Microglia are immune-competent cells involved in maintaining brain homeostasis through their capacity to prune synapses and continuously survey the brain environment.The activation of microglia is one of the most prom...Microglia are immune-competent cells involved in maintaining brain homeostasis through their capacity to prune synapses and continuously survey the brain environment.The activation of microglia is one of the most prominent characteristics of Alzheimer’s disease(AD),a severe neurodegenerative disease featuring extra-cellular deposits of amyloid-βplaques(Aβplaques)and intracellular neurofibrillary tangles(NFTs)as the result of tau hyperphosphorylation.Whether microglia activation is beneficial or detrimental for brains with AD is still con-troversial.In this article,we review recent studies focus-ing on microglia phenotypes in AD by single-cell omics,to understand the signature genes,functions,and regula-tory factors of each phenotype.A profound understanding of the heterogeneity of microglial phenotypes will sug-gest new avenues for treatments for AD.展开更多
Background:Alzheimer’s disease(AD)is one of the most common neurodegenerative disorders in the elderly.Although numerous structural magnetic resonance imaging(sMRI)studies have reported diagnostic models that could d...Background:Alzheimer’s disease(AD)is one of the most common neurodegenerative disorders in the elderly.Although numerous structural magnetic resonance imaging(sMRI)studies have reported diagnostic models that could distinguish AD from normal controls(NCs)with 80–95%accuracy,limited efforts have been made regarding the clinically practical computer-aided diagnosis(CAD)system for AD.Objective:To explore the potential factors that hinder the clinical translation of the AD-related diagnostic mod-els based on sMRI.Methods:To systematically review the diagnostic models for AD based on sMRI,we identified relevant studies published in the past 15 years on PubMed,Web of Science,Scopus,and Ovid.To evaluate the heterogeneity and publication bias among those studies,we performed subgroup analysis,meta-regression,Begg’s test,and Egger’s test.Results:According to our screening criterion,101 studies were included.Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies,accompanied by significant heterogeneity.Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI,which included but was not limited to the following aspects:(i)different datasets;(ii)different machine learning models,e.g.traditional machine learning or deep learning model;(iii)different cross-validation methods,e.g.k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation,but both overestimate the accuracy when compared to validation in independent samples;(iv)different sample sizes;and(v)the publication times.We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD.Conclusions:Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI.However,considering the many factors hindering clinical radiology practice,there would still be a long way to go to improve.展开更多
文摘Microglia are immune-competent cells involved in maintaining brain homeostasis through their capacity to prune synapses and continuously survey the brain environment.The activation of microglia is one of the most prominent characteristics of Alzheimer’s disease(AD),a severe neurodegenerative disease featuring extra-cellular deposits of amyloid-βplaques(Aβplaques)and intracellular neurofibrillary tangles(NFTs)as the result of tau hyperphosphorylation.Whether microglia activation is beneficial or detrimental for brains with AD is still con-troversial.In this article,we review recent studies focus-ing on microglia phenotypes in AD by single-cell omics,to understand the signature genes,functions,and regula-tory factors of each phenotype.A profound understanding of the heterogeneity of microglial phenotypes will sug-gest new avenues for treatments for AD.
基金supported by the Beijing Natural Science Funds for Distinguished Young Scholars(No.JQ20036)the Fundamental Research Funds for the Central Universities(No.2021XD-A03-1)the National Natural Science Foundation of China(Nos.81871438 and 82172018).
文摘Background:Alzheimer’s disease(AD)is one of the most common neurodegenerative disorders in the elderly.Although numerous structural magnetic resonance imaging(sMRI)studies have reported diagnostic models that could distinguish AD from normal controls(NCs)with 80–95%accuracy,limited efforts have been made regarding the clinically practical computer-aided diagnosis(CAD)system for AD.Objective:To explore the potential factors that hinder the clinical translation of the AD-related diagnostic mod-els based on sMRI.Methods:To systematically review the diagnostic models for AD based on sMRI,we identified relevant studies published in the past 15 years on PubMed,Web of Science,Scopus,and Ovid.To evaluate the heterogeneity and publication bias among those studies,we performed subgroup analysis,meta-regression,Begg’s test,and Egger’s test.Results:According to our screening criterion,101 studies were included.Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies,accompanied by significant heterogeneity.Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI,which included but was not limited to the following aspects:(i)different datasets;(ii)different machine learning models,e.g.traditional machine learning or deep learning model;(iii)different cross-validation methods,e.g.k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation,but both overestimate the accuracy when compared to validation in independent samples;(iv)different sample sizes;and(v)the publication times.We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD.Conclusions:Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI.However,considering the many factors hindering clinical radiology practice,there would still be a long way to go to improve.
