Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a cli...Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.展开更多
目的:本研究旨在探讨身体质量指数(body mass index,BMI)与子宫内膜癌之间的因果关系。方法:使用全基因组关联研究(GWAS)的公开汇总数据集进行孟德尔随机化(MR)分析。从171977名欧洲血统参与者的汇总数据中,总共提取37个单核苷酸多态性(...目的:本研究旨在探讨身体质量指数(body mass index,BMI)与子宫内膜癌之间的因果关系。方法:使用全基因组关联研究(GWAS)的公开汇总数据集进行孟德尔随机化(MR)分析。从171977名欧洲血统参与者的汇总数据中,总共提取37个单核苷酸多态性(SNP)作为暴露BMI的工具变量。子宫内膜癌的GWAS汇总数据来自子宫内膜癌协会联盟(12906例病例和108979例对照)。采用逆方差加权(IVW)作为MR的主要分析方法,并辅以加权中位数(WME)、MR-Egger法、简单众数法(SM)和加权众数法(WM)。同时进行了多效性分析、异质性分析和留一敏感性分析,以进一步评估BMI与子宫内膜癌之间的因果关系。结果:IVW分析发现,BMI与子宫内膜癌之间存在因果关系(OR:1.734,95%CI:1.442,2.084),BMI每增加一个标准差(4.77 kg/m^(2))与子宫内膜癌总体风险增加相关。异质性分析显示纳入SNP之间无异质性(P=0.055)。水平多效性分析显示BMI与子宫内膜癌之间的相关性无水平多效性(MR Egger截距=0.010,P=0.278)。结论:BMI与子宫内膜癌之间存在因果关系,BMI升高增加患子宫内膜癌的风险。展开更多
A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy...A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.展开更多
基金supported by an internal fund from Macao Polytechnic University(RP/FCSD-02/2022).
文摘Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.
文摘目的:本研究旨在探讨身体质量指数(body mass index,BMI)与子宫内膜癌之间的因果关系。方法:使用全基因组关联研究(GWAS)的公开汇总数据集进行孟德尔随机化(MR)分析。从171977名欧洲血统参与者的汇总数据中,总共提取37个单核苷酸多态性(SNP)作为暴露BMI的工具变量。子宫内膜癌的GWAS汇总数据来自子宫内膜癌协会联盟(12906例病例和108979例对照)。采用逆方差加权(IVW)作为MR的主要分析方法,并辅以加权中位数(WME)、MR-Egger法、简单众数法(SM)和加权众数法(WM)。同时进行了多效性分析、异质性分析和留一敏感性分析,以进一步评估BMI与子宫内膜癌之间的因果关系。结果:IVW分析发现,BMI与子宫内膜癌之间存在因果关系(OR:1.734,95%CI:1.442,2.084),BMI每增加一个标准差(4.77 kg/m^(2))与子宫内膜癌总体风险增加相关。异质性分析显示纳入SNP之间无异质性(P=0.055)。水平多效性分析显示BMI与子宫内膜癌之间的相关性无水平多效性(MR Egger截距=0.010,P=0.278)。结论:BMI与子宫内膜癌之间存在因果关系,BMI升高增加患子宫内膜癌的风险。
文摘A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.
基金ACKNOWLEDGEMENTS We especially thank the National Science Foundation of China （70771009, 71071017） and the Fundamental Research Funds for the Central Universities （FRF-BR-09-019） for supporting this research.
