ObjectiveTo investigate the mechanism of ferroptosis mediated by long-chain acyl-CoA synthetase 4 （ACSL4） signalling pathway in rats with coronary heart disease with blood stasis syndrome and the intervention effect of Xuefu Zhuyutang. MethodsSPF male SD rats were randomly divided into normal group， sham-operation group， model group， trimetazidine group （5.4 mg·kg^-1）， low-， medium-， and high-dose group （3.51， 7.02，14.04 g·kg^-1） of Xuefu Zhuyutang. The coronary artery left anterior descending ligation method was used to prepare a model of coronary heart disease with blood stasis syndrome， and continuous treatment for 7 d was conducted， while the sham-operation group was only threaded and not ligated. The general macroscopic symptoms of the rats were observed， and indicators such as electrocardiogram， echocardiography， and blood rheology were detected. The pathological morphology of myocardial tissue was observed by hematoxylin-eosin （HE） staining， and the changes in mitochondria in myocardial tissue were observed by transmission electron microscopy. The level of iron deposition in myocardial tissue was observed by Prussian blue staining. The levels of 12-hydroxyeicosatetraenoic acid （12-HETE） and 15-HETE were detected in serum by enzyme-linked immunosorbent assay. A biochemical colourimetric assay was used to detect the levels of Fe²⁺， lipid peroxidation （LPO）， glutathione （GSH）， and T-GSH/glutathione disulfide （GSSG） in myocardial tissue. DCFH-DA fluorescence quantitative assay was employed to detect the levels of reactive oxygen species （ROS）. Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was adopted to detect the protein and mRNA expressions of glutathione peroxidase 4 （GPX4）， ferritin heavy chain 1 （FTH1）， ACSL4， and ly-sophosphatidylcholine acyltransferase3 （LPCAT3） in myocardial tissue. ResultsCompared with those in the normal group， the rats in the model group were poor in general macroscopic symptoms. The electrocardiogram showed widened QRS wave amplitude and increased voltage， bow-back elevation of the ST segments， elevated T waves， J-point elevation， and accelerated heart rate. Echocardiography showed a significant reduction in left ventricular ejection fraction （LVEF） and left ventricular fraction shortening （LVFS） （P<0.01）. Blood rheology showed that the viscosity of the whole blood （low， medium， and high rate of shear） was significantly increased （P<0.01）. HE staining showed an abnormal structure of myocardial tissue. There was a large area of myocardial necrosis and inflammatory cell infiltration and a large number of connective tissue between myocardial fibers. Transmission electron microscopy showed that the mitochondria were severely atrophy or swelling. The cristae were reduced or even broken， and the matrix was flocculent or even vacuolated. Prussian blue staining showed that there were a large number of iron-containing particles， and the iron deposition was obvious. The content of 12-HETE and 15-HETE in the serum was significantly increased （P<0.01）. The content of Fe²⁺， LPO， and ROS in myocardial tissue was significantly increased （P<0.01）. The content of GSH was significantly decreased （P<0.01）， and T-GSH/GSSG was decreased （P<0.01）. The protein and mRNA expressions of GPX4 and FTH1 in myocardial tissue were both significantly decreased （P<0.05， P<0.01）， while those of ACSL4 and LPCAT3 increased significantly （P<0.01）. Compared with the model group， the general macroscopic symptoms and electrocardiogram results of rats in low-， medium- and high-dose groups of Xuefu Zhuyutang were alleviated， and the differences in LVEF/LVFS ratios were all significantly increased （P<0.05， P<0.01）. The differences in whole-blood viscosity （low， medium， and high rate of shear） were all significantly decreased （P<0.01）. The results of HE staining and transmission electron microscopy showed that the morphology， structure， and mitochondria of cardiomyocytes were improved. The content of 12-HETE and 15-HETE in serum was reduced to different degrees in low-， medium-， and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）. The content of Fe²⁺， LPO， and ROS was significantly reduced in the medium- and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）， and the content of GSH and T-GSH/GSSG was significantly increased （P<0.05， P<0.01）. The protein and mRNA expressions of GPX4 and FTH1 were significantly increased to varying degrees in the medium- and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）， and ACSL4 and LPCAT3 were decreased to different degrees in the low-， medium-， and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）. ConclusionXuefu Zhuyutang can regulate iron metabolism and anti-lipid oxidation reaction to mediate ferroptosis through the ACSL4 signalling pathway， thus exerting a protective effect on rats with coronary heart disease with blood stasis syndrome.

