Chronic heart failure （CHF） is a complex clinical syndrome caused by ventricular dysfunction， with mitochondrial energy metabolism disorder being a critical factor in disease progression. Heart-Yang deficiency syndrome， as the core pathogenesis of CHF， persists throughout the disease course. Insufficiency of heart-Yang leads to weakened warming and propelling functions， resulting in the accumulation of phlegm-fluid， blood stasis， and dampness. This eventually causes Qi stagnation with phlegm obstruction and blood stasis with water retention， forming a vicious cycle that exacerbates disease progression. According to the theory of reinforcing Yang， the clinical experience of the traditional Chinese medicine （TCM） master Tang Zuxuan in treating CHF with heart-Yang deficiency syndrome， and achievements from molecular biological studies， this study innovatively proposes an integrated research framework of "TCM syndrome differentiation and staging-mitochondrial metabolism mechanisms-intervention with Yang-reinforcing prescriptions" which is characterized by the integration of traditional Chinese and Western medicine. Heart-Yang deficiency syndrome is classified into mild （Stage Ⅰ-Ⅱ）， severe （Stage Ⅲ）， and critical （Stage Ⅳ） stages. The study elucidates the precise correlations between the pathogenesis of each stage and mitochondrial metabolism disorders from theoretical， pathophysiological， and therapeutic perspectives. The mild stage is characterized by impaired biogenesis and substrate-utilization imbalance， corresponding to heart-Yang deficiency and phlegm-fluid aggregation. Linggui Zhugantang and similar prescriptions can significantly improve the expression of peroxisome proliferator-activated receptor gamma co-activator-1α（PGC-1α）/silent information regulator 2 homolog 1 （SIRT1） and ATPase activity. The severe stage centers on oxidative stress and structural damage， reflecting Yang deficiency with water overflow and phlegm-blood stasis intermingling. At this stage， Zhenwu Tang and Qiangxin Tang can effectively mitigate oxidative stress damage， increase adenosine triphosphate （ATP） content， and repair mitochondrial structure. The critical stage arises from calcium overload and mitochondrial disintegration， leading to the collapse of Yin-Yang equilibrium. At this stage， Yang-restoring and crisis-resolving prescriptions such as Fuling Sini Tang and Qili Qiangxin capsules can inhibit abnormal opening of the mitochondrial permeability transition pore （MPTP）， reduce cardiomyocyte apoptosis rate， and protect mitochondrial function. By summarizing the characteristics of mitochondrial energy metabolism disorders at different stages of CHF， this study explores the application of the theory of reinforcing Yang in treating heart-Yang deficiency syndrome and provides new insights for the clinical diagnosis and treatment of CHF.

ObjectiveTo explore the mechanism of ventricular remodeling mediated by the p38 mitogen-activated protein kinase （MAPK）/nuclear factor kappa B （NF-κB） signaling pathway in the rat model of chronic heart failure （CHF） with heart-blood stasis syndrome， as well as the intervention effect of Danhong injection. MethodsIn vivo experiment： SPF-grade male SD rats were assigned via the random number table method into 4 groups： Sham operation， model， captopril （8.8 mg·kg^-1）， and Danhong injection （6.0 mL·kg^-1）. The model of CHF with heart-blood stasis syndrome was established by abdominal aortic constriction， and the sham operation group only underwent laparotomy without constriction. All the groups were treated continuously for 15 days. The tongue color of rats was observed. Echocardiography， hemorheology， heart mass index （HMI）， and left ventricular mass index （LVMI） were measured. Hematoxylin-eosin （HE） staining and Masson staining were performed to observe the pathological and fibrotic changes of the myocardial tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to quantify the levels of N-terminal pro-B-type natriuretic peptide （NT-proBNP）， interleukin-6 （IL-6）， angiotensin Ⅱ （AngⅡ）， tumor necrosis factor-α （TNF-α）， and Creactive protein （CRP） in the serum， as well as the levels of matrix metalloproteinase-2 （MMP-2） and matrix metalloproteinase-9 （MMP-9） in the myocardial tissue. Western blot was used to quantify the protein levels of p-p38 MAPK/p38 MAPK and p-NF-κB p65/NF-κB p65 in the myocardial tissue. In vitro experiment： H9C2 cardiomyocytes were treated with 1×10^-6 mol·L^-1 AngⅡ to establish a model of myocardial hypertrophy. H9C2 cardiomyocytes were allocated into normal， model， inhibitor + Danhong injection， Danhong injection （20 mL·L^-1）， and inhibitor （SB203580， 5 μmol·L^-1） groups. CCK-8 assay was used to detect the viability of H9C2 cardiomyocytes. Rhodamine-labeled phalloidin staining was used to reveal the area of cardiomyocytes. Real-time PCR was performed to determine the mRNA levels of atrial natriuretic peptide （ANP） and brain natriuretic peptide （BNP）. Western blot was used to assess the protein levels of p-p38 MAPK/p38 MAPK and p-NF-κB p65/NF-κB p65. ResultsIn vivo experiment： Compared with the sham operation group， the model group showed purplish-dark tongue with decreased R， G， B values of the tongue surface （P<0.01）， increased whole blood viscosity （at low， medium， and high shear rates） （P<0.01）， decreased left ventricular ejection fraction （LVEF） and left ventricular fractional shortening （LVFS） （P<0.01）， increased left ventricular end-diastolic diameter （LVIDd）， left ventricular end-systolic diameter （LVIDs）， and left ventricular posterior wall thickness at end-diastole （LVPWd） （P<0.01）， raised LVMI and HMI （P<0.01）， and elevated levels of NT-proBNP， TNF-α， IL-6， and CRP in the serum and MMP-2 and MMP-9 in the myocardial tissue （P<0.01）. The HE and Masson staining of the myocardial tissue showed compensatory myocardial hypertrophy， fibrosis， and massive inflammatory cell infiltration in the model group. Additionally， the model group presented up-regulated protein levels of p-p38 MAPK/p38 MAPK and p-NF-κB p65/NF-κB p65 in the myocardial tissue （P<0.01）. Compared with the model group， each administration group showed increased R， G， B values of the tongue surface （P<0.05， P<0.01）， decreased whole blood viscosity （at low， medium， and high shear rates） （P<0.05， P<0.01）， increased LVEF and LVFS （P<0.01）， decreased LVIDd， LVIDs， and LVPWd （P<0.05， P<0.01）， declined LVMI and HMI （P<0.05， P<0.01）， and lowered levels of NT-proBNP， TNF-α， IL-6， and CRP in the serum and MMP-2 and MMP-9 in the myocardial tissue （P<0.01）. HE and Masson staining showed alleviated compensatory myocardial hypertrophy， reduced fibrosis， and decreased expression of p-p38 MAPK/p38 MAPK and p-NF-κB p65/NF-κB p65 in the myocardial tissue （P<0.01）. In vitro experiment： When the concentration of Danhong injection reached 20 mL·L^-1， the survival rate of H9C2 cardiomyocytes was the highest （P<0.01）. Compared with the normal group， the model group showed up-regulated mRNA levels of ANP and BNP （P<0.01）， increased relative cell surface area （P<0.01）， and raised protein levels of p-p38 MAPK/p38 MAPK and p-NF-κB p65/NF-κB p65 （P<0.01）. Compared with the model group， each administration group showed down-regulated mRNA levels of ANP and BNP （P<0.01）， reduced relative cell surface area （P<0.05， P<0.01）， and down-regulated protein levels of p-p38 MAPK/p38 MAPK and p-NF-κB p65/NF-κB p65 （P<0.05， P<0.01）. ConclusionDanhong injection can regulate ventricular remodeling through the p38 MAPK/NF-κB pathway， thereby exerting a protective effect on the rat model of CHF with heart-blood stasis syndrome.

