ObjectiveThis paper aims to explore the in vitro anti-psoriasis activity and potential mechanism of Kangfuxin liquid （KFX liquid）， providing experimental evidence for the anti-psoriasis effect of KFX liquid. MethodsFirstly， the uninduced human immortalized keratinocyte cells （HaCaT cells） were divided into seven groups， namely the control group and KFX liquid groups with different doses （5， 10， 20， 40， 80， 160 g·L^-1）. After being treated with different concentrations of KFX liquid， the effect of KFX liquid on the normal cell proliferation was detected by using the cell counting kit-8 （CCK-8） method. Secondly， the uninduced HaCaT cells were divided into six groups， namely the control group and recombinant human interleukin-7A （rh-IL-7A） groups with different doses （5， 10， 50， 100， 120 g·L^-1）. After being treated with different concentrations of recombinant human interleukin-17A （rh IL-17A） liquid， the effect of rh IL-17A on cell proliferation was detected. The optimal induction concentration was screened. Then， normal HaCaT cells were divided into a control group and KFX liquid groups with different doses （5， 10， 20， 40， 80， 160 g·L^-1）. Except for the control group， the other groups established psoriasis cell models with the optimal induction concentration of rh IL-17A. After being treated with different concentrations of KFX liquid， the effects of KFX liquid on the psoriasis-like HaCaT cell proliferation were investigated. Finally， the uninduced HaCaT cells were divided into six groups， namely the control group， rh IL-17A group， methotrexate （MTX） group， and KFX liquid groups with different doses （20， 40， 80 g·L^-1）. Except for the control group， the other groups used the optimal induction concentration of rh IL-17A to establish psoriasis cell models. After being treated with different drugs， the cell migration levels were detected through scratch assays， and real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the relative mRNA expression levels of Ki-67 antigen （Ki67）， S100 calcium-binding protein A7 （S100A7）， S100 calcium-binding protein A8 （S100A8）， and S100 calcium-binding protein A9 （S100A9）， thereby comprehensively evaluating the in vitro anti-psoriasis activity of KFX liquid. By detecting the relative mRNA expression levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and chemokine-20 （CXCL-20） inflammatory-related factors in psoriasis-like HaCaT cells and the protein expression levels of Janus kinase 3 （JAK3）， phosphorylated Janus kinase 3 （p-JAK3）， signal transducer and activator of transcription 3 （STAT3）， and phosphorylated signal transducer and activator of transcription 3 （p-STAT3）， the mechanism was explored. ResultsCompared with that of control group， when treated with 80 g·L^-1 KFX liquid for 72 h （P<0.05） and at different times with 160 g·L^-1 KFX liquid， the HaCaT cell proliferation activity was significantly affected （P<0.01）， while the other concentrations of KFX liquid had no significant differences in cell morphology and cell proliferation activity at different times， indicating that the KFX liquid is relatively safe for HaCaT cells and has no obvious toxic side effects. Compared with that of control group， when treated with different concentrations of rh IL-17A for 24 h， the HaCaT cell proliferation activity was significantly enhanced， and the cell activity was the strongest when the concentration was 100 μg·L^-1 （P<0.05）， with a density close to 100% and intact cell morphology， indicating that 100 μg·L^-1 is the optimal concentration for inducing HaCaT cell proliferation. The results of the KFX liquid treatment on rh IL-17A-induced psoriasis-like cells show that the KFX liquid not only effectively inhibits the rh IL-17A-induced psoriasis-like HaCaT cell proliferation activity （P<0.01）， but also significantly reduces the migration ability of rh IL-17A-induced psoriasis-like HaCaT cells （P<0.01）， and the relative mRNA expression levels of Ki67， S100A7， S100A8， and S100A9 （P<0.01）. Moreover， the KFX liquid can significantly reduce the relative mRNA expression levels of IL-1β， IL-6， and CXCL-20 in rh IL-17A-induced psoriasis-like cells （P<0.01）， and significantly inhibit the phosphorylation levels of JAK3 and STAT3 proteins （P<0.05， P<0.01）. ConclusionThe KFX liquid has no obvious toxicity to uninduced HaCaT cells. It can inhibit rh IL-17A-induced psoriasis-like HaCaT cell proliferation， reduce the cell migration ability， and has good in vitro anti-psoriasis activity. Its action mechanism may be related to downregulating the expression levels of inflammation-related cytokines in the JAK3/STAT3 signaling pathway and inhibiting the phosphorylation levels of JAK3 and STAT3 proteins.

