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.

Emodin is a hydroxyanthraquinone compound that is widely distributed and has multiple pharmacological activities, including anti-diarrheal, anti-inflammatory, and liver-protective effects. Research indicates that emodin may be one of the main components responsible for inducing hepatotoxicity. However, studies on the mechanisms of liver injury are relatively limited, particularly those related to bile acids(BAs) metabolism. This study aims to systematically investigate the effects of different dosages of emodin on BAs metabolism, providing a basis for the safe clinical use of traditional Chinese medicine(TCM)containing emodin. First, this study evaluated the safety of repeated administration of different dosages of emodin over a 5-week period, with a particular focus on its impact on the liver. Next, the composition and content of BAs in serum and liver were analyzed. Subsequently, qRT-PCR was used to detect the mRNA expression of nuclear receptors and transporters related to BAs metabolism. The results showed that 1 g·kg~(-1) emodin induced hepatic damage, with bile duct hyperplasia as the primary pathological manifestation. It significantly increased the levels of various BAs in the serum and primary BAs(including taurine-conjugated and free BAs) in the liver. Additionally, it downregulated the mRNA expression of farnesoid X receptor(FXR), retinoid X receptor(RXR), and sodium taurocholate cotransporting polypeptide(NTCP), and upregulated the mRNA expression of cholesterol 7α-hydroxylase(CYP7A1) in the liver. Although 0.01 g·kg~(-1) and 0.03 g·kg~(-1) emodin did not induce obvious liver injury, they significantly increased the level of taurine-conjugated BAs in the liver, suggesting a potential interference with BAs homeostasis. In conclusion, 1 g·kg~(-1) emodin may promote the production of primary BAs in the liver by affecting the FXR-RXR-CYP7A1 pathway, inhibit NTCP expression, and reduce BA reabsorption in the liver, resulting in BA accumulation in the peripheral blood. This disruption of BA homeostasis leads to liver injury. Even doses of emodin close to the clinical dose can also have a certain effect on the homeostasis of BAs. Therefore, when using traditional Chinese medicine or formulas containing emodin in clinical practice, it is necessary to regularly monitor liver function indicators and closely monitor the risk of drug-induced liver injury.
Emodin/administration & dosage* ; Bile Acids and Salts/metabolism* ; Animals ; Male ; Liver/injuries* ; Chemical and Drug Induced Liver Injury/genetics* ; Drugs, Chinese Herbal/adverse effects* ; Humans ; Rats, Sprague-Dawley ; Mice ; Rats

Aim To screen and study the expression of long non-coding RNA (IncRNA) in rats with middle cerebral artery occlusion (MCAO) with MCAO treated with Tao Hong Si Wu decoction (THSWD) and determine the possible molecular mechanism of THSWD in treating MCAO rats. Methods Three cerebral hemisphere tissue were obtained from the control group, MCAO group and MCAO + THSWD group. RNA sequencing technology was used to identify IncRNA gene expression in the three groups. THSWD-regulated IncRNA genes were identified, and then a THSWD-regu-lated IncRNA-mRNA network was constructed. MCODE plug-in units were used to identify the modules of IncRNA-mRNA networks. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) were used to analyze the enriched biological functions and signaling pathways. Cis- and trans-regulatory genes for THSWD-regulated IncRNAs were identified. Reverse transcription real-time quantitative pol-ymerase chain reaction (RT-qPCR) was used to verify IncRNAs. Molecular docking was used to identify IncRNA-mRNA network targets and pathway-associated proteins. Results In MCAO rats, THSWD regulated a total of 302 IncRNAs. Bioinformatics analysis suggested that some core IncRNAs might play an important role in the treatment of MCAO rats with THSWD, and we further found that THSWD might also treat MCAO rats through multiple pathways such as IncRNA-mRNA network and network-enriched complement and coagulation cascades. The results of molecular docking showed that the active compounds gallic acid and a-mygdalin of THSWD had a certain binding ability to protein targets. Conclusions THSWD can protect the brain injury of MCAO rats through IncRNA, which may provide new insights for the treatment of ischemic stroke with THSWD.