ObjectiveTo investigate the influence of histone deacetylase 3 (HDAC3) on the occurrence, development of psoriasis-like inflammation in mice, and the relative immune mechanisms. MethodsHealthy C57BL/6 mice aged 6-8 weeks were selected and randomly divided into 3 groups: control group (Control), psoriasis model group (IMQ), and HDAC3 inhibitor RGFP966-treated psoriasis model group (IMQ+RGFP966). One day prior to the experiment, the back hair of the mice was shaved. After a one-day stabilization period, the mice in Control group was treated with an equal amount of vaseline, while the mice in IMQ group was treated with imiquimod (62.5 mg/d) applied topically on the back to establish a psoriasis-like inflammation model. The mice in IMQ+RGFP966 group received intervention with a high dose of the HDAC3-selective inhibitor RGFP966 (30 mg/kg) based on the psoriasis-like model. All groups were treated continuously for 5 d, during which psoriasis-like inflammation symptoms (scaling, erythema, skin thickness), body weight, and mental status were observed and recorded, with photographs taken for documentation. After euthanasia, hematoxylin-eosin (HE) staining was used to assess the effect of RGFP966 on the skin tissue structure of the mice, and skin thickness was measured. The mRNA and protein expression levels of HDAC3 in skin tissues were detected using reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot (WB), respectively. Flow cytometry was employed to analyze neutrophils in peripheral blood and lymph nodes, CD4⁺ T lymphocytes, CD8⁺ T lymphocytes in peripheral blood, and IL-17A secretion by peripheral blood CD4⁺ T lymphocytes. Additionally, spleen CD4⁺ T lymphocyte expression of HDAC3, CCR6, CCR8, and IL-17A secretion levels were analyzed. Immunohistochemistry was used to detect the localization and expression levels of HDAC3, IL-17A, and IL-10 in skin tissues. ResultsCompared with the Control group, the IMQ group exhibited significant psoriasis-like inflammation, characterized by erythema, scaling, and skin wrinkling. Compared with the IMQ group, RGFP966 exacerbated psoriasis-like inflammatory symptoms, leading to increased hyperkeratosis. The psoriasis area and severity index (PASI) skin symptom scores were higher in the IMQ group than those in the Control group, and the scores were further elevated in the IMQ+RGFP966 group compared to the IMQ group. Skin thickness measurements showed a trend of IMQ+RGFP966>IMQ>Control. The numbers of neutrophils in the blood and lymph nodes increased sequentially in the Control, IMQ, and IMQ+RGFP966 groups, with a similar trend observed for CD4⁺ and CD8⁺ T lymphocytes in the blood. In skin tissues, compared with the Control group, the mRNA and protein levels of HDAC3 decreased in the IMQ group, but RGFP966 did not further reduce these expressions. HDAC3 was primarily located in the nucleus. Compared with the Control group, the nuclear HDAC3 content decreased in the skin tissues of the IMQ group, and RGFP966 further reduced nuclear HDAC3. Compared with the Control and IMQ groups, RGFP966 treatment decreased HDAC3 expression in splenic CD4⁺ and CD8⁺ T cells. RGFP966 treatment increased the expression of CCR6 and CCR8 in splenic CD4⁺ T cells and enhanced IL-17A secretion by peripheral blood and splenic CD4⁺ T lymphocytes. Additionally, compared with the IMQ group, RGFP966 reduced IL-10 protein levels and upregulated IL-17A expression in skin tissues. ConclusionRGFP966 exacerbates psoriatic-like inflammatory responses by inhibiting HDAC3, increasing the secretion of the cytokine IL-17A, and upregulating the expression of chemokines CCR8 and CCR6.