ObjectiveThis paper aims to explore the in vitro anti-psoriasis activity and potential mechanism of Kangfuxin liquid （KFX liquid）， providing experimental evidence for the anti-psoriasis effect of KFX liquid. MethodsFirstly， the uninduced human immortalized keratinocyte cells （HaCaT cells） were divided into seven groups， namely the control group and KFX liquid groups with different doses （5， 10， 20， 40， 80， 160 g·L^-1）. After being treated with different concentrations of KFX liquid， the effect of KFX liquid on the normal cell proliferation was detected by using the cell counting kit-8 （CCK-8） method. Secondly， the uninduced HaCaT cells were divided into six groups， namely the control group and recombinant human interleukin-7A （rh-IL-7A） groups with different doses （5， 10， 50， 100， 120 g·L^-1）. After being treated with different concentrations of recombinant human interleukin-17A （rh IL-17A） liquid， the effect of rh IL-17A on cell proliferation was detected. The optimal induction concentration was screened. Then， normal HaCaT cells were divided into a control group and KFX liquid groups with different doses （5， 10， 20， 40， 80， 160 g·L^-1）. Except for the control group， the other groups established psoriasis cell models with the optimal induction concentration of rh IL-17A. After being treated with different concentrations of KFX liquid， the effects of KFX liquid on the psoriasis-like HaCaT cell proliferation were investigated. Finally， the uninduced HaCaT cells were divided into six groups， namely the control group， rh IL-17A group， methotrexate （MTX） group， and KFX liquid groups with different doses （20， 40， 80 g·L^-1）. Except for the control group， the other groups used the optimal induction concentration of rh IL-17A to establish psoriasis cell models. After being treated with different drugs， the cell migration levels were detected through scratch assays， and real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the relative mRNA expression levels of Ki-67 antigen （Ki67）， S100 calcium-binding protein A7 （S100A7）， S100 calcium-binding protein A8 （S100A8）， and S100 calcium-binding protein A9 （S100A9）， thereby comprehensively evaluating the in vitro anti-psoriasis activity of KFX liquid. By detecting the relative mRNA expression levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and chemokine-20 （CXCL-20） inflammatory-related factors in psoriasis-like HaCaT cells and the protein expression levels of Janus kinase 3 （JAK3）， phosphorylated Janus kinase 3 （p-JAK3）， signal transducer and activator of transcription 3 （STAT3）， and phosphorylated signal transducer and activator of transcription 3 （p-STAT3）， the mechanism was explored. ResultsCompared with that of control group， when treated with 80 g·L^-1 KFX liquid for 72 h （P<0.05） and at different times with 160 g·L^-1 KFX liquid， the HaCaT cell proliferation activity was significantly affected （P<0.01）， while the other concentrations of KFX liquid had no significant differences in cell morphology and cell proliferation activity at different times， indicating that the KFX liquid is relatively safe for HaCaT cells and has no obvious toxic side effects. Compared with that of control group， when treated with different concentrations of rh IL-17A for 24 h， the HaCaT cell proliferation activity was significantly enhanced， and the cell activity was the strongest when the concentration was 100 μg·L^-1 （P<0.05）， with a density close to 100% and intact cell morphology， indicating that 100 μg·L^-1 is the optimal concentration for inducing HaCaT cell proliferation. The results of the KFX liquid treatment on rh IL-17A-induced psoriasis-like cells show that the KFX liquid not only effectively inhibits the rh IL-17A-induced psoriasis-like HaCaT cell proliferation activity （P<0.01）， but also significantly reduces the migration ability of rh IL-17A-induced psoriasis-like HaCaT cells （P<0.01）， and the relative mRNA expression levels of Ki67， S100A7， S100A8， and S100A9 （P<0.01）. Moreover， the KFX liquid can significantly reduce the relative mRNA expression levels of IL-1β， IL-6， and CXCL-20 in rh IL-17A-induced psoriasis-like cells （P<0.01）， and significantly inhibit the phosphorylation levels of JAK3 and STAT3 proteins （P<0.05， P<0.01）. ConclusionThe KFX liquid has no obvious toxicity to uninduced HaCaT cells. It can inhibit rh IL-17A-induced psoriasis-like HaCaT cell proliferation， reduce the cell migration ability， and has good in vitro anti-psoriasis activity. Its action mechanism may be related to downregulating the expression levels of inflammation-related cytokines in the JAK3/STAT3 signaling pathway and inhibiting the phosphorylation levels of JAK3 and STAT3 proteins.

