ObjectiveTo investigate the ameliorative effect of Wendantang combined with Danshenyin and Dushentang （WDD） on mice with ischemic heart disease （IHD） presenting phlegm-stasis syndrome based on the inflammatory phenotype and differentiation of circulating monocytes. MethodsA model of IHD with phlegm-stasis syndrome was established using left anterior descending coronary artery ligation supplemented with a high-fat diet. Eighty model mice were randomly assigned to the model group， WDD low-dose group （WDD-L）， WDD medium-dose group （WDD-M）， WDD high-dose group （WDD-H）， and atorvastatin calcium tablet group， with 16 mice in each group. An additional 16 C57BL/6J mice were designated as the sham-operation group. The WDD groups received intragastric administration at doses of 8.91， 17.81， 35.62 g·kg^-1， and the atorvastatin calcium tablet group received the corresponding drug at 1.3 mg·kg^-1， twice daily. The sham-operation and model groups were given the same volume of pure water by gavage each day. After 5 consecutive weeks of administration， the cardiac index was calculated. Cardiac function was assessed by echocardiography. Myocardial histopathology was examined by hematoxylin-eosin （HE） staining. Serum N-terminal pro-B-type natriuretic peptide （pro-BNP） content was measured by enzyme-linked immunosorbent assay （ELISA）. Hemorheological parameters were analyzed using an automated hemorheology analyzer. Serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein （LDL）， and high-density lipoprotein （HDL） were determined using an automated biochemical analyzer. Changes in circulating monocytes were detected by flow cytometry. Mouse bone marrow mononuclear cells were isolated in vitro and divided into blank group， model serum group， WDD-L drug-containing serum group， WDD-M drug-containing serum group， and WDD-H drug-containing serum group. CD36 expression and macrophage differentiation in each group were assessed by flow cytometry. The mechanism by which WDD mediates circulating monocyte differentiation was further explored using CD36 knockdown/overexpression RAW264.7 cell lines. ResultsCompared with the sham-operation group， the model group showed a significantly increased cardiac index （P0.01）， significantly decreased fractional shortening （FS） （P0.01）， and significantly increased left ventricular end-diastolic internal diameter （LVDD） and left ventricular end-systolic internal diameter （LVDS） （P0.01）. Cardiomyocytes exhibited marked deformation and necrosis with inflammatory cell infiltration. Serum pro-BNP levels were significantly elevated （P0.01）， and whole-blood viscosity （BV） at high， medium， and low shear rates was significantly increased （P0.01）. Compared with the model group， the WDD groups showed significantly reduced cardiac index （P0.05， P0.01）， significantly increased FS （P0.05， P0.01）， significantly decreased LVDD and LVDS （P0.01）， markedly improved cardiomyocyte morphology， significantly reduced inflammatory infiltration， significantly decreased serum pro-BNP levels （P0.01）， and significantly decreased BV at high， medium， and low shear rates （P0.01）， with the most pronounced improvement observed in the WDD-M group. Compared with the sham-operation group， TC， TG， and LDL levels were significantly increased in the model group （P0.05， P0.01）， while HDL levels were significantly decreased （P0.05）. After WDD-H treatment， TC， TG， and LDL levels were significantly reduced and HDL levels were significantly increased in mice （P0.05， P0.01）. Compared with the sham-operation group， classical monocytes in blood and bone marrow and intermediate monocytes in blood were significantly increased in the model group （P0.01）， whereas intermediate monocytes in bone marrow and non-classical monocytes in blood were significantly decreased （P0.01）. After WDD administration， all circulating monocyte subsets in blood and bone marrow were significantly alleviated （P0.05， P0.01）， with the WDD-M group showing the optimal effect. In vitro， compared with the blank group， CD36 expression on bone marrow monocytes and the proportion of differentiated macrophages were significantly increased in the model serum group （P0.01）， and CD36 expression was significantly upregulated on RAW264.7 cells （P0.01）. Compared with the model serum group， all drug-containing serum groups exhibited significantly reduced CD36 expression on bone marrow monocytes and significantly reduced macrophage differentiation （P0.01）. WDD downregulated CD36 expression in both CD36 knockdown and overexpression RAW264.7 cell lines （P0.05， P0.01）， with the strongest regulatory effect observed in the WDD-M drug-containing serum group. ConclusionWDD can significantly improve the manifestations of phlegm-stasis syndrome in IHD mice and reduce the proportion of classical circulating monocytes. Its mechanism may be related to the inhibition of CD36 expression on classical circulating monocytes.

