Chitinase-3-like protein 1(CHI3L1)is part of the glycoside hydrolase family 18.Despite lacking enzymatic activity,its unique structure allows it to bind to ligands,altering its steric configuration to mediate cell pro...Chitinase-3-like protein 1(CHI3L1)is part of the glycoside hydrolase family 18.Despite lacking enzymatic activity,its unique structure allows it to bind to ligands,altering its steric configuration to mediate cell proliferation,inflammation,fibrosis,and carcinogenesis.In liver disease,CHI3L1 serves as a common diagnostic biomarker for hepatitis-related fibrosis.Additionally,CHI3L1 can predict the risk of non-alcoholic steatohepatitis,the progression of hepatic fibrosis,and the prognosis of alcoholic liver disease and hepatocellular carcinoma.It also aids in diagnosing and staging non-alcoholic fatty liver disease-related and alcoholic liver disease-related fibrosis,and in monitoring hepatitis-related fibrosis treatment.Furthermore,CHI3L1 is secreted by various cells,including hepatocytes,hepatic stellate cells,macrophages,and mesenchymal stem cells,to regulate hepatic injury,fibrosis,steatosis,and hepatocellular carcinoma through different signaling pathways.This review highlights CHI3L1's dual roles as both a biomarker and regulator in various liver diseases,aiming to broaden researchers'understanding of its potential applications.展开更多
Approaches to enhance adeno-associated virus(AAV)-based cardiac gene transfer are the key to successful cardiac gene therapy,but factors influencing AAV transduction remain poorly investigated.This study showed that m...Approaches to enhance adeno-associated virus(AAV)-based cardiac gene transfer are the key to successful cardiac gene therapy,but factors influencing AAV transduction remain poorly investigated.This study showed that myocardial infarction(MI)enhanced cardiac AAV transduction,peaking at the third day post-MI in mice.The excessive AAV enrichment at the border zone is due to local vascular permeabilization and cardiomyocyte metabolic remodeling,which is independent of AAV dosage,serotypes and promoters.This effect was harnessed to boost cardiac base editing and improve the outcome of gene therapy for MI in mice.Thus,heart disease itself is a non-negligible factor that alters AAV-based cardiac gene transfer,which provides a new inroad to develop approaches to enhance cardiac gene therapy.展开更多
Importance:Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized trai...Importance:Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, which limits its generalizability. Deep learning, a subset of machine learning, involves training artiffcial neural networks to learn from large datasets and perform complex tasks with intricate patterns. Over the past decade, deep learning has been successfully applied to heart sound analysis, achieving remarkable results and accumulating substantial heart sound data for model training. Although several reviews have summarized deep learning algorithms for heart sound analysis, there is a lack of comprehensive summaries regarding the available heart sound data and the clinical applications. Highlights:This review will compile the commonly used heart sound datasets, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis, along with their limitations and areas for future improvement. Conclusions:The integration of deep learning into heart sound analysis represents a signiffcant advancement in clinical practice. The growing availability of heart sound datasets and the continuous development of deep learning techniques contribute to the improvement and broader clinical adoption of these models. However, ongoing research is needed to address existing challenges and reffne these technologies for broader clinical use.展开更多
基金supported by the National Key R&D Program of China(No.2022YFA1303804).
文摘Chitinase-3-like protein 1(CHI3L1)is part of the glycoside hydrolase family 18.Despite lacking enzymatic activity,its unique structure allows it to bind to ligands,altering its steric configuration to mediate cell proliferation,inflammation,fibrosis,and carcinogenesis.In liver disease,CHI3L1 serves as a common diagnostic biomarker for hepatitis-related fibrosis.Additionally,CHI3L1 can predict the risk of non-alcoholic steatohepatitis,the progression of hepatic fibrosis,and the prognosis of alcoholic liver disease and hepatocellular carcinoma.It also aids in diagnosing and staging non-alcoholic fatty liver disease-related and alcoholic liver disease-related fibrosis,and in monitoring hepatitis-related fibrosis treatment.Furthermore,CHI3L1 is secreted by various cells,including hepatocytes,hepatic stellate cells,macrophages,and mesenchymal stem cells,to regulate hepatic injury,fibrosis,steatosis,and hepatocellular carcinoma through different signaling pathways.This review highlights CHI3L1's dual roles as both a biomarker and regulator in various liver diseases,aiming to broaden researchers'understanding of its potential applications.
基金the National Science and Technology Major Project of China(2023ZD0503100 to Y.G.)the National Key R&D Program of China(2022YFA1104800 to Y.G.)+2 种基金Beijing Natural Science Foundation(F252059/25FS1588 to Y.G.and F.G.)the National Natural Science Foundation of China(82222006 to Y.G.,82070235 to E.D.,92168113 to E.D.and 82470343 to F.G.)the CAMS Innovation Fund for Medical Sciences(2021-I2M-5-003 to E.D.).
文摘Approaches to enhance adeno-associated virus(AAV)-based cardiac gene transfer are the key to successful cardiac gene therapy,but factors influencing AAV transduction remain poorly investigated.This study showed that myocardial infarction(MI)enhanced cardiac AAV transduction,peaking at the third day post-MI in mice.The excessive AAV enrichment at the border zone is due to local vascular permeabilization and cardiomyocyte metabolic remodeling,which is independent of AAV dosage,serotypes and promoters.This effect was harnessed to boost cardiac base editing and improve the outcome of gene therapy for MI in mice.Thus,heart disease itself is a non-negligible factor that alters AAV-based cardiac gene transfer,which provides a new inroad to develop approaches to enhance cardiac gene therapy.
基金supported by the National Natural Science Foundation of China(No.62102008)the Peking University People’s Hospital Scientific Research Development Funds(RDJP2022-39)the Clinical Medicine Plus X-Young Scholars Project of Peking University,and the Fundamental Research Funds for the Central Universities(PKU2024LCXQ030).
文摘Importance:Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, which limits its generalizability. Deep learning, a subset of machine learning, involves training artiffcial neural networks to learn from large datasets and perform complex tasks with intricate patterns. Over the past decade, deep learning has been successfully applied to heart sound analysis, achieving remarkable results and accumulating substantial heart sound data for model training. Although several reviews have summarized deep learning algorithms for heart sound analysis, there is a lack of comprehensive summaries regarding the available heart sound data and the clinical applications. Highlights:This review will compile the commonly used heart sound datasets, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis, along with their limitations and areas for future improvement. Conclusions:The integration of deep learning into heart sound analysis represents a signiffcant advancement in clinical practice. The growing availability of heart sound datasets and the continuous development of deep learning techniques contribute to the improvement and broader clinical adoption of these models. However, ongoing research is needed to address existing challenges and reffne these technologies for broader clinical use.
