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.

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.

OBJECTIVE: To study the in vitro and in vivo antitumor effects of the polysaccharide of Alocasia cucullata (PAC) and the underlying mechanism. METHODS: B16F10 and 4T1 cells were cultured with PAC of 40 µg/mL, and PAC was withdrawn after 40 days of administration. The cell viability was detected by cell counting kit-8. The expression of Bcl-2 and Caspase-3 proteins were detected by Western blot and the expressions of ERK1/2 mRNA were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A mouse melanoma model was established to study the effect of PAC during long-time administration. Mice were divided into 3 treatment groups: control group treated with saline water, positive control group (LNT group) treated with lentinan at 100 mg/(kg·d), and PAC group treated with PAC at 120 mg/(kg·d). The pathological changes of tumor tissues were observed by hematoxylin-eosin staining. The apoptosis of tumor tissues was detected by TUNEL staining. Bcl-2 and Caspase-3 protein expressions were detected by immunohistochemistry, and the expressions of ERK1/2, JNK1 and p38 mRNA were detected by qRT-PCR. RESULTS: In vitro, no strong inhibitory effects of PAC were found in various tumor cells after 48 or 72 h of administration. Interestingly however, after 40 days of cultivation under PAC, an inhibitory effect on B16F10 cells was found. Correspondingly, the long-time administration of PAC led to downregulation of Bcl-2 protein (P<0.05), up-regulation of Caspase-3 protein (P<0.05) and ERK1 mRNA (P<0.05) in B16F10 cells. The above results were verified by in vivo experiments. In addition, viability of B16F10 cells under long-time administration culture in vitro decreased after drug withdrawal, and similar results were also observed in 4T1 cells. CONCLUSIONS Long-time administration of PAC can significantly inhibit viability and promote apoptosis of tumor cells, and had obvious antitumor effect in tumor-bearing mice.
Mice ; Animals ; Alocasia/metabolism* ; MAP Kinase Signaling System ; Caspase 3/metabolism* ; Apoptosis ; RNA, Messenger/metabolism*

The purpose of this study was to enrich the genomic information and provide a basis for further development and utilization of Polygonatum kingianum. The fresh rhizome of P. kingianum was used as the experimental material, and the full-length transcriptome was sequenced by PacBio Sequel platform. The final measured polymerase reads were 1 120 485, with a total of 77.73 GB of data. In NR database, 41 864 homologous sequence alignments were aligned to 5 species; 40 506 were annotated in the KOG database and classified into 26 categories based on their functionality; 69 060 GO annotated 32 functional groups divided into 3 categories: cellular components, molecular functions, and biological processes; 45 779 annotations were added to 145 metabolic pathways in the KEGG database. Among them, there are 127 identified transcripts related to polysaccharide synthesis in the glycolysis/gluconeogenesis metabolic pathway, 144 in the starch and sucrose metabolic pathway, 85 in the amino acid sugar and nucleotide sugar metabolic pathway, and 69 in the fructose and mannose metabolic pathway. In addition, 1 781 transcription factors were detected, distributed in 37 transcription factor families; 180 293 SSR loci were detected, among which single base repeats accounted for the most, accounting for 30.48% of all base repeats, and at least five base repeats, accounting for only 0.14% of single base repeats. The results of this study provide important data supporting for enriching the genomic information of P. kingianum and exploring its effective biosynthetic pathway.

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.

Thrombus is a major factor leading to cardiovascular diseases such as myocardial infarction and stroke. Although fibrinolytic anti-thrombotic drugs have been widely used in clinical practice, they are still limited by narrow therapeutic windows, short half-lives, susceptibility to inactivation, and abnormal bleeding caused by non-targeting. Therefore, it is crucial to effectively deliver thrombolytic agents to the site of thrombus with minimal adverse effects. Based on the long blood circulation and excellent drug-loading properties of human serum albumin (HSA), we employed genetic engineering techniques to insert a functional peptide (P-selectin binding peptide, PBP) which can target the thrombus site to the N-terminus of HSA. The fusion protein was expressed using Pichia pastoris and purified by Ni-chelating affinity chromatography. After being loaded with gold nanoparticles (Au NPs), the fusion protein formed homogeneous and stable nanoparticles (named as PBP-HSA@Au) with a diameter of 17.7 ± 1.0 nm and a zeta potential of -11.3 ± 0.2 mV. Cytotoxicity and hemolysis tests demonstrated the superb biocompatibility of PBP-HSA@Au. Platelet-targeting experiments confirmed the thrombus-targeting ability conferred by the introduction of PBP into PBP-HSA@Au. Upon near-infrared ray (NIR) irradiation, PBP-HSA@Au rapidly converted light energy into heat, thereby disrupting fibrinogen and exhibiting outstanding thrombolytic efficacy. The designed HSA fusion protein delivery system provides a precise, rapid, and drug-free treatment strategy for thrombus therapy. This system is characterized by its simple design, high biocompatibility, and strong clinical applicability. All animal experiments involved in this study were carried out under the protocols approved by the Animal Experiment Ethics Committee of Jiangnan University [JN. No20230915S0301015(423)].