Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and cl...Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.展开更多
OBJECTIVE:To investigate biological indicators of sub-optimal health status and provide means of objective assessment of sub-optimal health status.METHODS:We set the unified standards for diagnosing a SHS.We tested va...OBJECTIVE:To investigate biological indicators of sub-optimal health status and provide means of objective assessment of sub-optimal health status.METHODS:We set the unified standards for diagnosing a SHS.We tested various laboratory indicators in 407 cases that we selected randomly from2807 subjects and collected 15 mL of fasting venous blood from each case.We measured serum immunoglobulin A(IgA)and immunoglobulin G(IgG)concentrations,serum beta endorphins(β-EP),cortisol(C),testosterone(T),plasma adrenocorticotropic hormone(ACTH)and serum T lymphocyte subsets CD3+and CD4+.RESULTS:Mean serum testosterone concentrations and their ratio to cortisol(C)concentrations weresignificantly higher in the healthy group than in those with sub-optimal health status(P<0.01).Mean serum CD3+concentrations were significantly higher in those with sub-optimal health status than in the healthy group(P<0.05).CONCLUSION:Decreased serum testosterone/cortisol ratio may be an objective indication of sub-optimal health status.Changes in neuroendocrine and immunological indicators may explain some of the symptoms,including malaise and poor work performance,attributable to persistent or relapsing fatigue in subjects with sub-optimal health status.展开更多
基金This work was funded by the scientific and technological innovation project of China Academy of Chinese Medical Sciences(CI2021A04706,CI2021B003)the National Key Research and Development Program of China(2023YFC3503404,2017YFC1700406-2,2018YFC1704306)the independent selection project of China Academy of Chinese Medical Sciences(Z0643,Z0723).
文摘Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.
基金Supported by China National Funds for Distinguished Young Scientists(No.30825046)Hi-Tech Research and Development Program of China(863 Program),No.2008AA02Z406Program for Innovative Research Team in Beijing University of Chinese Medicine(No.2011CXTD-07)
文摘OBJECTIVE:To investigate biological indicators of sub-optimal health status and provide means of objective assessment of sub-optimal health status.METHODS:We set the unified standards for diagnosing a SHS.We tested various laboratory indicators in 407 cases that we selected randomly from2807 subjects and collected 15 mL of fasting venous blood from each case.We measured serum immunoglobulin A(IgA)and immunoglobulin G(IgG)concentrations,serum beta endorphins(β-EP),cortisol(C),testosterone(T),plasma adrenocorticotropic hormone(ACTH)and serum T lymphocyte subsets CD3+and CD4+.RESULTS:Mean serum testosterone concentrations and their ratio to cortisol(C)concentrations weresignificantly higher in the healthy group than in those with sub-optimal health status(P<0.01).Mean serum CD3+concentrations were significantly higher in those with sub-optimal health status than in the healthy group(P<0.05).CONCLUSION:Decreased serum testosterone/cortisol ratio may be an objective indication of sub-optimal health status.Changes in neuroendocrine and immunological indicators may explain some of the symptoms,including malaise and poor work performance,attributable to persistent or relapsing fatigue in subjects with sub-optimal health status.
