ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

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.

This study employed the "disease-medicine-dose" pattern to mine the medication rules of traditional Chinese medicine(TCM) prescriptions containing Astragali Radix in ancient Chinese medical books, aiming to provide a scientific basis for the clinical application of Astragali Radix and the development of new medicines. The TCM prescriptions containing Astragali Radix were retrieved from databases such as Chinese Medical Dictionary and imported into Excel 2020 to construct the prescription library. Statical analysis were performed for the prescriptions regarding the indications, syndromes, medicine use frequency, herb effects, nature and taste, meridian tropism, dosage forms, and dose. SPSS statistics 26.0 and IBM SPSS Modeler 18.0 were used for association rules analysis and cluster analysis. A total of 2 297 prescriptions containing Astragali Radix were collected, involving 233 indications, among which sore and ulcer, consumptive disease, sweating disorder, and apoplexy had high frequency(>25), and their syndromes were mainly Qi and blood deficiency, Qi and blood deficiency, Yin and Yang deficiency, and Qi deficiency and collateral obstruction, respectively. In the prescriptions, 98 medicines were used with the frequency >25 and they mainly included Qi-tonifying medicines and blood-tonifying medicines. Glycyrrhizae Radix et Rhizoma, Angelicae Sinensis Radix, Ginseng Radix et Rhizoma, Atractylodis Macrocephalae Rhizoma, and Citri Reticulatae Pericarpium were frequently used. The medicines with high frequency mainly have warm or cold nature, and sweet, pungent, or bitter taste, with tropism to spleen, lung, heart, liver, and kidney meridians. In the treatment of sore and ulcer, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to promote granulation and heal up sores. In the treatment of consumptive disease, Astragali Radix was mainly used with the dose of 37.30 g and combined with Ginseng Radix et Rhizoma to tonify deficiency and replenish Qi. In the treatment of sweating disorder, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to consolidate exterior and stop sweating. In the treatment of apoplexy, Astragali Radix was mainly used with the dose of 7.46 g and combined with Glycyrrhizae Radix et Rhizoma to dispell wind and stop convulsions. Astragali Radix can be used in the treatment of multiple system diseases, with the effects of tonifying Qi and ascending Yang, consolidating exterior and stopping sweating, and expressing toxin and promoting granulation. According to the manifestations of different diseases, when combined with other medicines, Astragali Radix was endowed with the effects of promoting granulation and healing up sores, tonifying deficiency and Qi, consolidating exterior and stopping sweating, and dispelling wind and replenishing Qi. The findings provide a theoretical reference and a scientific basis for the clinical application of Astragali Radix and the development of new medicines.
Drugs, Chinese Herbal/history* ; Humans ; Medicine, Chinese Traditional/history* ; History, Ancient ; Astragalus Plant/chemistry* ; China ; Astragalus propinquus

This study explores the effect and mechanism of Banxia Xiexin Decoction(BXD) in inhibiting colon cancer progression by reshaping tryptophan metabolism. Balb/c mice were assigned into control, model, low-dose BXD(BXD-L), and high-dose BXD(BXD-H) groups. Except the control group, the other groups were subcutaneously injected with CT26-Luc cells for the modeling of colon cancer, which was followed by the intervention with BXD. Small animal live imaging was employed to monitor tumor growth, and the tumor volume and weight were measured. Hematoxylin-eosin(HE) staining was used to observe the pathological changes in mouse tumors. Immunohistochemistry was used to detect Ki67 expression in tumors. Immunofluorescence and flow cytometry were used to detect the infiltration and number changes of CD3~+/CD8~+ T cells in the tumor tissue. Enzyme-linked immunosorbent assay(ELISA) was employed to measure the levels of interferon-gamma(IFN-γ) and interleukin-2(IL-2) in tumors. Targeted metabolomics was employed to measure the level of tryptophan(Trp) in the serum, and the Trp content in the tumor tissue was measured. Western blot and RT-qPCR were employed to determine the protein and mRNA levels, respectively, of indoleamine 2,3-dioxygenase 1(IDO1), MYC proto-oncogene, and solute carrier family 7 member 5(SLC7A5) in the tumor tissue. Additionally, a co-culture model with CT26 cells and CD8~+ T cells was established in vitro and treated with the BXD-containing serum. The cell counting kit-8(CCK-8) assay was used to examine the viability of CT26 cells. The content of Trp in CT26 cells and CD8~+ T cells, as well as the secretion of IFN-γ and IL-2 by CD8~+ T cells, was measured. RT-qPCR was used to determine the mRNA levels of MYC and SLC7A5 in CT26 cells. The results showed that BXD significantly inhibited the tumor growth, reduced the tumor weight, and decreased the tumor volume in the model mice. In addition, the model mice showed sparse arrangement of tumor cells, varying degrees of patchy necrosis, and downregulated expression of Ki67 in the tumor tissue. BXD elevated the levels of IFN-γ and IL-2 in the tumor tissue, while upregulating the ratio of CD3~+/CD8~+ T cells and lowering the levels of Trp, IDO1, MYC, and SLC7A5. The co-culture experiment showed that BXD-containing serum reduced Trp uptake by CT26 cells, increased Trp content in CD8~+T cells, enhanced IL-2 and IFN-γ secretion of CD8~+T cells, and down-regulated the mRNA levels of MYC and SLC7A5 in CT26 cells. In summary, BXD can inhibit the MYC/SLC7A5 pathway to reshape Trp metabolism and adjust Trp uptake by CD8~+ T cells to enhance the cytotoxicity, thereby inhibiting the development of colon cancer.
Animals ; Tryptophan/metabolism* ; Colonic Neoplasms/pathology* ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Mice, Inbred BALB C ; Humans ; Cell Line, Tumor ; Indoleamine-Pyrrole 2,3,-Dioxygenase/metabolism* ; Female ; Disease Progression ; Cell Proliferation/drug effects* ; Proto-Oncogene Mas ; Male