ObjectiveTo investigate the ameliorative effect of Wendantang combined with Danshenyin and Dushentang （WDD） on mice with ischemic heart disease （IHD） presenting phlegm-stasis syndrome based on the inflammatory phenotype and differentiation of circulating monocytes. MethodsA model of IHD with phlegm-stasis syndrome was established using left anterior descending coronary artery ligation supplemented with a high-fat diet. Eighty model mice were randomly assigned to the model group， WDD low-dose group （WDD-L）， WDD medium-dose group （WDD-M）， WDD high-dose group （WDD-H）， and atorvastatin calcium tablet group， with 16 mice in each group. An additional 16 C57BL/6J mice were designated as the sham-operation group. The WDD groups received intragastric administration at doses of 8.91， 17.81， 35.62 g·kg^-1， and the atorvastatin calcium tablet group received the corresponding drug at 1.3 mg·kg^-1， twice daily. The sham-operation and model groups were given the same volume of pure water by gavage each day. After 5 consecutive weeks of administration， the cardiac index was calculated. Cardiac function was assessed by echocardiography. Myocardial histopathology was examined by hematoxylin-eosin （HE） staining. Serum N-terminal pro-B-type natriuretic peptide （pro-BNP） content was measured by enzyme-linked immunosorbent assay （ELISA）. Hemorheological parameters were analyzed using an automated hemorheology analyzer. Serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein （LDL）， and high-density lipoprotein （HDL） were determined using an automated biochemical analyzer. Changes in circulating monocytes were detected by flow cytometry. Mouse bone marrow mononuclear cells were isolated in vitro and divided into blank group， model serum group， WDD-L drug-containing serum group， WDD-M drug-containing serum group， and WDD-H drug-containing serum group. CD36 expression and macrophage differentiation in each group were assessed by flow cytometry. The mechanism by which WDD mediates circulating monocyte differentiation was further explored using CD36 knockdown/overexpression RAW264.7 cell lines. ResultsCompared with the sham-operation group， the model group showed a significantly increased cardiac index （P<0.01）， significantly decreased fractional shortening （FS） （P<0.01）， and significantly increased left ventricular end-diastolic internal diameter （LVDD） and left ventricular end-systolic internal diameter （LVDS） （P<0.01）. Cardiomyocytes exhibited marked deformation and necrosis with inflammatory cell infiltration. Serum pro-BNP levels were significantly elevated （P<0.01）， and whole-blood viscosity （BV） at high， medium， and low shear rates was significantly increased （P<0.01）. Compared with the model group， the WDD groups showed significantly reduced cardiac index （P<0.05， P<0.01）， significantly increased FS （P<0.05， P<0.01）， significantly decreased LVDD and LVDS （P<0.01）， markedly improved cardiomyocyte morphology， significantly reduced inflammatory infiltration， significantly decreased serum pro-BNP levels （P<0.01）， and significantly decreased BV at high， medium， and low shear rates （P<0.01）， with the most pronounced improvement observed in the WDD-M group. Compared with the sham-operation group， TC， TG， and LDL levels were significantly increased in the model group （P<0.05， P<0.01）， while HDL levels were significantly decreased （P<0.05）. After WDD-H treatment， TC， TG， and LDL levels were significantly reduced and HDL levels were significantly increased in mice （P<0.05， P<0.01）. Compared with the sham-operation group， classical monocytes in blood and bone marrow and intermediate monocytes in blood were significantly increased in the model group （P<0.01）， whereas intermediate monocytes in bone marrow and non-classical monocytes in blood were significantly decreased （P<0.01）. After WDD administration， all circulating monocyte subsets in blood and bone marrow were significantly alleviated （P<0.05， P<0.01）， with the WDD-M group showing the optimal effect. In vitro， compared with the blank group， CD36 expression on bone marrow monocytes and the proportion of differentiated macrophages were significantly increased in the model serum group （P<0.01）， and CD36 expression was significantly upregulated on RAW264.7 cells （P<0.01）. Compared with the model serum group， all drug-containing serum groups exhibited significantly reduced CD36 expression on bone marrow monocytes and significantly reduced macrophage differentiation （P<0.01）. WDD downregulated CD36 expression in both CD36 knockdown and overexpression RAW264.7 cell lines （P<0.05， P<0.01）， with the strongest regulatory effect observed in the WDD-M drug-containing serum group. ConclusionWDD can significantly improve the manifestations of phlegm-stasis syndrome in IHD mice and reduce the proportion of classical circulating monocytes. Its mechanism may be related to the inhibition of CD36 expression on classical circulating monocytes.

