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.

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.

italic>Salmonella has emerged as a promising tumor-targeting strategy in recent years due to its good tumor targeting ability and certain safety. In order to further optimize its therapeutic effect, scientists have tried to modify Salmonella, including its attenuation and drug loading. This paper summarizes the mechanism and research progress of Salmonella-mediated targeted tumor therapy, and introduces the strategies and related progress of its modification and optimization. At the same time, the advantages, current challenges and future development directions of Salmonella-mediated tumor therapy are summarized.

Objective:To explore the application value of lifestyle intervention based on planned behavior theory in arrhythmic patients.Methods:A total of 100 arrhythmic patients admitted in our hospital from Jan 2020 to Jan 2023 were selected.Patients were divided into control group(n=50)and experimental group(n=50)according to random number table method.The control group received routine nursing intervention,while the experimental group received lifestyle intervention based on planned behavior theory,both groups were intervened for three months.The heart function,scores of activity of daily living scale(ADL),exercise of self-care agency scale(ES-CA),the MOS item short from health survey(SF-36)and intervention compliance were compared between two groups before and after intervention.Results:Compared with control group after intervention,there was significant decrease in percentage of NYHA class Ⅲ(32.00％vs.8.00％)in experimental group(x2=9.000,P=0.003).Compared with control group,there were significant rise in scores of ADL[(68.84±6.43)points vs.(85.58±5.08)points],ESCA[(112.94±6.17)points vs.(144.52±5.25)points],physiological function[(74.05±3.12)points vs.(83.34±2.89)points]and psychological function[(70.30±3.04)points vs.(82.52±3.08)points]of SF-36 in experimental group after intervention(P=0.001 all).Compared with control group,there was signifi-cant rise in overall compliance rate(70.00％vs.94.00％)in experimental group after intervention(Z=2.824,P=0.005).Conclusion:Lifestyle intervention based on planned behavior theory can significantly improve the heart function,ability of daily living,self-care capacity,quality of life and compliance in arrhythmic patients,which has high application value.

Objective:To investigate the status quo of training and domestic use of 707 clinical nutrition support specialty nurses from 21 provinces,cities,and autonomous regions in China. And to analyze their influencing factors and provide reference for improving the training system of clinical nutrition support specialty nurses,selection and development of specialist nurses in clinical nutrition support. Methods:From October to November 2023,a cross-sectional survey was conducted on 707 clinical nutrition support specialty nurses from 21 provinces,cities,and autonomous regions across China was conducted using a convenience sampling method based on a questionnaire about the training and home use of clinical nutrition support nurses. Univariate and multiple linear regression analysis was used to examine the use status and application of clinical nutrition support specialty nurses in five aspects:clinical nursing practice,nursing education,nursing management,coordination,nursing research and consultation. Results:The use of specialist clinical nutrition support nurses is not ideal,with 75.67％ of specialist nurses scoring less than 208 points (i.e. less than 80％ of the total score). Among the use of different dimensions,the clinical nursing practice dimension received the highest score (54.17±10.26),followed by the nursing education dimension (36.98±8.00). The results of multiple linear regression analysis show that hospital level and professional title are independent influencing factors influencing the use and development of specialist nurses. Conclusion:There is a need to further improve the utilisation of clinical nutrition support nurses. It is recommended that links and cooperation between hospitals at all levels,communities,and families be strengthened. For specialist nurses with higher professional titles,encourage them to fully play their roles,strengthen training in weak areas,continuously optimize the professional ability of clinical nutrition support nursing teams,comprehensively improve the quality of clinical nutrition support specialist nursing,and promote their high-level development.

Objective:To summarize the best evidence on case management of patients with home enteral nutrition.Methods:Relevant evidence on the case management of home enteral nutrition patients was retrieved by literature search,and the evidence was extracted and summarized for the literature that met the quality requirements.Result:A total of 10 literatures were included,including 1 guideline,3 expert consensus,2 industry standards,1 systematic review and 3 randomized controlled trials.By establishment of archives,policy management,establishment of multidisciplinary teams,overall evaluation of home enteral nutrition,as well as implementation management,a total of 33 home enteral nutrition case management was summarized from 6 aspects including health education and follow-up,etc.Conclusion:All the summarized relevant evidence about case filing and management of home enteral nutrition patients can be applied in clinical practice to promote the standardized management of home enteral nutrition.