The prescription of Qidong Yixin oral liquid is derived from the experience of national medical master Ren Jixue in treating viral myocarditis （VMC）. It has the functions of tonifying Qi， nourishing the heart，calming the mind， and relieving palpitations. It is used to treat VMC and angina pectoris of coronary heart disease caused by deficiency of both Qi and Yin. However，the understanding of its efficacy evidence， advantageous aspects， dosage and administration， and medication safety remains insufficient in clinical practice. Therefore，the development of the Expert Consensus on the Clinical Application of Qidong Yixin Oral Liquid （hereinafter referred to as consensus） was initiated. Consensus strictly followed the process and methods of the expert consensus on the clinical application of Chinese patent medicines of the China Association of Chinese Medicine，successively completing multiple tasks such as the consensus project initiation，determination of clinical problems，evidence search and evaluation，formation of recommendation opinions and consensus suggestions，solicitation of opinions，peer review， submission for review and release， and so on. Consensus formed a total of 10 recommendation opinions and 12 consensus suggestions，clarifying the clinical positioning，efficacy advantages，syndrome differentiation，dosage and administration，combination therapy，timing of medication，adverse reactions，contraindications， and precautions of Qidong Yixin oral liquid，indicating that it has good clinical advantages and safety in the treatment of VMC and angina pectoris of coronary heart disease，providing norms and references for physicians to safely and rationally apply Qidong Yixin oral liquid. Consensus was reviewed and approved for release by the Standardization Office of the China Association of Chinese Medicine on December 23， 2024. Standard number：GSCACM-376-2024.

Objective: To explore the network structure of eating behaviors with anxiety, depression and perceived stress in adolescents, so as to provide a basis for effective prevention and intervention of eating behavior problems and negative emotions in adolescents. Methods: Based on the Psychology and Behavior Investigation of Chinese Residents (2021) database, the study was conducted among 3 087 adolescents. Sakata Eating Behavior Scale Short From(EBS-SF) was used to investigate their eating behaviors. The Patient Health Questionnaire-9(PHQ-9), Generalized Anxiety Disorder Scale-7 Item(GAD-7), and Perceived Stress Questionnaire-3 Item (PSQ-3) were used to evaluate their depression, anxiety and perceived stress. Network analysis method was applied to construct a network of eating behaviors and negative emotional symptoms among adolescents, so as to evaluate the centrality, bridge strength, stability and accuracy of each item. Results: The total scores of eating behaviors, depression,anxiety and stress perception in adolescents were 17.41±4.53,6.95±6.08,4.86±5.03,9.34±3.80,respectively. The symptom with the highest intensity and expected impact was "I am only satisfied when I buy more food than I need", with a node intensity and expected impact value of 4.37. The nodes Depression and Anxiety were the most closely connected(weight=0.87). There were no statistically significant differences in the network structure( M =0.13,0.11) and network connection strength(female and male:4.16,4.06, s =0.10;urban and rural areas:4.08,4.07, s =0.01) between different sexes and residents ( P >0.05). Conclusion The negative impact of comorbidities such as anxiety, depression, perceived stress and eating behaviors among adolescents can be reduced through targeted prevention and intervention of core symptoms and bridging symptoms.

Objective:To develop a machine learning prediction model based on peripheral blood indicators for assessing 30-day mortality risk in elderly patients diagnosed with hemophagocytic lymphohistiocytosis (HLH).Methods:A retrospective cohort study was conducted, enrolling elderly patients (age≥65 years) diagnosed HLH at the First Affiliated Hospital of Nanjing Medical University between January 1, 2015, and November 30, 2023. Demographic characteristics, clinical manifestations, and laboratory parameters at admission were collected. The study included 204 elderly HLH patients with a median age of 70 (68-75) years, comprising 134 males (65.69%) and 70 females (34.31%). Using computer-generated random numbers, the data was randomly divided into the training and validation cohorts at a 7∶3 ratio. Based on 30-day survival outcomes, patients in the training cohort were categorized into the death and survivor groups. Predictive variables were screened through univariate analysis and the Boruta algorithm, with prediction models constructed using 11 machine learning algorithms. Model performance was evaluated using the following metrics: area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, calibration curve, and decision curve analysis. SHAP analysis was employed for model interpretation.Results:Comparison between the death and survivor groups in the training cohort identified 25 significant indicators ( P<0.05) through univariate analysis. Boruta algorithm-based screening further identified nine predictive variables: urea, ferritin, creatinine (CREA), D-dimer (D-D), platelet (PLT), activated partial thromboplastin time (APTT), aspartate aminotransferase (AST), creatine kinase (CK), and alanine aminotransferase (ALT). Among the 11 algorithms, the top five models by AUC in the training cohort were: XGBoost(AUC=1.000), AdaBoost(AUC=1.000), GBDT(AUC=1.000), DT(AUC=0.967), and RF(AUC=0.945). In the validation cohort, the top five performers by AUC were: RF(AUC=0.812), LR(AUC=0.792), LightGBM(AUC=0.769), AdaBoost(AUC=0.746), and GBDT(AUC=0.742). Thus, the RF model demonstrated optimal performance. SHAP analysis indicated urea as the most significant contributor to prediction outcomes. Conclusion:A machine learning model based on routine laboratory indicators can accurately predict the 30-day mortality risk in elderly HLH patients.

