Objectives:This study aims to evaluate the relationship between the triglyceride-glucose body mass index(TyG-BMI),the right pericoronary fat attenuation index(RCA-FAI),and prognosis in patients with coronary artery disease(CAD).Methods:This study included 513 CAD patients who underwent coronary computed tomography angiography(CCTA)and coronary angiography between April 2018 and June 2023.Data collection and parameter calculations were performed for all research variables.The patients were stratified into three groups based on TyG-BMI tertiles:T1 group(TyG-BMI≤207.02,n=171),T2 group(207.02

A strategy combining a tailored database and high-throughput activity screening that discover bioactive metabolites derived from Magnoliae Officinalis Cortex(MOC)was developed and implemented to rapidly profile and discover bioactive metabolites in vivo derived from traditional Chinese medicine(TCM).The strategy possessed four characteristics:1)The tailored database consisted of metabolites derived from big data-originated reference compound,metabolites predicted in silico,and MOC chemical profile-based pseudomolecular ions.2)When profiling MOC-derived metabolites in vivo,attentions were paid not only to prototypes of MOC compounds and metabolites directly derived from MOC compounds,as reported by most papers,but also to isomerized metabolites and the degradation products of MOC compounds as well as their derived metabolites.3)Metabolite traceability was performed,especially to distinguish isomeric prototypes-derived metabolites,prototypes of MOC compounds as well as phase Ⅰ metabolites derived from other MOC compounds.4)Molecular docking was utilized for high-throughput activity screening and molecular dynamic simulation as well as zebrafish model were used for verification.Using this strategy,134 metabolites were swiftly characterized after the oral administration of MOC to rats,and several metabolites were reported for the first time.Furthermore,17 potential active metabolites were discovered by targeting the motilin,dopamine D2,and the serotonin type 4(5-HT4)receptors,and part bioactivities were verified using molecular dynamic simulation and a zebrafish constipation model.This study extends the application of mass spectrometry(MS)to rapidly profile TCM-derived metabolites in vivo,which will help pharmacologists rapidly discover potent metabolites from a complex matrix.

Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

A strategy combining a tailored database and high-throughput activity screening that discover bioactive metabolites derived from Magnoliae Officinalis Cortex (MOC) was developed and implemented to rapidly profile and discover bioactive metabolites in vivo derived from traditional Chinese medicine (TCM). The strategy possessed four characteristics: 1) The tailored database consisted of metabolites derived from big data-originated reference compound, metabolites predicted in silico, and MOC chemical profile-based pseudomolecular ions. 2) When profiling MOC-derived metabolites in vivo, attentions were paid not only to prototypes of MOC compounds and metabolites directly derived from MOC compounds, as reported by most papers, but also to isomerized metabolites and the degradation products of MOC compounds as well as their derived metabolites. 3) Metabolite traceability was performed, especially to distinguish isomeric prototypes-derived metabolites, prototypes of MOC compounds as well as phase I metabolites derived from other MOC compounds. 4) Molecular docking was utilized for high-throughput activity screening and molecular dynamic simulation as well as zebrafish model were used for verification. Using this strategy, 134 metabolites were swiftly characterized after the oral administration of MOC to rats, and several metabolites were reported for the first time. Furthermore, 17 potential active metabolites were discovered by targeting the motilin, dopamine D2, and the serotonin type 4 (5-HT₄) receptors, and part bioactivities were verified using molecular dynamic simulation and a zebrafish constipation model. This study extends the application of mass spectrometry (MS) to rapidly profile TCM-derived metabolites in vivo, which will help pharmacologists rapidly discover potent metabolites from a complex matrix.

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Objective This study aimed to investigate the relationship between malnutrition and coronary inflammation,and explore the interaction and mediating effects of coronary inflammation in the association between malnutrition and major adverse cardiovascular events(MACE).Methods A retrospective analysis was conducted on 428 patients diagnosed with coronary heart disease at the Central China Fuwai Hospital from May 2018 to July 2022.All patients underwent coronary angiography(CAG)and coronary computed tomography angiography(CCTA).The TCB index(triglycerides×total cholesterol×body weight)and the coronary fat attenuation index around the proximal right coronary artery(RCA-FAI)were used to assess patients' nutritional state and the degree of coronary inflammation,respectively.The study endpoint was MACE.We used linear regression models to analyze the correlation between TCBI and RCA-FAI,cox regression models to assess the correlation of TCBI and RCA-FAI with MACE,and mediation analysis to investigate whether RCA-FAI mediated the relationship between TCBI and MACE.Results A total of 428 patients were included in the study.There was a negative correlation between RCA-FAI and TCBI(r=-0.224,P<0.001).After adjusting for potential confounders,each standard deviation decrease in the TCBI index was associated with a 2.20 HU increase in RCA-FAI(95%CI:-3.40～-1.19,P<0.001).During a mean follow-up period of 2.15 years,51 MACE occurred.MACE risk in the low TCBI/high RCA-FAI group was 6.58 times higher than that in the high TCBI/low RCA-FAI group(adjusted HR=6.580,95%CI:2.237～19.360,P=0.001),and the interaction between TCBI and RCA-FAI was identified.Mediation analysis revealed that RCA-FAI mediated 37.5%of the associations between TCBI and MACE.Conclusions In patients with coronary artery disease,malnutrition is associated with increased coronary inflammation.There is a significant interaction between malnutrition and coronary inflammation in the risk of MACE,and coronary inflammation partially mediates the relationship between malnutrition and MACE.The combination of the TCBI index and RCA-FAI can help identify patients at high cardiovascular risk.Improving malnutrition and controlling coronary inflammation may provide addi-tional benefits for patients with coronary artery disease.

