BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

The development of artificial intelligence(AI)technology has provided new avenues for the public to access health information.While both domestic and international research and practical applications of health conversational AI have emerged,there remains a lack of consensus on the key issues surrounding their development.Based on a comprehensive review of domestic and international literature and multiple rounds of expert consultations,the study focuses on six issues:the definition,characteristics,application scenarios,evaluation elements,evaluation methods,as well as prospects and challenges of AI-driven health consultation systems,and develops the practice guideline for health conversational artificial intelligence.The findings aim to provide scientific reference for promoting the innovative application of AI technology in the healthcare field.

Objective:To investigate the sex difference and symptom correlation of pituitary-thyroid axis and pituitary-adrenal axis function in patients with remitted schizophrenia.Methods:Using cross-sectional study method,243 patients with remitted schizophrenia at Changning District Mental Health Center of Shanghai from January 2019 to September 2021(schizophrenia group)were selected,and healthy individuals who underwent physical examinations during the same period(healthy control group)were also selected.The demographic data and HPT axis,HPA axis neuroendocrine indicators[serum thyroid stimulating hormone(TSH),free triiodothyronine(FT3),free thyroid hormone(FT4),triiodothyronine(TT3),thyroid hormone(TT4),adrenocorticotropic hormone(ACTH),and cortisol(COR)]between healthy control group and schizophrenia group were compared.Gender differences in HPT axis and HPA axis in schizophrenia patients were compared.The correlation between neuroendocrine indicators and gender,disease duration,positive and negative symptom scale(PANSS)scores in patients with schizophrenia were analyzed by linear regression method.Using stratified multiple linear regression,laboratory measured variables and age were included as predictive factors in the model to construct a regression prediction model for hormone levels between schizophrenia and healthy control group,as well as subgroups of schizophrenia gender.Generate receiver operating characteristic(ROC)curves based on the probability values of predictive factors,and determine the predictive value of the logistic regression model using the area under the curve(AUC).Results:TT3,TSH,TT4 in schizophrenia group were lower than those in the healthy control group(P＜0.05),while ACTH and COR were higher than those in the healthy control group(P＜0.05).There was a statistically significant difference in the negative symptom scores,TSH,TT3,FT3,ACTH,and COR levels between the female schizophrenia group and male schizophrenia group(P＜0.05).PANSS,total score of general psychopathology score were positively correlated with COR level,negative symptom score was negatively correlated with FT3 levels(all P＜0.05),but there was no linear relationship among the three(absolute value of r＜0.3).ROC curve results showed that,the schizophrenia hormone level model had good discrimination accuracy,with AUC=0.872(95％confidence interval 0.841-0.904),the optimal threshold(Yoden index)=0.651.ROC curve shows that the male and female subgroups of schizophrenia models also have good discrimination accuracy,with AUC=0.794(95％confidence interval 0.737-0.850)and the optimal cutoff value(Yoden index)=0.495.Conclusion:The changes of active T4 in schizophrenia patients may be one of the possible causes of the chronic pathological changes of schizophrenia.The level of high-functioning T3 hormone is significantly lower in female patients than that in male patients.The hierarchical regression model provides good identification accuracy for remitted schizophrenia and gender subgroups.

Objective:Analyze the influencing factors of postoperative recurrence of common bile duct stones in patients with gallstones and multiple common bile duct stones,and evaluate the application value of nomogram predic-tion models.Methods:Collected clinical data of 587 patients with gallstones and multiple common bile duct stones who underwent surgical treatment at Beijing Fengtai Hospital from January 2022 to January 2024.They were divided into a training cohort(n=410)and a validation cohort(n=177)according to a 7∶3 ratio.Analyzed the influencing factors of postoperative recurrence of common bile duct stones in patients with gallstones and multiple common bile duct stones,and constructed a nomogram prediction model based on this.Results:The recurrence rate of postoperative common bile duct stones in the training queue was 38.05％(156/410).Age,BMI,intrahepatic bile duct stones,initial course of dis-ease,duodenal diverticulum,biliary tract infection,history of biliary surgery,thickness of the common bile duct wall,num-ber of multiple stones in the common bile duct,diameter of the common bile duct,and dysfunction of the Oddis sphincter in the duodenal papilla during ERCP examination are independent risk factors for postoperative recurrence in patients with gallbladder stones combined with multiple stones in the common bile duct(P＜0.05).The AUC of the constructed nomo-gram prediction model in the training queue and validation queue were 0.854 and 0.889,respectively.The Hosmer Lem-eshow fitting test results showed that there was no statistically significant difference between the predicted probability and the actual probability of the prediction model(x2=3.952,P=0.865),indicating that this model has good predictive abil-ity.Conclusion:The nomogram prediction model based on various risk factors can effectively predict the risk of post-operative common bile duct stone recurrence in patients with gallstones and multiple common bile duct stones.

