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.

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.

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.

Objective To investigate the distribution patterns of traditional Chinese medicine(TCM)constitution in 959 patients with endometriosis(EMs).Methods From January 2019 to November 2019,959 EMs patients were selected from Guang'anmen Hospital of China Academy of Chinese Medical Sciences,Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University,Beijing Hospital,Dongfang Hospital of Beijing University of Chinese Medicine,Beijing Friendship Hospital Affiliated to Capital Medical University,and Fuxing Hospital Affiliated to Capital Medical University.The general clinical information of the patients was recorded and then the TCM constitution was identified.After that,the correlation of TCM constitution distribution with concurrent constitution and the relationship of TCM constitution distribution with age and the complication of dysmenorrhea were analyzed.Results(1)The constitution types of EMs patients listed in descending order of the proportion were yang deficiency constitution(65.1％,624/959),qi stagnation constitution(58.4％,560/959),qi deficiency constitution(52.8％,506/959),blood stasis constitution(44.2％,424/959),phlegm-damp constitution(42.5％,408/959),damp-heat constitution(41.9％,402/959),yin deficiency constitution(39.6％,380/959),balanced constitution(26.8％,257/959),and inherited special constitution(16.6％,159/959).Among the patients,there were fewer patients with single constitution,accounting for 20.2％(194/959),and most of them had concurrent constitution types,accounting for 79.8％(765/959).(2)The association rule mining based on Apriori algorithm obtained 33 related rules.The concurrent constitution types of qi deficiency-yang deficiency,blood stasis-yang deficiency,and blood stasis-qi stagnation were the association rules with high confidence.(3)Compared with patients aged 35 years and below,the patients over 35 years old were predominated by high proportion of blood stasis constitution(P＜0.05).Compared with patients without dysmenorrhea,the patients with dysmenorrhea had the increased proportion of biased constitutions and the decreased proportion of balanced constitution(P＜0.05 or P＜0.01).Conclusion Yang deficiency constitution,qi stagnation constitution,qi deficiency constitution and blood stasis constitution are the high-risk constitution types of EMs patients.The concurrent constitution types are commonly seen in EMs patients,which are more common than single biased constitution.Management of EMs patients with the methods of warming yang,relieving stagnation,benefiting qi and activating blood will be helpful for correcting the biased constitutions in time and preventing disease progression,which will achieve the preventive treatment efficacy through TCM constitution correction.

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 effect of safflower yellow pigment injection combined with alprostadil on patients after coronary artery bypass grafting(CABG).Methods:A total of 92 patients with coronary heart disease who received CABG in Department of Cardiovascular Surgery,Handan Central Hospital between September 2018 and September 2020 were selected.According to order of admission,they were divided into control group(n=46,from September 2018 to Sep-tember 2019,routine therapy+alprostadil after CABG)and study group(n=46,from October 2019 to September 2020,safflower yellow pigment injection based on control group),both groups were treated for 28d.On 3d after drug withdraw-al,therapeutic effect,cardiac function indexes,four myocardial enzyme spectrum and perioperative indexes were compared between two groups.Results:On 3d after drug withdrawal,compared with control group,patients in study group had sig-nificant higher total effective rate(73.9％vs.91.3％),left ventricular ejection fraction(LVEF)[(55.77±4.48)％vs.(62.18±4.21)％](P=0.028,＜0.001),and significant lower left atrial diameter(LAD)[(36.83±3.45)mm vs.(32.09±3.23)mm],left ventricular end-diastolic diameter(LVEDd)[(49.04±4.65)mm vs.(43.83±5.24)mm],levels of creatine kinase(CK)[(125.13±14.21)U/L vs.(62.56±8.42)U/L],lactate dehydrogenase(LDH)[(203.58±31.63)U/L vs.(156.07±22.26)U/L],aspartate aminotransferase(AST)[(44.25±12.98)U/L vs.(35.41±12.37)U/L]and creatine kinase isoenzyme MB(CK-MB)[(28.11±9.84)U/L vs.(17.59±7.41)U/L](P＜0.001 all).Conclusion:The combination of safflower yellow pigment injection and alprostadil can improve the thera-peutic effect and heart function,and reduce myocardial injury in patients after CABG.