Objective:To develop a machine learning prediction model based on peripheral blood indicators for assessing 30-day mortality risk in elderly patients diagnosed with hemophagocytic lymphohistiocytosis (HLH).Methods:A retrospective cohort study was conducted, enrolling elderly patients (age≥65 years) diagnosed HLH at the First Affiliated Hospital of Nanjing Medical University between January 1, 2015, and November 30, 2023. Demographic characteristics, clinical manifestations, and laboratory parameters at admission were collected. The study included 204 elderly HLH patients with a median age of 70 (68-75) years, comprising 134 males (65.69%) and 70 females (34.31%). Using computer-generated random numbers, the data was randomly divided into the training and validation cohorts at a 7∶3 ratio. Based on 30-day survival outcomes, patients in the training cohort were categorized into the death and survivor groups. Predictive variables were screened through univariate analysis and the Boruta algorithm, with prediction models constructed using 11 machine learning algorithms. Model performance was evaluated using the following metrics: area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, calibration curve, and decision curve analysis. SHAP analysis was employed for model interpretation.Results:Comparison between the death and survivor groups in the training cohort identified 25 significant indicators ( P<0.05) through univariate analysis. Boruta algorithm-based screening further identified nine predictive variables: urea, ferritin, creatinine (CREA), D-dimer (D-D), platelet (PLT), activated partial thromboplastin time (APTT), aspartate aminotransferase (AST), creatine kinase (CK), and alanine aminotransferase (ALT). Among the 11 algorithms, the top five models by AUC in the training cohort were: XGBoost(AUC=1.000), AdaBoost(AUC=1.000), GBDT(AUC=1.000), DT(AUC=0.967), and RF(AUC=0.945). In the validation cohort, the top five performers by AUC were: RF(AUC=0.812), LR(AUC=0.792), LightGBM(AUC=0.769), AdaBoost(AUC=0.746), and GBDT(AUC=0.742). Thus, the RF model demonstrated optimal performance. SHAP analysis indicated urea as the most significant contributor to prediction outcomes. Conclusion:A machine learning model based on routine laboratory indicators can accurately predict the 30-day mortality risk in elderly HLH patients.

OBJECTIVE To investigate the ameliorative effects and potential mechanism of total secondary ginsenosides(TSG)on hypertrophic changes of primary cardiomyocytes stimulated by angiotensin Ⅱ(AngⅡ).METHODS Primary cardiomyocytes were isolated from the hearts of neonatal SD rats and divided into the following groups:control group,AngⅡgroup(2 μmol/L),TSG group(7.5 μg/mL),PFK-015 group[6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3(PFKFB3)inhibitor,10 nmol/L],and TSG+PFK-015 group(TSG 7.5 μg/mL+PFK-015 10 nmol/L).The surface area,protein synthesis,energy metabolism-related indicators[free fatty acid(FFA),coenzyme A(CoA),acetyl coenzyme A(acetyl-CoA)],and the expressions of glycolysis-related factors[hypoxia-inducible factor 1α(HIF-1α),glucose transporter protein 4(GLUT-4),lactate dehydrogenase A(LDHA),pyruvate dehydrogenase kinase 1(PDK1)and PFKFB3]in primary cardiomyocytes of each group were measured.RESULTS Compared with the control group,the surface area of primary cardiomyocytes and protein synthesis were significantly increased,the content of FFA,protein and mRNA expressions of HIF-1α,LDHA,PDK1 and PFKFB3 were significantly increased or up-regulated in the AngⅡ group,while the contents of CoA and acetyl-CoA,the protein and mRNA expressions of GLUT-4 were significantly decreased or down-regulated(P＜0.05).Compared with the AngⅡ group,both TSG group and PFK-015 group showed significant improvements in these indexes,with the TSG+PFK-015 group generally demonstrating superior effects compared to either treatment alone(P＜0.05).CONCLUSIONS TSG can reduce the surface area of AngⅡ-induced primary cardiomyocytes,decrease protein synthesis,and inhibit their hypertrophic changes.These effects may be related to improving energy metabolism and the inhibition of glycolysis activity.