Objective To investigate different doses of the analgesic effects of dezocine comparing with sufentanil after laryngectomy.Methods A total of 129 patients who underwent elective partial laryngectomy from Feb 2022 to Jan 2023 were randomly assigned to dezocine 0.5 mg/kg group(group D1),dezocine 0.6 mg/kg group(group D2)and sufentanil 2 μg/kg group(group S).Twenty-four hours amount of drugs,the visual analogue scale(visual analogue scale,VAS)and 48 h total pressing times of PCA(patient-controlled intravenous analgesia,PCIA)were compared among the three groups at 6,12,24 and 48 h after operation,and the postoperative adverse reactions(nausea,vomiting,dizziness,urinary retention and respiratory depression)were recorded.Results There was no significant difference in 24 h amount of drugs among the three groups.The VAS score of group D1 was higher than that of group S at 6 h postoperatively(P＜0.05),but did not differ significantly among the three groups at 12,24 and 48 h.There was no significant differences in the number of compressions and postoperative adverse reactions among the three groups.Conclusion Compared with sufentanil,0.6 mg/kg dezocine can provide the same degree of analgesic effect.However,no advantage was found to reduce adverse reactions.

Objective To construct a lentiviral vector for overexpression of bone morphogenetic protein 7(BMP7)in mice,and the effect of BMP7 overexpression on the expression of Jagged1 in mouse aortic endothelial cells and the calcification of the co-cultured vascular smooth muscle cells(VSMCs)were analyzed.Methods According to the target gene information Mouse-BMP7(NM_007557.3)and plasmid information pLVX-zsGreen-C1,gene sequence synthesis was carried out to construct BMP7 overexpression lentivirus.The efficiency of BMP7 overexpression lentivirus infection was detected by qPCR;the expression of Jagged1 protein in aortic endothelial cells from infected mice was detected by Western blot.The endothelial cells with lentivirus overexpressing BMP7 were co-cultured with VSMCs,and the calcification of VSMCs was observed by alizarin red staining.Results BMP7 overexpression lentiviral vector was successfully constructed and transfected into aortic endothelial cells.qPCR test results showed that the expression level of BMP7 mRNA was significantly increased in the BMP7 overexpression group than that in the normal control group(P<0.01),while there was no significant difference in the expression of BMP7 mRNA between the empty vector control group and the normal control group(P>0.05).Western blot results showed that the expression level of Jagged1 protein in endothelial cells of mouse in the BMP7 overexpression group was significantly lower than that in the normal control group(P<0.01),while there was no significant difference in the expression level of Jagged1 protein in endothelial cells between the empty vector control group and the normal control group(P>0.05).The results of alizarin red staining showed that the calcification of VSMCs was significantly increased after co-cultured with endothelial cells infected with BMP7 lentivirus.Conclusion Mouse BMP7 overexpression lentiviral vector was successfully constructed,and overexpression of BMP7 can reduce the expression of Jagged1 in mouse aortic endothelial cells and promote the calcification of co-cultured VSMCs.

Objective To investigate the effect of artemisinin(ART)on the malignant biological behavior of colorectal cancer(CRC)cells stimulated by glucose and its mechanism.Methods The concentration gradients of 0,5,10,20,40 and 60 μmol/L of ART were used to treat the human colorectal cancer cell line SW480,and then the cell viability was detected by CCK-8.Cell apoptosis was detected by flow cytometry.Transwell was used to detect the cell migration and invasion.Western blot was used to detect the apoptosis,epithelial-mesenchymal transition(EMT)and Janus kinase 2(JAK2)/signal transducer and activator of transcription 3(STAT3)related proteins expression.Results Compared with the 0 μmol/L of ART,the viability of SW480 cells decreased under 5,10,20,40,60 μmol/L of ART treatment(P<0.05),and IC50 was 36.91 μmol/L.Therefore,the cells treated with 10,20 and 40 μmol/L of ART were as the low-dose,medium-dose and high-dose ART groups,the cells treated with 0 μmol/L of ART were as the control group,and the cells treated with 40 μmol/L of ART and 10 μmol/L of Coumermycin A1 were as the Coumermycin A1 group.Compared with the control group,the cell scratch wound healing rate,invasion ability,and expression levels of Bcl-2,N-cadherin,Vimentin,p-JAK2,and p-STAT3 in the low-dose ART group,the medium-dose ART group,and the high-dose ART group decreased obviously(P<0.05),while the apoptosis rate,and expression levels of Bax,Caspase-3 and E-cadherin increased(P<0.05).Compared with the high-dose ART group,the cell scratch wound healing rate,invasion ability,and expression levels of Bcl-2,N-cadherin,Vimentin,p-JAK2,and p-STAT3 in the Coumermycin A1 group increased obviously(P<0.05),while the apoptosis rate,and expression levels of Bax,Caspase-3 and E-cadherin decreased(P<0.05).Conclusion ART may inhibit the viability,migration,invasion and EMT of glucose-stimulated CRC cells and promote apoptosis by inhibiting the JAK2/STAT3 signaling pathway.