumor candidates have been discovered with a significant progress. This article reviews the research progress of evodiamine-based antitumor drug design strategies, in order to provide reference for the development of new drugs with natural products as leads.

Objective To establish the method for content determination of three lignans of Dendrobium Fimbriatum Hook..Methods The lignans in Dendrobium tasselii were identified by high-performance liquid chromatography/multi-stage mass spectrometry(HPLC-ESI/MSn)coupled with ultraviolet absorption spectrometry(UV)coupled with retention time localization of high-performance liquid chromatography(HPLC).The separation was carried out on a Kromasil 100-5 C18 column(4.6 mm×250 mm,5 μm)using a gradient elution of acetonitrile-0.1%formic acid solution as the mobile phase,the volume flow rate was 0.8 mL·min-1 and the column temperature was 35℃,and the mass spectrometry was performed using an ESI ion source with the data collected in the negative ion mode.The HPLC content was determined on the same column as that of MS analysis,with the mobile phase methanol + acetonitrile(V/V=1∶1)-0.01 mol/L ammonium acetate solution,gradient elution,flow rate of 0.8 mL·min-1,column temperature of 40℃,and detection wavelength of 215 nm.Results Syringaresinol di-O-glucoside and(-)-Syringaresinol 4-O-β-D-glucopyranoside and DL-Syringaresinol were identified from Dendrobium fimbriatum Hook.,and the results of content determination showed that the linear ranges of above three components were respectively 0.1701-3.4020,0.1020-2.0400,0.0403-0.8060 μg(r≥0.9995),the average recoveries were in the range of 97.71%-101.67%,and the relative standard deviations(RSDs)were all less than 3.0%.The contents of Syringaresinol di-O-glucoside and(-)-Syringaresinol 4-O-β-D-glucopyranoside and DL-Syringaresinol in the 10 batches of samples were 0.7779-1.3852,0.0734-0.1966,0.0295-0.1882 mg·g-1.Conclusion This research method can provide a reference basis for the quality evaluation method of Dendrobium fimbriatum Hook..

This article discussed the evolution of the traditional preparation process of Pinelliae Rhizoma Fermentata.The production methods for Pinelliae Rhizoma Fermentata in Song Dynasty include cake-making of Pinelliae Rhizoma together with ginger juice and fermentation after cake-making,and the former method of cake-making was the mainstream.The process technology in Jin and Yuan Dynasties inherited from that in Song Dynasty,and the application of Pinelliae Rhizoma Fermentata had certain limitations.The medical practitioners of Ming Dynasty elucidated the mechanism of processing of Pinelliae Rhizoma Fermentata,and proposed the view of"sliced Pinelliae Rhizoma being potent while fermented Pinelliae Rhizoma being mild".In the Ming Dynasty,LI Shi-Zhen defined the cake-making process and fermentation process for Pinelliae Rhizoma,and HAN Mao's Han Shi Yi Tong(Han's Clear View of Medicine)contained five prescriptions for the processing of Pinelliae Rhizoma Fermentata,which had the epoch-making signficance in the expansion of prescriptions for the processing of Pinelliae Rhizoma Fermentata.In the Qing Dynasty,HAN Fei-Xia's ten methods for making Pinelliae Rhizoma Fermentata were summarized on the basis of the methods recorded in Han Shi Yi Tong,and at that time,the processing of Pinelliae Rhizoma Fermentata and the preparation of Massa Medicata Fermentata interacted with each other.After the founding of the People's Republic of China,the local experience in the preparation of Pinelliae Rhizoma Fermentata was deeply influenced by the methods in the Qing Dynasty,and the local preparation technical standards gradually became the same.Moreover,this article also explored the issues of the importance of"Pinelliae Rhizoma"and"ingredients for fermentation",the pre-treatment of Pinelliae Rhizoma,the distinction between cake-making process and fermentation process for Pinelliae Rhizoma,the amount of flour added as well as the timing of adding,the addition of Massa Medicata Fermentata powder,the role of Alum in Pinelliae Rhizoma Fermentata and so on.

Objective: To explore the mechanism by which icariin alleviates viral myocarditis. Methods: CVB3-induced cardiomyocytes were used as an in vitro model of viral myocarditis to assess the effects of icariin treatment on cell viability, inflammation, and apoptosis. Moreover, the effects of icariin on ferroptosis and TLR4 signaling were assessed. After AC16 cells were transfected with TLR4 overexpression plasmids, the role of TLR4 in mediating the regulatory effect of icariin in viral myocarditis was investigated. Results: Icariin significantly elevated cell viability and reduced inflammatory factors TNF-α, IL-1β, IL-6, and IL-18. Flow cytometry revealed that icariin decreased apoptosis rate, and the protein expression of Bax and cleaved caspase 3 and 9 in CVB3-induced cardiomyocytes. Additionally, it suppressed ferroptosis including lipid peroxidation and ferrous ion, as well as the TLR4 signaling. However, TLR4 overexpression abrogated the modulatory effects of icariin. Conclusions: Icariin mitigates CVB3-induced myocardial injury by inhibiting TLR4-mediated ferroptosis. Further animal study is needed to verify its efficacy.

This paper outlines the common aspects of constructing integrated urban medical groups,focusing on governance,organizational restructuring,operational modes,and mechanism synergy.It then delves into the challenges in China's group construction,highlighting issues with power-responsibility alignment,capacity evolution,incentive alignment,and performance evaluation.Finally,the paper suggests strategies to enhance China's compact urban medical groups,focusing on governance reform,capacity building,benefit integration,and performance evaluation.