ObjectiveTo study the correlations between traditional Chinese medicine （TCM） syndromes and lipid metabolism in children with Wilson disease （WD）. MethodsClinical data and lipid metabolism indicators ［total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， apolipoprotein A1 （ApoA1）， apolipoprotein B （ApoB）， and lipoprotein a （Lpa）］ were retrospectively collected from 341 children with WD. The clinical data were compared among WD children with different syndromes， and the correlations between TCM syndromes and lipid metabolism in children with WD were analyzed. Least absolute shrinkage and selection operator （LASSO） regression was used for variable screening， and unordered multinomial Logistic regression was employed to analyze the effects of lipid metabolism indicators on TCM syndromes. ResultsThe 341 children with WD included 121 （35.5%） children with the dampness-heat accumulation syndrome， 103 （30.2%） children with the liver-kidney Yin deficiency syndrome， 68 children with the combined phlegm and stasis syndrome， 29 children with the spleen-kidney Yang deficiency syndrome， and 20 children with the liver qi stagnation syndrome. The liver-kidney Yin deficiency syndrome， combined phlegm and stasis syndrome， and spleen-kidney Yang deficiency syndrome had correlations with the levels of lipid metabolism indicators （P<0.05）. Lipid metabolism abnormalities occurred in 232 （68.0%） children， including hypertriglyceridemia （108）， hypercholesterolemia （23）， mixed hyperlipidemia （67）， lipoprotein a-hyperlipoproteinemia （12）， and hypo-HDL-cholesterolemia （22）. The percentages of hypertriglyceridemia and hypo-HDL-cholesterolemia varied among children with different TCM syndromes （P<0.05）. Correlations existed for the liver-kidney Yin deficiency syndrome with TG， TC， and HDL-C， the combined phlegm and stasis syndrome with TG， the spleen-kidney Yang deficiency syndrome with TG， TC， and LDL-C， and the liver Qi stagnation syndrome with TC and LDL-C （P<0.05， P<0.01）. ConclusionThe TCM syndromes of children with WD are dominated by the dampness-heat accumulation syndrome and the liver-kidney Yin deficiency syndrome， and dyslipidemia in the children with WD is dominated by hypertriglyceridemia and mixed hyperlipidemia. There are different correlations between TCM syndromes and lipid metabolism indicators， among which TG， TC， LDL-C， and HDL-C could assist in identifying TCM syndromes in children with WD.

Objective: To explore the distribution of traditional Chinese medicine (TCM) syndromes in children with hepatolenticular degeneration (Wilson disease, WD) based on factor analysis and cluster analysis. Methods: From November 2018 to November 2023, general information (gender, age of admission, age of onset, course of disease, clinical staging, Western medicine clinical symptoms, and family history) and TCM four-examination informations (symptoms and signs) were retrospectively collected from 757 cases of children with WD at the First Affiliated Hospital of Anhui University of Chinese Medicine, and factor analysis and cluster analysis were used to investigate TCM syndromes in children with WD. Results: A total of 757 children with WD were included, of which 483 were male and 274 were female; the median age at admission was 12.58 years, the median age at onset was 8.33 years, and the median course of disease was 24.37 months; clinical typing result indicated 506 cases of hepatic type, 133 cases of brain type, 99 cases of mixed-type, and 19 cases of other type; 36.46% of the children had no clinical symptoms (elevated aminotransferases or abnormalities in copper biochemistry); a total of 177 cases had a definite family history, and 10 cases had a suspected family history. Forty-three TCM four-examination information were obtained, with the top 10 in descending order being feeling listless and weak, brown urine, slow action, inappetence, dim complexion, slurred speech, angular salivation, body weight loss, hand and foot tremors, and abdominal fullness. In children with WD, the syndrome element of disease location was primarily characterized by the liver, involving the spleen and kidney, and the syndrome elements of disease nature were characterized by dampness, heat, and yin deficiency. Based on factor analysis and cluster analysis, five TCM syndromes were derived, which were, in order, syndrome of dampness-heat accumulation (265 cases, 35.01%), syndrome of yin deficiency of the liver and kidney (202 cases, 26.68%), syndrome of liver hyperactivity with spleen deficiency (185 cases, 24.44%), syndrome of qi and blood deficiency (79 cases, 10.44%), and syndrome of yang deficiency of the spleen and kidney (26 cases, 3.43%). Conclusion The TCM syndromes of children with WD were primarily syndromes of dampness-heat accumulation, yin deficiency of the liver and kidney, and liver hyperactivity with spleen deficiency. The liver was the main disease location, and the disease nature was characterized by deficiency in origin and excess in superficiality, excess and deficiency mixed. These findings suggest that treating children with WD should be based on the liver while also considering the spleen and kidney.

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.

Aim To study the effect of menthol on hypobaric hypoxia-induced pulmonary arterial hypertension and explore the underlying mechanism in mice. Methods 10 to 12 weeks old wild type (WT) mice and TRPM8 gene knockout (TRPM8