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 explore the bidirectional associations among body mass index Z scores (BMI Z scores) and weight stigma with depressive symptoms in adolescents, thereby providing evidence for targeted intervention strategies. Methods: A stratified cluster random sampling method was employed to select 18 301 adolescents aged 12-18 years from all 12 prefectures (103 counties) in the Inner Mongolia Autonomous Region, and two waves of longitudinal surveys were conducted in September 2023 (T1) and September 2024 (T2) among the adolescents. Weight stigma was assessed by using a self developed questionnaire, depressive symptom was measured with the Center for Epidemiologic Studies Depression Scale (CES-D), and BMI Z scores were calculated according to the World Health Organization standards. Pearson correlation analysis was used to examine associations among variables, and cross lagged panel models were constructed to investigate the dynamic bidirectional relationships among the three variables. Results: Adolescents BMI Z scores and weight stigma with depressive symptoms all exhibited autoregressive stability across the two time points (autoregressive paths, all P <0.01). Cross lagged model comparisons indicated that the bidirectional path model achieved the best fit ( χ 2=12.65, RMSEA =0.017, CFI =1.000; △ χ 2=193.39, P <0.01), supporting dynamic bidirectional associations among the three variables. After adjusting for gender, age, subjective social status and only child status, T1 BMI Z scores among adolescents positively predicted T2 weight stigma ( β =0.061), and T1 weight stigma positively predicted T2 depressive symptoms ( β =0.608); in the reverse direction, T1 depressive symptoms predicted T2 weight stigma ( β =0.003), and T1 weight stigma predicted T2 BMI Z scores ( β =0.081) (all P <0.01). Conclusions There is a bidirectional cross lagged relationship among adolescents BMI Z scores and weight stigma with depressive symptoms, suggesting that weight stigma may serve as a key psychological variable linking obesity and depressive symptoms. Greater attention should be paid to the potential threat of weight stigma to adolescents mental health, with intervention strategies expanded from a solely physiological focus to encompass psychosocial dimensions.

The effect of Huanglian Jiedu Decoction on the intestinal flora of type 2 diabetes mellitus(T2DM) was investigated using 16S rRNA sequencing technology. Sixty rats were randomly divided into a normal group(10 rats) and a modeling group(50 rats). After one week of adaptive feeding, a high-fat diet + streptozotocin was given for modeling, and fasting blood glucose >16.7 mmol·L~(-1) was considered a sign of successful modeling. The modeling group was randomly divided into the model group, high-, medium-, and low-dose groups of Huanglian Jiedu Decoction, and metformin group. After seven days of intragastric treatment, the feces, colon, and pancreatic tissue of each group of rats were collected, and the pathological changes of the colon and pancreatic tissue of each group were observed by hematoxylin-eosin staining. The changes in the intestinal flora structure of each group were observed by the 16S rRNA sequencing method. The results showed that compared with the model group, the high-, medium-, and low-dose of Huanglian Jiedu Decoction reduced fasting blood glucose levels to different degrees and showed no significant changes in body weight. The number of islet cells increased, and intestinal mucosal damage attenuated. Alpha diversity analysis revealed that Huanglian Jiedu Decoction reduced the abundance and diversity of intestinal flora in rats with T2DM; at the phylum level, low-and mediam-dose of Huanglian Jiedu Decoction reduced the abundance of Bacteroidota, Proteobacteria, and Desulfobacterota and increased the abundance of Firmicute and Bacteroidota/Firmicutes, while the high-dose of Huanglian Jiedu Decoction increased the relative abundance of Proteobacteria and Bacteroidota/Firmicutes ratio, and decreaseal the relative; abundance of Firmicute; at the genus level, Huanglian Jiedu Decoction increased the relative abundance of