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.

AIM: To evaluate the surgical outcomes and changes in the ocular surface microenvironment following radiofrequency minimally invasive treatment for conjunctivochalasis-induced epiphora.METHODS: Patients with epiphora primarily caused by conjunctivochalasis were enrolled. All patients had conjunctivochalasis of ≥grade II, and their symptoms showed no significant improvement after previous pharmacological treatment. All patients underwent radiofrequency minimally invasive correction of conjunctivochalasis, supplemented with artificial tears, anti-inflammatory therapy, and ocular surface repair treatment postoperatively. At 8 wk post-surgery, the ocular surface disease index(OSDI), eye redness, tear secretion, non-invasive tear break-up time, lipid layer thickness, tear ferning test, and conjunctival impression cytology were assessed to compare treatment efficacy and observe changes in the ocular surface microenvironment.RESULTS: A total of 43 cases(43 eyes)of conjunctivochalasis and with a main complaint of epiphora were included, including 23 males and 20 males, with a mean age of 64.69±3.36 years. The total effective rate of surgery was 91% at 8 wk postoperatively. Compared with preoperative values, the OSDI scores significantly decreased and the non-invasive tear break-up time was prolonged at 8 wk post-surgery(all P<0.05). No statistically significant differences were observed in lipid layer thickness or tear secretion at 8 wk postoperatively(all P>0.05). The normal rate of chloramphenicol taste test increased from 21% preoperatively to 63% postoperatively; the normal rate of eye redness increased from 40% to 70%; normal rate of tear ferning grading improved from 30% to 63%; and normal conjunctival impression cytology grading increased from 21% to 74%.CONCLUSION: Radiofrequency minimally invasive treatment is effective for conjunctivochalasis and is straightforward to perform. Patients with conjunctivochalasis often present with other ocular surface issues beyond conjunctivochalasis itself, such as insufficient tear secretion, reduced lipid layer thickness, and other dry eye-related problems. Therefore, a comprehensive approach emphasizing tear dynamics should be adopted during treatment.

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 clarify the risk factors of diaphragmatic dysfunction (DD) after cardiac surgery with extracorporeal circulation. Methods A retrospective analysis was conducted on the data of patients who underwent cardiac surgery with extracorporeal circulation in the Department of Cardiovascular Surgery of Peking University People's Hospital from January 2023 to March 2024. Patients were divided into two groups according to the results of bedside diaphragm ultrasound: a DD group and a control group. The preoperative, intraoperative, and postoperative indicators of the patients were compared and analyzed, and independent risk factors for DD were screened using multivariate logistic regression analysis. Results A total of 281 patients were included, with 32 patients in the DD group, including 23 males and 9 females, with an average age of (64.0±13.5) years. There were 249 patients in the control group, including 189 males and 60 females, with an average age of (58.0±11.2) years. The body mass index of the DD group was lower than that of the control group [(18.4±1.5) kg/m2 vs. (21.9±1.8) kg/m2, P=0.004], and the prevalence of hypertension, chronic obstructive pulmonary disease, heart failure, and renal insufficiency was higher in the DD group (P<0.05). There was no statistical difference in intraoperative indicators (operation method, extracorporeal circulation time, aortic clamping time, and intraoperative nasopharyngeal temperature) between the two groups (P>0.05). In terms of postoperative aspects, the peak postoperative blood glucose in the DD group was significantly higher than that in the control group (P=0.001), and the proportion of patients requiring continuous renal replacement therapy was significantly higher than that in the control group (P=0.001). The postoperative reintubation rate, tracheotomy rate, mechanical ventilation time, and intensive care unit stay time in the DD group were higher or longer than those in the control group (P<0.05). Multivariate logistic regression analysis showed that low body mass index [OR=0.72, 95%CI (0.41, 0.88), P=0.011], preoperative dialysis [OR=2.51, 95%CI (1.89, 4.14), P=0.027], low left ventricular ejection fraction [OR=0.88, 95%CI (0.71, 0.93), P=0.046], and postoperative hyperglycemia [OR=3.27, 95%CI (2.58, 5.32), P=0.009] were independent risk factors for DD. Conclusion The incidence of DD is relatively high after cardiac surgery, and low body mass index, preoperative renal insufficiency requiring dialysis, low left ventricular ejection fraction, and postoperative hyperglycemia are risk factors for DD.