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: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

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.

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics.Similarities and variations of components present significant analytical challenges.A two-dimensional(2D)liquid chromatography-mass spectrometry(LC-MS)method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium(CMS).A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated.For efficient and high-accuracy screening of CMS,a targeted method based on a self-constructed high resolution(HR)mass spectrum database of CMS components was established.The database was built based on the commercial MassHunter Personal Compound Database and Library(PCDL)software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening.On this basis,the unknown peaks in the CMS chromatograms were deduced and assigned.The molecular formula,group composition,and origins of a total of 99 compounds,of which the combined area percentage accounted for more than 95％of CMS components,were deduced by this 2D-LC-MS method combined with the MassHunter PCDL.This profiling method was highly efficient and could distinguish hundreds of components within 3 h,providing reliable results for quality control of this kind of complex drugs.

Integrated metabolomic and lipidomic profiling,utilizing liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS),has emerged as a pivotal strategy for biomarker discovery.However,the inherent polarity disparity between metabolites and lipids complicates simultaneous analysis.To address this,a dual-stationary phase polarity-extended liquid chromatography(PELC)system was developed,which surpassed conventional one-dimensional LC(1D-LC)by enabling comprehensive coverage of both polar and non-polar compounds within a single injection.This system enhanced chromatographic resolution,peak capacity,and throughput while minimizing analytical variability.Extracellular vesicles(EVs),lipid bilayer-enclosed nanoparticles ubiquitously present in biofluids,had gained prominence as reservoirs of cancer biomarkers due to their cargo stability and pathophysiological relevance.Herein,the application of PELC-HRMS for concurrent metabolome-lipidome profiling in EVs was pioneered.A total of 193 metabolites were identified using this technique coupled with MS-DIAL software and Human Metabolome Database.Subsequently,this technique was employed to explore potential biomarkers for prostate cancer(PCa).Multivariate analysis identified 17 differentially abundant metabolites in PCa,implicating dysregulated pathways including purine metabolism,starch and sucrose metabolism,galactose metabolism,cysteine and methionine metabolism,and biosynthesis of unsaturated fatty acids.Notably,creatine(AUC=0.92)and DG 42:5(AUC=0.80)demonstrated robust diagnostic efficacy,attributable to their broad polarity ranges and EV-specific enrichment.This study established PELC as a high-fidelity platform for multi-omics integration in complex biospecimens,advancing mechanistic insights into metabolic rewiring and disease pathophysiology.