Allobaculum, Blautia, and Lactobacillus; LEfse analysis revealed that the biomarker of low-and medium-dose groups of Huanglian Jiedu Decoction was Lactobacillus, and the structure of the intestinal flora of the low-dose group of Huanglian Jiedu Decoction was highly similar to that of the metformin group. PICRUSt2 function prediction revealed that Huanglian Jiedu Decoction mainly affected carbohydrate and amino acid metabolic pathways. It suggested that Huanglian Jiedu Decoction could reduce fasting blood glucose and increase the number of islet cells in rats with T2DM, and its mechanism of action may be related to increasing the abundance of short-chain fatty acid-producing strains and Lactobacillus and affecting carbohydrate and amino acid metabolic pathways.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Rats, Sprague-Dawley ; Humans ; Bacteria/drug effects* ; Blood Glucose/metabolism*

This study aims to explore the effects of Buzhong Yiqi Decoction(BYD) on the cyclic guanosine monophosphate-adenosine monophosphate synthase(cGAS)-stimulator of interferon genes(STING) signaling pathway in the mouse model of autoimmune thyroiditis(AIT) and the mechanism of BYD in alleviating the immune injury. Forty-eight NOD.H-2~(h4) mice were assigned into normal, model, low-, medium-, and high-dose BYD, and selenium yeast tablets groups(n=8). Mice of 8 weeks old were treated with 0.05% sodium iodide solution for 8 weeks for the modeling of AIT and then administrated with corresponding drugs by gavage for 8 weeks before sampling. High performance liquid chromatography was employed to measure the astragaloside Ⅳ content in BYD. Hematoxylin-eosin staining was employed to observe the pathological changes in the mouse thyroid tissue. Enzyme-linked immunosorbent assay was employed to measure the serum levels of thyroid peroxidase antibody(TPO-Ab), thyroglobulin antibody(TgAb), and interferon-γ(IFN-γ). Flow cytometry was employed to detect the distribution of T cell subsets in the spleen. The immunohistochemical method was used to detect the expression of cGAS, STING, TANK-binding kinase 1(TBK1), and interferon regulatory factor 3(IRF3). Real-time PCR and Western blot were employed to determine the mRNA and protein levels, respectively, of markers related to the cGAS-STING signaling pathway in the thyroid tissue. The results showed that the content of astragaloside Ⅳ in BYD was(7.06±0.08) mg·mL~(-1). Compared with the normal group, the model group showed disrupted structures of thyroid follicular epithelial cells, massive infiltration of lymphocytes, and elevated levels of TgAb and TPO-Ab. Compared with the model group, the four treatment groups showed intact epithelial cells, reduced lymphocyte infiltration, and lowered levels of TgAb and TPO-Ab. Compared with the normal group, the model group showed increases in the proportions of Th1 and Th17 cells, a decrease in the proportion of Th2 cells, and an increase in the IFN-γ level. Compared with the model group, the four treatment groups presented decreased proportions of Th1 and Th17 cells and lowered levels of IFN-γ, and the medium-dose BYD group showed an increase in the proportion of Th2 cells. Compared with the normal group, the modeling up-regulated the mRNA levels of cGAS, STING, TBK1, and IRF3 and the protein levels of cGAS, p-STING, p-TBK1, and p-IRF3. Compared with the model group, the four treatment groups showed reduced levels of cGAS, STING, TBK1, and IRF3-positive products, down-regulated mRNA levels of cGAS, STING, and TBK1, and down-regulated protein levels of cGAS and p-STING. The high-dose BYD group showed down-regulations in the mRNA level of IRF3 and the protein levels of p-TBK1 and p-IRF3. The above results indicate that BYD can repair the imbalance of T cell subsets, alleviate immune injury, and reduce thyroid lymphocyte infiltration in AIT mice by inhibiting the cGAS-STING signaling pathway.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Signal Transduction/drug effects* ; Thyroiditis, Autoimmune/metabolism* ; Mice ; Membrane Proteins/metabolism* ; Mice, Inbred NOD ; Humans ; Female ; Nucleotidyltransferases/metabolism* ; Male ; Disease Models, Animal