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 effect of nicotinamide adenine dinucleotide on vascular endothelial injury in hypertension and its molecular mechanism. MethodsC57BL/6J mice were randomly divided into saline group （Saline） and hypertension group （Ang Ⅱ， which were infused with Ang Ⅱ via subcutaneously implanted osmotic pumps）， and supplemented daily with nicotinamide mononucleotide （300 mg/kg）， a precursor of NAD⁺. Blood pressure， endothelial relaxation function and pulse wave velocity were measured after 4 weeks. Wound healing assay and adhesion assay were used to evaluate the function of endothelial cells in vitro. mtROS levels were detected by immunofluorescence staining. RT-PCR was used to detect the mRNA expression of mtDNA， SIRT3 and isocitrate dehydrogenase 2 （IDH2）. 8-hydroxy-2'-deoxyguanosine levels were detected by enzyme-linked immunosorbent assay. The protein expression levels of p-eNOS， eNOS， SIRT3 and IDH2 were detected by Western blot. ResultsNMN supplementation reduced blood pressure （P<0.001） and improved endothelial function and arterial stiffness （P<0.001） in hypertensive mice. In vitro， NMN improved endothelial function in AngII-stimulated endothelial cells （P<0.05） and attenuated mitochondrial oxidative stress levels （P<0.001）. Mechanistically， NMN elevated SIRT3 activity （P<0.001）， which subsequently enhanced IDH activity （P<0.001） and reduced oxidative stress levels in endothelial cells. Conversely， knockdown of IDH2 would reverse the effect of SIRT3 in improving endothelial function （P<0.001）. ConclusionNAD⁺ lowers blood pressure and enhances vascular function in hypertension by reducing the level of oxidative stress in endothelial cells through activation of the SIRT3/IDH2 signal pathway.

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 current status and influencing factors of heart failure occurrence in patients with unstable angina pectoris (UAP), and to provide a scientific basis for developing individualized prevention and treatment strategies. Methods A total of 310 patients with UAP admitted to the Fifth People's Hospital from October 2021 to October 2024 were selected as study subjects. The current status of the patients' heart failure was statistically analyzed, and the patients were divided into heart failure group and non-heart failure group according to whether they had heart failure. Univariate and logistic multivariate regression analyses were used to analyze the risk factors for the occurrence of heart failure in patients with UAP. Results Among the 310 patients with UAP, 63 cases had heart failure, with an incidence rate of 20.32%. After logistic multivariate analysis, it was found that diabetes mellitus, hyperlipidemia, number of coronary artery lesions, homocysteine and plasma brain natriuretic peptide levels were risk factors of heart failure in patients with UAP, and hemoglobin level was a protective factor (OR: 2.010, 95%CI: 1.063-3.800; OR: 4.495, 95%CI: 2.228-9.067; OR: 2.408, 95%CI: 1.256-4.617; OR: 3.655, 95%CI: 1.812-7.372; OR: 4.693, 95%CI: 2.622-8.399; OR: 0.359, 95%CI: 0.205-0.628, P<0.05). Conclusion The coronary heart disease risk of heart failure is high in patients with UAP, and is affected by comorbidities, number of coronary artery lesions, homocysteine, and plasma brain natriuretic peptide levels. It is necessary to perform clinical screening and pay attention to such patients, and take active prevention and control interventions.