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.

Objective:To investigate the application effect of situational simulation combined with the Debriefing-GAS method in the teaching of prenatal genetic counseling.Methods:A total of 30 medical students of the five- and eight-year programs in the classes of 2017 and 2018 who received genetic counseling training in The First Affiliated Hospital of Sun Yat-sen University from May 2021 to May 2022 were selected as research subjects, and situational simulation combined with the debriefing-GAS method was used for the teaching of prenatal genetic counseling. Assessment was performed by the teacher to evaluate the change in genetic counseling abilities during the teaching process, and a questionnaire survey was conducted to investigate the degree of satisfaction with teaching among the students. SPSS 26.0 software was used for data analysis; normally distributed continuous data were expressed as mean±standard deviation, non-normally distributed continuous data were expressed as M d(P 25,P75), and categorical data were expressed as frequency and rate; the paired samples t-test was used for comparison of assessment scores before and after teaching. Results:After teaching, there were significant increases in the assessment scores of genetic counseling [(74.5±18.6) points vs. (87.2±14.5) points, t=4.10, P<0.001] and comprehensive abilities such as clinical ability [(35.4±9.6) points vs. (41.1±6.9) points, t=3.72, P=0.001], doctor-patient communication [(17.5±4.6) points vs. (20.8±3.8) points, t=4.34, P<0.001], professional literacy [(11.0±2.5) points vs. (12.5±2.3) points, t=2.89, P=0.007], teamwork [(3.5±1.0) points vs. (4.2±0.8) points, t=3.67, P=0.001], and organizational effectiveness [(7.1±2.0) points vs. (8.3±1.7) points, t=2.94, P=0.006]. The questionnaire survey showed that the degree of satisfaction among students was rated above satisfaction for the reasonability of the implementation process and links of genetic counseling teaching [3.0 (3.0, 4.0) points], teaching quality [3.5 (3.0, 4.0) points], whether the teaching model could effectively increase the interest and initiative in learning [4.0 (3.0, 4.0) points], the improvement in theoretical knowledge [4.0 (3.0, 4.0) points], communication skills in genetic counseling [3.0 (3.0, 4.0) points], and the understanding of related techniques and application prospect [3.0 (3.0, 4.0) points]. However, two students (6.7%) thought that this teaching model could not efficiently reach teaching objectives, since the teaching process was slightly complicated. Conclusions:Situational simulation combined with the debriefing-GAS method has achieved a good effect in the teaching of prenatal genetic counseling and can help undergraduates to master the theoretical knowledge of prenatal genetic counseling and improve their comprehensive clinical abilities, with a relatively high degree of satisfaction, and therefore, it holds promise for clinical application.

Aim To study the regulatory effect of poly-datin on D-galactose-induced aging model mice.Methods Fifty-six ICR mice(half male and half fe-male)were divided into normal group,model group,positive group,low,medium and high polydatin treat-ment groups.Aging model was established by subcuta-neous injection of D-galactose(500 mg·kg-1)into the back of neck every day.During the modeling peri-od,the positive group was given donepezil hydrochlo-ride tablets(0.75 mg·kg-1)by gavage,the treat-ment group was given polydatin(40,70,100 mg·kg1)by gavage,and the normal group was given the same amount of normal saline.The learning and cogni-tive ability of mice was evaluated by nesting experi-ment,new object recognition experiment and Morris water maze experiment.The heart,liver,spleen,kid-ney and thymus of mice were taken to calculate the or-gan index.The pathological changes of whole brain tis-sue in mice were observed by hematoxylin-eosin(HE)staining.The levels of T-SOD,MDA,GSH-Px and AchE in serum and whole brain tissue of mice were de-tected by ELISA.The protein expression levels of Keap1,Nrf2 and HO-1 in hippocampus of mice were detected by Western blot.Results Compared with the model group,the nesting ability,the ability to recog-nize new objects and the ability to find platforms under-water of the mice in the positive group and the low,medium and high dose groups of polydatin were im-proved.The organ index increased.The neuronal dam-age in the cerebral cortex and hippocampus was signifi-cantly ameliorated.The activities of T-SOD and GSH-Px in serum and brain tissue increased and the activi-ties of MDA and AchE decreased.The expression lev-els of Nrf2 and HO-1 protein in hippocampus in-creased,and the expression level of Keap1 protein de-creased.Conclusions Polydatin can ameliorate the learning and cognitive impairment in D-galactose-in-duced aging model mice,and its mechanism may be related to the Keap1/Nrf2/HO-1 pathway.