Liver cancer is one of the most threatening diseases to the human body,and most patients are already in the advanced stage at the time of diagnosis,resulting in an extremely high mortality rate.The diagnosis and treatment of early-stage liver cancer is the key to improving the prognosis of patients.Medical imaging is an important method that assists in the diagnosis of liver cancer,and currently,intelligent image recognition technology based on medical imaging data has been widely applied in the field of medical diagnosis and has good application prospects.This article reviews the current status of research on artificial intelligence(AI)methods for the diagnosis of focal liver lesions based on liver medical images and proposes the advantages and shortcomings of current AI diagnosis,so as to provide new research ideas for the intelligent diagnosis of liver cancer in the future.

AIM:Using bioinformatics analysis methods to identify the hub genes involved in myocardial isch-emia-reperfusion injury(MIRI).METHODS:Firstly,the rat MIRI related dataset GSE122020,E-MEXP-2098,and E-GEOD-4105 were downloaded from the database.Secondly,differentially expressed genes(DEGs)were screened from each dataset using the linear models for microarray data(limma)package,and robust DEGs were filtered using the robust rank aggregation(RRA)method.In addition,the surrogate variable analysis(SVA)package was used to merge all datas-ets into one,and merged DEGs were screened using the limma package.The common DEGs were obtained by taking the intersection of the two channels of DEGs.Next,the protein-protein interaction(PPI)network of common DEGs was con-structed,and the hub genes were identified using the density-maximizing neighborhood component(DMNC)algorithm.The receiver operating characteristic curve(ROC)was plotted to evaluate the diagnostic performance of the hub gene.Then,the mRNA and protein expression levels of hub genes were detected in the rat MIRI model,and the literature re-view analysis was carried out on the involvement of hub genes in MIRI.Finally,the gene set enrichment analysis(GSEA)was performed on hub gene to further reveal the possible mechanism in mediating MIRI.RESULTS:A total of 143 robust DEGs and 48 merged DEGs were identified.After taking the intersection of the two,48 common DEGs were obtained.In the PPI network of common DEGs,5 hub genes were screened out,namely MYC proto-oncogene bHLH transcription fac-tor(MYC),prostaglandin-endoperoxide synthase 2(PTGS2),heme oxygenase 1(HMOX1),caspase-3(CASP3),and plasminogen activator urokinase receptor(PLAUR).The ROC results showed that the area under the curve values for all hub genes were greater than 0.8.MYC,PTGS2,CASP3,and PLAUR showed high mRNA and protein expression in rat MIRI,while there was no difference in mRNA and protein expression for HMOX1.The literature review revealed that among the 5 hub genes,only PLAUR has not been reported to be involved in MIRI.The GSEA results for PLAUR indicat-ed that its functional enrichment mainly focused on pathways such as NOD-like receptor signaling pathway,P53 signaling pathway,Toll-like receptor signaling pathway,apoptosis,and fatty acid metabolism.CONCLUSION:MYC,PTGS2,CASP3,HMOX1,and PLAUR are involved in the pathological process of MIRI.PLAUR is a potential hub gene that can mediate MIRI by regulating pathways such as NOD like receptor signaling,P53 signaling,Toll like receptor signaling,cell apoptosis,and fatty acid metabolism.The results can provide reference for further investigation into the molecular mechanisms and therapeutic targets of MIRI.

Objective To explore the molecular mechanism of Yixin Tongbi Mixture in the intervention of atherosclerosis(AS).Methods Thirty-six SD rats were divided into the normal control group,model con-trol group,low dose Yixin Tongbi Mixture group,medium dose Yixin Tongbi Mixture group,high dose Yixin Tongbi Mixture group and atorvastatin group,6 cases in each group.Except the normal control group,the oth-er groups adopted the high fat vitamin D3 mixed feed gavage for constructing the rat AS model.The low dose,medium dose and high doses Yixin Tongbi Mixture groups were gavaged by 10,20,40 g/kg Yixin Tongbi Mixture.The atorvastatin group was gavaged by 5 mg/kg atorvastatin;and the normal control group and mod-el control group were gavaged by placebo once a day for consecutive 14 d.The pathological changes of aortic tissues were observed by HE staining.The levels of serum TC,TG,HDL and LDL were detected by automatic biochemical analyzer.The expression levels of IL-6,TNF-α and ICAM-1 in aortic tissue were detected by ELISA.The expression levels of MMP-2 and MMP-9 in aortic tissue and Bax and Bcl-2 protein expression lev-els in cardiac tissue were detected by Western blot.Results Compared with the normal control group,the aor-tic wall of the model control group rats was significantly thickened,and the levels of serum TC,TG,HDL and LDL were increased.The expression levels of MMP-2,MMP-9,IL-6,TNF-α and ICAM-1 in the aortic tissues were also increased.The expression level of Bax in the myocardial tissues was increased,while the expression level of Bcl-2 was decreased,and the differences were statistically significant(P＜0.05).Compared with the model control group,the above indicators in the medium dose and high doses Yixin Tongbi Mixture groups and atorvastatin group were significantly improved(P＜0.05).Except Bax,IL-6 and ICAM-1 expression lev-els,the other indicators in the low dose Yixin Tongbi Mixture group had no statistically significant differences(P＞0.05).Conclusion Yixin Tongbi Mixture could inhibit the AS occurrence and development by inhibiting inflammation and cellular apoptosis,and its mechanism is related to the abnormal expression of IL-6/TNF-αand MMP-2/MMP-9.