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 establish and validate a multimodal MRI radiomics model based on intratumoral and peritumoral heterogeneity for prediction of spatial pattern of locally recurrent high-grade gliomas(HGGs).Methods A retrospective analysis was conducted on the clinical and imaging data of all HGGs patients who underwent maximum safe resection followed by postoperative radiotherapy combined with temozolomide treatment and experienced in local recurrence in Army Medical Center of PLA from 2012 to 2021.Two radiologists independently assessed the spatial patterns of locally recurrence HGGs through continuous follow-up MRI data,and primarily categorized the pattern into intra-resection cavity recurrence and extra-resection cavity recurrence.The subjected patients were randomly divided into a training set and a validation set in a 7∶3 ratio.In the training set,Pearson or Spearman correlation analysis and least absolute shrinkage and selection operator(LASSO)analysis were employed to screen radiomic features within the intratumoral and peritumoral regions,as well as to calculate radiomic scores.A radiomics model was established using logistic regression analysis.The performance of the model was assessed using calibration curves,Hosmer-Lemeshow goodness-of-fit test,and the area under the receiver operating characteristic curve(AUC).Validation of the model was performed in the validation set.Results A total of 121 patients with locally recurrent HGGs were enrolled in this study,including 54 in intra-resection cavity recurrence group and 67 in extra-resection cavity recurrence group.Among them,84 were assigned into the training set and 37 into the validation set.In the training set,the radiomics score for the extra-resection cavity recurrence group was 0.424(0.278,0.573),which was higher than that for the intra-resection cavity recurrence group[-0.030(-0.226,0.248),P<0.001].In the validation set,the radiomics score for the extra-resection cavity recurrence group was 0.369(0.258,0.487),which was higher than that for the intra-resection cavity recurrence group[0.277(0.103,0.322),P=0.033].The established radiomics model exhibited good calibration and performed well in predicting spatial recurrence patterns,with an AUC value of 0.844(95%CI:0.749～0.914)in the training set and 0.706(95%CI:0.534～0.844)in the validation set.Conclusion Our multimodal radiomics model combined with intratumoral and peritumoral heterogeneity can predict the spatial pattern of locally recurrent HGGs,providing a basis for individualized treatment of HGGs.

This paper aimed to investigate the therapeutic effect and mechanism of action of the Yiqi Fumai Formula(YQFM), a kind of traditional Chinese medicine(TCM), on mice with experimental heart failure based on the "heart-gut axis" theory. Based on the network pharmacology integrated with the group collaboration algorithm, the active ingredients were screened, a "component-target-disease" network was constructed, and the potential pathways regulated by the formula were predicted and analyzed. Next, the model of experimental heart failure was established by intraperitoneal injection of adriamycin at a single high dose(15 mg·kg~(-1)) in BALB/c mice. After intraperitoneal injection of YQFM(lyophilized) at 7.90, 15.80, and 31.55 mg·d~(-1) for 7 d, the protective effects of the formula on cardiac function were evaluated using indicators such as ultrasonic electrocardiography and myocardial injury markers. Combined with inflammatory factors in the cardiac and colorectal tissue, as well as targeted assays, the relevant indicators of potential pathways were verified. Meanwhile, 16S rDNA sequencing was performed on mouse fecal samples using the Illumina platform to detect changes in gut flora and analyze differential metabolic pathways. The results show that the administration of injectable YQFM(lyophilized) for 7 d significantly increased the left ventricular end-systolic internal diameter, fractional shortening, and ejection fraction of cardiac tissue of mice with experimental heart failure(P<0.05). Moreover, markers of myocardial injury were significantly decreased(P<0.05), indicating improved cardiac function, along with significantly suppressed inflammatory responses in cardiac and intestinal tissue(P<0.05). Additionally, the species of causative organisms was decreased, and the homeostasis of gut flora was improved, involving a modulatory effect on PI3K-Akt signaling pathway-related inflammation in cardiac and colorectal tissue. In conclusion, YQFM can affect the "heart-gut axis" immunity through the homeostasis of the gut flora, thereby exerting a therapeutic effect on heart failure. This finding provides a reference for the combination of TCM and western medicine to prevent and treat heart failure based on the "heart-gut axis" theory.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Heart Failure/microbiology* ; Mice ; Mice, Inbred BALB C ; Male ; Disease Models, Animal ; Gastrointestinal Microbiome/drug effects* ; Heart/physiopathology* ; Humans ; Signal Transduction/drug effects*