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.

ObjectiveTo investigate the role of 4-week high-intensity interval training (HIIT) in modulating the metabolic homeostasis of the pyruvate-lactate axis in the hippocampus of rats with chronic unpredictable mild stress (CUMS) to improve their depressive-like behavior. MethodsForty-eight SPF-grade 8-week-old male SD rats were randomly divided into 4 groups: the normal quiet group (C), the CUMS quiet group (M), the normal exercise group (HC), and the CUMS exercise group (HM). The M and HM groups received 8 weeks of CUMS modeling, while the HC and HM groups were exposed to 4 weeks of HIIT starting from the 5th week (3 min (85%-90%) S_max+1 min (50%-55%) S_max, 3-5 cycles, S_max is the maximum movement speed). A lactate analyzer was used to detect the blood lactate concentration in the quiet state of rats in the HC and HM groups at week 4 and in the 0, 2, 4, 8, 12, and 24 h after exercise, as well as in the quiet state of rats in each group at week 8. Behavioral indexes such as sucrose preference rate, number of times of uprightness and number of traversing frames in the absenteeism experiment, and other behavioral indexes were used to assess the depressive-like behavior of the rats at week 4 and week 8. The rats were anesthetized on the next day after the behavioral test in week 8, and hippocampal tissues were taken for assay. LC-MS non-targeted metabolomics, target quantification, ELISA and Western blot were used to detect the changes in metabolite content, lactate and pyruvate concentration, the content of key metabolic enzymes in the pyruvate-lactate axis, and the protein expression levels of monocarboxylate transporters (MCTs). Results4-week HIIT intervention significantly increased the sucrose preference rate, the number of uprights and the number of traversed frames in the absent field experiment in CUMS rats; non-targeted metabolomics assay found that 21 metabolites were significantly changed in group M compared to group C, and 14 and 11 differential metabolites were significantly dialed back in the HC and HM groups, respectively, after the 4-week HIIT intervention; the quantitative results of the targeting showed that, compared to group C, lactate concentration in the hippocampal tissues of M group, compared with group C, lactate concentration in hippocampal tissue was significantly reduced and pyruvate concentration was significantly increased, and 4-week HIIT intervention significantly increased the concentration of lactate and pyruvate in hippocampal tissue of HM group; the trend of changes in blood lactate concentration was consistent with the change in lactate concentration in hippocampal tissue; compared with group C, the LDHB content of group M was significantly increased, the content of PKM2 and PDH, as well as the protein expression level of MCT2 and MCT4 were significantly reduced. The 4-week HIIT intervention upregulated the PKM2 and PDH content as well as the protein expression levels of MCT2 and MCT4 in the HM group. ConclusionThe 4-week HIIT intervention upregulated blood lactate concentration and PKM2 and PDH metabolizing enzymes in hippocampal tissues of CUMS rats, and upregulated the expression of MCT2 and MCT4 transport carrier proteins to promote central lactate uptake and utilization, which regulated metabolic homeostasis of the pyruvate-lactate axis and improved depressive-like behaviors.