This study explored the potential therapeutic targets and mechanisms of Danggui Shaoyao San(DSS) in the prevention and treatment of Alzheimer's disease(AD) through transcriptomics and metabolomics, combined with animal experiments. Fifty male C57BL/6J mice, aged seven weeks, were randomly divided into the following five groups: control, model, positive drug, low-dose DSS, and high-dose DSS groups. After the intervention, the Morris water maze was used to assess learning and memory abilities of mice, and Nissl staining and hematoxylin-eosin(HE) staining were performed to observe pathological changes in the hippocampal tissue. Transcriptomics and metabolomics were employed to sequence brain tissue and identify differential metabolites, analyzing key genes and metabolites related to disease progression. Reverse transcription-quantitative polymerase chain reaction(RT-qPCR) was employed to validate the expression of key genes. The Morris water maze results indicated that DSS significantly improved learning and cognitive function in scopolamine(SCOP)-induced model mice, with the high-dose DSS group showing the best results. Pathological staining showed that DSS effectively reduced hippocampal neuronal damage, increased Nissl body numbers, and reduced nuclear pyknosis and neuronal loss. Transcriptomics identified seven key genes, including neurexin 1(Nrxn1) and sodium voltage-gated channel α subunit 1(Scn1a), and metabolomics revealed 113 differential metabolites, all of which were closely associated with synaptic function, oxidative stress, and metabolic regulation. RT-qPCR experiments confirmed that the expression of these seven key genes was consistent with the transcriptomics results. This study suggests that DSS significantly improves learning and memory in SCOP model mice and alleviates hippocampal neuronal pathological damage. The mechanisms likely involve the modulation of synaptic function, reduction of oxidative stress, and metabolic balance, with these seven key genes serving as important targets for DSS in the treatment of AD.
Animals ; Alzheimer Disease/genetics* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Mice, Inbred C57BL ; Metabolomics ; Transcriptome/drug effects* ; Maze Learning/drug effects* ; Hippocampus/metabolism* ; Humans ; Disease Models, Animal ; Memory/drug effects*

The therapeutic effects of Yueju Pills on depression and cardiovascular diseases have been widely recognized. Previous studies have shown that the drug can significantly improve depressive-like behaviors induced by chronic unpredictable mild stress(CUMS) combined with atherosclerosis(AS). Given the complex pathogenesis of psychocardiac diseases, this study integrated metabolomics and network pharmacology to systematically elucidate the mechanism of Yueju Pills in alleviating depressive symptoms in psychocardiac diseases. The results demonstrate that, after Yueju Pill intervention, the levels of 9 abnormal metabolites in the hippocampus restore to normal ranges, primarily involving key pathways or signaling pathways, including the cyclic adenosine monophosphate(cAMP), mammalian target of rapamycin(mTOR), glycine/serine/threonine metabolism, and aminoacyl-tRNA biosynthesis. In a high-fat diet-induced CUMS ApoE~(-/-) mouse model, Yueju Pills significantly increases adenosine monophosphate(AMP) levels and decreases L-alanine and D-glyceric acid levels in the hippocampus. In conclusion, Yueju Pills exert antidepressant effects by regulating multiple metabolic axes, including glycine/serine/threonine metabolism and the cAMP, mTOR signaling pathways. Network pharmacology predictions reveal that the treatment of CUMS combined with AS by its core active components may be realized through modulating pathways concerning neuroinflammation and synaptic plasticity, including serine/threonine-protein kinase 1(AKT1), mitogen-activated protein kinase 1(MAPK1), and prostaglandin-endoperoxide synthase 2(PTGS2). This study provides a theoretical reference for the clinical application of Yueju Pills in alleviating the depressive symptoms of psychocardiac diseases.
Animals ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Male ; Depression/genetics* ; Humans ; Hippocampus/drug effects* ; Mice, Inbred C57BL ; Signal Transduction/drug effects*

OBJECTIVE: To screen out the key metabolites related to diabetic foot (DF) by integrating genome-wide association studies (GWAS) and metabolome genome-wide association studies (mGWAS). METHODS: The literature databases such as PubMed and China national knowledge infrastructure(CNKI), as well as genomics databases such as PAN UKBB, FinnGen, and IEU Open GWAS were systematically retrieved from database estobilishment to November 2024 on DF-related single nucleotide polymorphisms and genome-wide association studies. DF-single nucleotide polymorphism-metabolite network was constructed by mGWAS package and mGWAS-Explorer platform. The causal relationship between key factors was evaluated by two-sample Mendelian randomization. The genetic correlation between DF and 575 metabolites (source:IEU Open GWAS) was evaluated by linkage disequilibrium score regression. In vitro experiments were conducted to induce injury of human umbilical vein endothelial cells with 30 mM glucose and intervene with 20 μM γ-tocopherol. Changes in cell migration, scratch healing and tube formation function were detected. RESULTS: Twenty-senen literatures on single nucleotide polymorphism literatures and 3 studies on GWAS were included. Genetic analysis results showed DF-related single nucleotide polymorphisms were enriched in vascular endothelial dysfunction-related pathways (such as fluid shear stress and atherosclerosis). The results of metabolic network analysis screened out 19 associated metabolites, among which 12 such as γ -tocopherol and pyruvate had significant genetic correlations with DF. Mendelian randomization suggested matrix metalloproteinase-9(MMP-9) might be a potential driver of DF (β=0.658, P=0.063 8), and the occurrence of DF could reduce the level of high-density lipoprotein (β=-0.002, P=0.015 2). The results of in vitro experiments confirmed that γ -tocopherol could improve endothelial dysfunction induced by high glucose, specifically manifested as an increase in the number of cell migrations, improvement in the scratch healing rate, and recovery of tubule formation ability (P<0.05). CONCLUSION DF has a genetic basis centered on vascular endothelial dysfunction, and its occurrence can lead to further metabolic disorders. The key single nucleotide polymorphism loci integrated provided molecular markers for the risk stratification of foot ulcers in diabetic patients. In addition, γ -tocopherol has demonstrated clinical application potential as a therapeutic drug for DF by significantly improving the function of vascular endothelial cells in a high-glucose environment.
Humans ; Diabetic Foot/drug therapy* ; Polymorphism, Single Nucleotide ; Genome-Wide Association Study ; Genomics ; Metabolomics ; Metabolome

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.