Aim To study the proliferation,invasion and migration ability of Quaking(QKI)in gastric cancer(GC)via elucidating the molecular mechanisms associated with QKI in the occurrence and development of GC through bioinformatics.Methods Differential expression analysis of QKI was performed across vari-ous human cancer samples by merging data from the TCGA and GTEx databases.The correlation was ana-lyzed between QKI protein expression and tumor muta-tion burden(TMB)score,microsatellite instability(MSI)score,and ESTIMATE score,and the correla-tion was also explored between QKI protein expression and overall survival(OS),disease free survival(DFS),and progression free survival(PFS).EMT related genes that could encode DECircRNAs were ob-tained through bioinformatics analysis to construct a QKI-EMT-circRNAs regulatory network.The differenti-ally expressed circRNAs and EMT related genes in TMK1 cells were verified.The proliferation,invasion and migration ability of the QKI was studied by using the knockdown system.Results QKI was differential-ly expressed in the vast majority of tumors and was closely related to TMB,MSI,and tumor microenviron-ment(TME);QKI emerged as a high-risk factor for predicting OS,DFS,and PFS in individuals with com-mon human cancers.QKI regulated the splicing of 6 EMT related gene transcripts to form eight circRNAs,all of which were significantly associated with the prog-nosis of gastric cancer patients.Cell experiments showed that compared to normal gastric epithelial cells,only hsa_ccirc_0004015,CALD1,and CDK14 were down-regulated in TMK1 cells.Knocking down QKI inhibited the proliferation,invasion and migration ability of TMK1 cells.Conclusion QKI exerts regu-latory control over the transcription of six EMT-related genes,resulting in the formation of circRNAs,thereby promoting the pathogenesis and progression of GC.QKI is highly expressed in TMK1 cells,and knock-down of QKI can inhibit the proliferation,invasion and migration ability of TMK1 cells.

Objective To investigate the expression and prognostic value of spindle and kinetochore-associated complex subunit 2(SKA2)in cervical cancer tissues,as well as its impact on the proliferation,migration and invasion of cervical cancer cells.Methods The expression of SKA2 in cervical cancer tissues was analyzed by bioinformatics database and immunohistochemical SP method,and the relationship between SKA2 expression level and clinicopathological features of cervical cancer patients and its prognostic value was analyzed.The mRNA expression of SKA2 in human normal cervical cells(HcerEpic)and cervical cancer cells(HeLa,SiHa,CaSki,C-33A)was detected by RT-qPCR.Cervical cancer cells SiHa with higher SKA2 expression level was selected for further study.SiHa cell model with down-regulated SKA2 expression was constructed,and its knockdown effect was verified.Cell proliferation capacity was detected by CCK-8 method,cell migration capacity was detected by cell scratch wound healing assay,and cell migration and invasion capacity was detected by Transwell assay.Results Compared with normal cervical tissues and cells,the expression levels of SKA2 mRNA and protein were higher in cervical cancer tissues and cells,and the differences were statistically significant(P<0.05).High SKA2 expression was associated with FIGO staging in patients with cervical cancer.Furthermore,SKA2 knockdown could inhibit the proliferation,migration and invasion of SiHa cells in cervical cancer(P<0.05).Conclusion SKA2 is up-regulated in cervical cancer tissues and cells,and can promote the proliferation,migration and invasion of cervical cancer cells.The expression level of SKA2 is associated with the progression of cervical cancer,and the prognosis of cervical cancer patients with high SKA2 expression is worse.

Objective:To explore the potential action mechanism of Huotu Jiji Pellets(HJP)in the treatment of erectile dys-function(ED)based on network pharmacology and molecular docking.Methods:We identified the main effective compounds and active molecular targets of HJP from the database of Traditional Chinese Medicine Systems Pharmacology(TCMSP)and Integrative Pharmacology-Based Research Platform of Traditional Chinese Medicine(TCMIP)and the therapeutic target genes of ED from the data-bases of Genecards.Then we obtained the common targets of HJP and ED using the Venny software,constructed a protein-protein in-teraction(PPI)network of HJP acting on ED,and screened out the core targets with the Cytoscape software.Lastly we performed GO functional enrichment and KEGG pathway enrichment analyses of the core targets followed by molecular docking of HJP and the core targets using Chem3D and AutoDock Tools and QuickVina-W software.Results:A total of 64 effective compounds,822 drug-related targets,1 783 disease-related targets and 320 common targets were obtained in this study.PPI network analysis showed that the core targets of HJP for ED included ESR1,HSP90AA1,SRC,and STAT3.GO functional enrichment analysis indicated the involvement of the core targets in such biological processes as response to xenobiotic stimulus,positive regulation of kinase activity,and positive regu-lation of MAPK cascade.KEGG pathway enrichment analysis suggested that PI3K-Akt,apoptosis,MAPK,HIF-1,VEGF,autophagy and other signaling pathways may be related to the mechanism of HJP acting on ED.Molecular docking prediction exhibited a good doc-king activity of the key active molecules of HJP with the core targets.Conclusion:This study showed that HJP acted on ED through multi-components,multi-targets and multi-pathways,which has provided some evidence and reference for the clinical treatment and subsequent studies of the disease.