ObjectiveThis study aimed to investigate the effects of 4-week high-intensity interval training (HIIT) on synaptic plasticity in the prefrontal cortex (PFC) of rats exposed to chronic unpredictable mild stress (CUMS), and to explore its potential mechanisms. MethodsA total of 48 male Sprague-Dawley rats were randomly divided into 4 groups: control (C), model (M), control plus HIIT (HC), and model plus HIIT (HM). Rats in groups M and HM underwent 8 weeks of CUMS to establish depression-like behaviors, while groups HC and HM received HIIT intervention beginning from the 5th week for 4 consecutive weeks. The HIIT protocol consisted of repeated intervals of 3 min at high speed (85%-90% maximal training speed, S_max) alternated with one minute at low speed (50%-55% S_max), with 3 to 5 sets per session, conducted 5 d per week. Behavioral assessments and tail-vein blood lactate levels were measured at the end of the 4th and 8th weeks. After the intervention, rat PFC tissues were collected for Golgi staining to analyze synaptic morphology. Enzyme-linked immunosorbent assays (ELISA) were employed to detect brain-derived neurotrophic factor (BDNF), monocarboxylate transporter 1 (MCT1), lactate, and glutamate levels in the PFC, as well as serotonin (5-HT) levels in serum. Additionally, Western blot analysis was conducted to quantify the expression of synaptic plasticity-related proteins, including c-Fos, activity-regulated cytoskeleton-associated protein (Arc), and N-methyl-D-aspartate receptor 1 (NMDAR1). ResultsCompared to the control group (C), the CUMS-exposed rats (group M) exhibited significant reductions in sucrose preference rates, number of grid crossings, frequency of upright postures, and entries into and duration spent in open arms of the elevated plus maze, indicating marked depressive-like behaviors. Additionally, the group M showed significantly reduced dendritic spine density in the PFC, along with elevated levels of c-Fos, Arc, NMDAR1 protein expression, and increased concentrations of lactate and glutamate. Conversely, BDNF and MCT1 contents in the PFC and 5-HT levels in serum were significantly decreased. Following HIIT intervention, rats in the group HM displayed considerable improvement in behavioral indicators compared with the group M, accompanied by significant elevations in PFC MCT1 and lactate concentrations. Furthermore, HIIT notably normalized the expression levels of c-Fos, Arc, NMDAR1, as well as glutamate and BDNF contents in the PFC. Synaptic spine density also exhibited significant recovery. ConclusionFour weeks of HIIT intervention may alleviate depressive-like behaviors in CUMS rats by increasing lactate levels and reducing glutamate concentration in the PFC, thereby downregulating the overexpression of NMDAR, attenuating excitotoxicity, and enhancing synaptic plasticity.

Resveratrol(Res) is a kind of polyphenolic compound, possessing multiple biological activities such as antioxidant, anti-inflammatory, cardioprotective, and anticancer effects. In recent years, the cardiovascular protective mechanism of Res has become a research hotspot. Studies have shown that Res has a protective effect on the cardiovascular system through various pathways, such as inhibiting oxidative stress, regulating ferroptosis of cells, improving ischemia-reperfusion(I/R) injury, regulating lipid metabolism, suppressing inflammatory responses, and enhancing endothelial function. It can also alleviate cardiotoxicity caused by drugs and chemicals. In terms of oxidative stress, Res reduces the level of intracellular reactive oxygen species(ROS) by enhancing the expression of proteins such as silent information regulator 1(SIRT1) and regulating mitochondrial function, thereby alleviating myocardial cell damage. Regarding ferroptosis, Res inhibits the occurrence of ferroptosis by regulating the expression of proteins related to iron metabolism. Res can also improve I/R injury through mechanisms such as activating autophagy and the mitochondrial quality control network. In regard to improving endothelial function, Res protects the function of endothelial cells by regulating multiple signaling pathways, such as downregulating the PREP1-mediated pathway. Res can also regulate lipid metabolism and inhibit the progression of atherosclerosis. In terms of inflammatory responses, Res exerts anti-inflammatory effects through mechanisms such as inhibiting the nuclear factor-kappa B(NF-κB) signaling pathway. In addition, Res has an improving effect on cardiotoxicity caused by different drugs or environmental factors. However, the clinical application of Res still faces limitations such as poor pharmacokinetic properties. In the future, in-depth exploration is needed at multiple levels from basic research to clinical application to clarify the dose-response relationship and standardize the standards of medication regimens with the expectation of providing more effective strategies for the prevention and treatment of cardiovascular diseases.
Humans ; Resveratrol/pharmacology* ; Animals ; Cardiotonic Agents/pharmacology* ; Oxidative Stress/drug effects* ; Cardiovascular Diseases/genetics* ; Cardiovascular System/metabolism* ; Signal Transduction/drug effects*