Purpose To investigate the effect of MYD88 gene overexpression on the proliferation and apoptosis of human diffuse large B cell lymphoma(DLBCL)cells,and to prelimi-narily explore the mechanism of MYD88 gene action.Methods PEGFP-C2-MYD88 overexpressing MYD88 L265P gene was transfected into DLBCL cells by plasmid transfection.The exper-iment was divided into blank control group,negative control group and MYD88 L265P overexpression group.The fluores-cence expression of MYD88 L265P after overexpression was ob-served under inverted fluorescence microscope.RT-PCR and Western blot were used to detect the mRNA and protein expres-sion of MYD88 L265P,IRAK4,NF-κB and BCL2 in DLBCL cells before and after overexpression of MYD88 L265.CCK8 method was used to detect DLBCL cells proliferation and Ho-echst staining was used to detect DLBCL cells apoptosis.Re-sults After overexpression of MYD88 L265P,compared with the blank control group(0.670 4±0.017 5)and the negative control group(0.715 3±0.019 6),the MYD88L265P overex-pression group(1.157 2±0.010 2)increased significantly,with statistical significance(all P＜0.05).After overexpression of MYD88 L265P,compared with the blank control group(0.69 ±0.04)and the negative control group(0.81±0.07),the MYD88L265P overexpression group(0.48±0.05)was signifi-cantly decreased,with statistical significance(all P＜0.05).After overexpression of MYD88 L265P,compared with the blank control group(mRNA:1.0158±0.0115,0.987 3±0.010 2,1.007 6±0.015 3,protein:0.183 4±0.058 9,0.096 8± 0.015 7,0.147 5±0.0418)and negative control group(mR-NA:0.9132±0.0098,1.0032±0.0156,0.9327± 0.011 2,protein:0.187 9±0.042 3,0.088 9±0.0513,0.134 8±0.050 1),the mRNA(3.243 2±0.013 6,2.976 6 ±0.0213,1.585 9±0.019 8)and protein expressions(0.452 7±0.052 4,0.218 9±0.047 5,0.301 4±0.059 8)of IRAK4,NF-κB and anti-apoptosis protein BCL2 in MYD88L265P overexpression group were significantly increased,which was statistically significant(all P＜0.05).Conclusion After overexpression of MYD88 L265P,the apoptosis rate of DLBCL cells decreased and the cell proliferation rate increased.The mechanism may be related to the mutation of MYD88 L265P gene,activation and amplification of NF-κB pathway,and pro-motion of the overexpression of antiapoptotic protein BCL2.

Objective:To evaluate the effect of Salvianolic acid B (Sal B) on the inflammatory responses of vascular smooth muscle cells (VSMCs) in septic mice and the role of circACTA2.Methods:In vivo experiment Eighty-one healthy male C57BL/6 mice, aged 6-8 weeks, were divided into 3 groups ( n=27 each) by a random number table method: sham operation group, sepsis group and Sal B group. Sepsis model was developed by cecal ligation and puncture. After sucessful preparation of the model, Sal B 7 mg/kg/d was intraperitoneally injected once a day for 2 consecutive days in Sal B group. Twenty mice in each group were randomly selected to measure systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and whole blood lactic acid (Lac) and to record the survival within 7 days after developing the model. Seven mice in each group were randomly selected at 48 h after developing the model, and the arterial vascular tissues were collected for determination of the expression of interleukin-1beta (IL-1β) (by immunofluorescence staining), expression of IL-1β, tumor necrosis factor-alpha (TNF-α) and IL-6 protein and mRNA (by Western blot and quantitative real-time polymerase chain reaction, respectively), and expression of circACTA2 (by quantitative real-time polymerase chain reaction). Cell experiment Mouse VSMCs were cultured and divided into 6 groups ( n=3 each) by a random number table method: control group (C group), lipopolysaccharide (LPS) group, Sal B group, si-circACTA2+ C group, si-circACTA2+ LPS group, and si-circACTA2+ Sal B group. The cells were incubated for 24 h with LPS (final concentration 1 μg/ml) in LPS group and with LPS (final concentration 1 μg/ml) and Sal B (final concentration 5 μmol/L) in Sal B group. VSMCs were transfected with si-circACTA2 only in si-circACTA2+ C group. At 24 h after transfection of si-circACTA2 into VSMCs, the cells were incubated with LPS (final concentration 1 μg/ml) in si-circACTA2+ LPS group and with LPS (final concentration 1 μg/ml) and Sal B (final concentration 5 μmol/L) for 24 h in si-circACTA2+ Sal B group. The expression of IL-1β, TNF-α and IL-6 protein and mRNA was detected using Western blot and quantitative real-time polymerase chain reaction, and the expression of circACTA2 was determined by the quantitative real-time polymerase chain reaction. Results:In vivo experiment Compared with sham operation group, SBP, DBP and MAP were significantly decreased, the concentrations of whole blood Lac were increased, 7-day survival rate was decreased, the expression of IL-1β, TNF-α and IL-6 protein and mRNA in arterial vascular tissues was up-regulated, circACTA2 expression was down-regulated ( P<0.05), and the fluorescence of IL-1β was enhanced in sepsis group. Compared with sepsis group, SBP, DBP and MAP were significantly increased, whole blood Lac concentrations were decreased, 7-day survival rate was increased, the expression of IL-1β, TNF-α and IL-6 protein and mRNA in arterial vascular tissues was down-regulated, the expression of circACTA2 was up-regulated ( P<0.05), and the fluorescence of IL-1β was weakened in Sal B group. Cell experiment Compared with group C, the expression of IL-1β, TNF-α and IL-6 protein and mRNA was significantly up-regulated, and the expression of circACTA2 was down-regulated in LPS group ( P<0.05). Compared with LPS group, the expression of IL-1β, TNF-α and IL-6 protein and mRNA was significantly down-regulated, and the expression of circACTA2 was up-regulated in Sal B group ( P<0.05). Compared with si-circACTA2+ C group, the expression of IL-1β, TNF-α and IL-6 protein and mRNA was significantly up-regulated in si-circACTA2+ LPS group ( P<0.05). There were no significant differences in the expression of IL-1β, TNF-α and IL-6 protein and mRNA between si-circACTA2+ LPS group and si-circACTA2+ Sal B group ( P>0.05). Conclusions:Sal B can reduce the inflammatory responses of VSMCs, and the mechanism may be related to promoting the expression of circACTA2 in septic mice.