OBJECTIVE To explore the mechanism of Tingli dazao xiefei decoction on ventricular remodeling in model rats with heart failure after myocardial infarction. METHODS The rat model of heart failure after myocardial infarction was established by ligation of anterior descending branch of left coronary artery， which was divided into 8 groups： sham operation group， model group， A779 group （1 mg/kg）， A779 （1 mg/kg）+Tingli dazao xiefei decoction equivalent-dose group （0.8 g/kg）， A779 （1 mg/kg） +Tingli dazao xiefei decoction high-dose group （1.6 g/kg）， Tingli dazao xiefei decoction equivalent-dose group （0.8 g/kg）， Tingli dazao xiefei decoction high-dose group （1.6 g/kg） and losartan potassium group （10 mg/kg）. Each group was given equal volume of distilled water or corresponding drugs intragastrically for 4 weeks. Masson staining was used to determine the distribution of collagen fibers in rat myocardium. The content of hydroxyproline （Hyp） in myocardium was determined by alkaline hydrolyzation. The expressions of type Ⅰ and Ⅲ collagen （COLⅠ， COLⅢ）in myocardium were detected by immunohisto-chemistry. Myocardial fibrosis-related indexes such as matrix metalloproteinase-2 （MMP-2）， MMP-9， tissue inhibitor of metalloproteinase-1 （TIMP-1） and soluble suppression of tumorigenicity-2 （sST-2） were detected by ELISA. The protein expressions of angiotensin converting enzyme 2-angiotensin-（1-7）-Mas [ACE2-Ang-（1-7）-Mas] axis were detected by Western blot. RESULTS Compared with sham operation group， myocardial cells in model group and A779 group were disordered， collagen fiber deposition was significantly increased and myocardial fibrosis was obvious； the Hyp content and MMP-2， MMP-9， sST-2 levels were increased， and COL Ⅰ and COL Ⅲ positive expressions were significantly enhanced； TIMP-1 level， protein expressions of ACE2， Ang-（1-7） and Mas were significantly decreased （P＜0.05）. Compared with model group， above indexes of Tingli dazao xiefei decoction equivalent-dose and high-dose groups were improved to different extents. Compared with A779 group， A779+Tingli dazao xiefei decoction equivalent-dose and A779+high-dose groups could improve myocardial arrangement and collagen distribution， reduce the Hyp content and MMP-2， MMP-9 levels， reduce positive expressions of COL Ⅰ and COL Ⅲ （P＜0.05）， but couldn’t improve Ang-（1-7） and Mas protein expression. CONCLUSIONS Tingli dazao xiefei decoction can improve ventricular remodeling in myocardial failure model rats after myocardial infarction by improving the expression of ACE2- Ang（- 1-7）-Mas axis proteins.

Coronary heart disease （CHD） with atherosclerosis is a common chronic disease worldwide， and anxiety and depression are potential and crucial risk factors for adverse prognosis in CHD. Chaihu Longgu Mulitang （CLMT）， first mentioned in the Shang Han Lun （《伤寒论》）， is a classic prescription for treating Shaoyang diseases combined with disturbance of the mind and spirit， with the effects of harmonizing Shaoyang and calming the mind. Current research on mechanisms has shown that CLMT can play a role in CHD complicated with anxiety and depression through multiple pathways， including regulating related signaling pathways， inhibiting the expression of inflammatory factors， improving oxidative stress damage， modulating neurotransmitter levels， suppressing the hypothalamic-pituitary-adrenal axis， promoting mobilization of mesenchymal stem cells from the bone marrow， and inhibiting platelet activation. Clinical studies have demonstrated that CLMT significantly improves symptoms such as angina and insomnia caused by CHD complicated with anxiety and depression， effectively reduces negative emotions， improves traditional Chinese medicine （TCM） syndrome scores， and decreases levels of inflammatory factors. Furthermore， it has fewer adverse reactions and higher safety than conventional western medicine treatments. This article provides a review of the mechanisms and clinical studies of CLMT in the treatment of CHD complicated with anxiety and depression based on a comprehensive analysis of literature from the China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP， PubMed， and other databases in the past 15 years， in order to provide references for further research on the use of CLMT in the management of CHD complicated with anxiety and depression.