Objective:To explore the relationships between physical activity and cognitive impairment in older adults aged ≥65 years in longevity areas in China.Methods:A total of 6 081 older adults aged ≥65 years from the Healthy Ageing and Biomarkers Cohort Study in China in 2021 were included in this study. Information about their demographic characteristics, lifestyles, and chronic disease histories were collected, the intensity of physical activity was evaluated by using Physical Activity Scale for the Elderly, and the cognitive function was evaluated by using Mini-Mental State Examination Scale (Chinese version). Multifactorial logistic regression model was used to analyze the associations between different levels and types of physical activity and cognitive impairment in older adults.Results:In the 6 081 older adults, 1 829 (30.1%) had cognitive impairment. After adjusting for confounders, older adults with T2 and T3 levels of physical activity had lower risks for cognitive impairment compared with those with T1 levels of physical activity, with ORs of 0.47 (95% CI: 0.40-0.55) and 0.22 (95% CI: 0.18-0.28). The results of different types of physical activities showed that the ORs in leisure activity T2 and T3 groups were 0.52 (95% CI: 0.44-0.63) and 0.49 (95% CI: 0.41-0.58), and the ORs in housework activity T2 and T3 groups were 0.36 (95% CI: 0.30-0.42) and 0.19 (95% CI: 0.16-0.24). There was no significant association between work-related activity and cognitive impairment. Conclusion:There is a negative association between the intensity level of physical activity and cognitive impairment, and active leisure and household activities might reduce the risk for cognitive impairment.

Objective:To explore the relationships between physical activity and cognitive impairment in older adults aged ≥65 years in longevity areas in China.Methods:A total of 6 081 older adults aged ≥65 years from the Healthy Ageing and Biomarkers Cohort Study in China in 2021 were included in this study. Information about their demographic characteristics, lifestyles, and chronic disease histories were collected, the intensity of physical activity was evaluated by using Physical Activity Scale for the Elderly, and the cognitive function was evaluated by using Mini-Mental State Examination Scale (Chinese version). Multifactorial logistic regression model was used to analyze the associations between different levels and types of physical activity and cognitive impairment in older adults.Results:In the 6 081 older adults, 1 829 (30.1%) had cognitive impairment. After adjusting for confounders, older adults with T2 and T3 levels of physical activity had lower risks for cognitive impairment compared with those with T1 levels of physical activity, with ORs of 0.47 (95% CI: 0.40-0.55) and 0.22 (95% CI: 0.18-0.28). The results of different types of physical activities showed that the ORs in leisure activity T2 and T3 groups were 0.52 (95% CI: 0.44-0.63) and 0.49 (95% CI: 0.41-0.58), and the ORs in housework activity T2 and T3 groups were 0.36 (95% CI: 0.30-0.42) and 0.19 (95% CI: 0.16-0.24). There was no significant association between work-related activity and cognitive impairment. Conclusion:There is a negative association between the intensity level of physical activity and cognitive impairment, and active leisure and household activities might reduce the risk for cognitive impairment.

Objectives:This study aims to evaluate the relationship between the triglyceride-glucose body mass index(TyG-BMI),the right pericoronary fat attenuation index(RCA-FAI),and prognosis in patients with coronary artery disease(CAD).Methods:This study included 513 CAD patients who underwent coronary computed tomography angiography(CCTA)and coronary angiography between April 2018 and June 2023.Data collection and parameter calculations were performed for all research variables.The patients were stratified into three groups based on TyG-BMI tertiles:T1 group(TyG-BMI≤207.02,n=171),T2 group(207.02

Objective This study aimed to investigate the relationship between malnutrition and coronary inflammation,and explore the interaction and mediating effects of coronary inflammation in the association between malnutrition and major adverse cardiovascular events(MACE).Methods A retrospective analysis was conducted on 428 patients diagnosed with coronary heart disease at the Central China Fuwai Hospital from May 2018 to July 2022.All patients underwent coronary angiography(CAG)and coronary computed tomography angiography(CCTA).The TCB index(triglycerides×total cholesterol×body weight)and the coronary fat attenuation index around the proximal right coronary artery(RCA-FAI)were used to assess patients' nutritional state and the degree of coronary inflammation,respectively.The study endpoint was MACE.We used linear regression models to analyze the correlation between TCBI and RCA-FAI,cox regression models to assess the correlation of TCBI and RCA-FAI with MACE,and mediation analysis to investigate whether RCA-FAI mediated the relationship between TCBI and MACE.Results A total of 428 patients were included in the study.There was a negative correlation between RCA-FAI and TCBI(r=-0.224,P<0.001).After adjusting for potential confounders,each standard deviation decrease in the TCBI index was associated with a 2.20 HU increase in RCA-FAI(95%CI:-3.40～-1.19,P<0.001).During a mean follow-up period of 2.15 years,51 MACE occurred.MACE risk in the low TCBI/high RCA-FAI group was 6.58 times higher than that in the high TCBI/low RCA-FAI group(adjusted HR=6.580,95%CI:2.237～19.360,P=0.001),and the interaction between TCBI and RCA-FAI was identified.Mediation analysis revealed that RCA-FAI mediated 37.5%of the associations between TCBI and MACE.Conclusions In patients with coronary artery disease,malnutrition is associated with increased coronary inflammation.There is a significant interaction between malnutrition and coronary inflammation in the risk of MACE,and coronary inflammation partially mediates the relationship between malnutrition and MACE.The combination of the TCBI index and RCA-FAI can help identify patients at high cardiovascular risk.Improving malnutrition and controlling coronary inflammation may provide addi-tional benefits for patients with coronary artery disease.