首页 > 最新文献

Current Problems in Cardiology最新文献

英文 中文
Title Page
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-08 DOI: 10.1016/S0146-2806(25)00032-5
{"title":"Title Page","authors":"","doi":"10.1016/S0146-2806(25)00032-5","DOIUrl":"10.1016/S0146-2806(25)00032-5","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 3","pages":"Article 103009"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Information for Readers
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-08 DOI: 10.1016/S0146-2806(25)00033-7
{"title":"Information for Readers","authors":"","doi":"10.1016/S0146-2806(25)00033-7","DOIUrl":"10.1016/S0146-2806(25)00033-7","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 3","pages":"Article 103010"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Guidelines for Authors
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-08 DOI: 10.1016/S0146-2806(25)00038-6
{"title":"Guidelines for Authors","authors":"","doi":"10.1016/S0146-2806(25)00038-6","DOIUrl":"10.1016/S0146-2806(25)00038-6","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 3","pages":"Article 103015"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-03 DOI: 10.1016/j.cpcardiol.2025.103004
André Luiz Carvalho Ferreira MD , Luanna Paula Garcez de Carvalho Feitoza , Maria E. Benitez MD , Buena Aziri MD , Edin Begic MD , Luciana Vergara Ferraz de Souza MD , Elísio Bulhões , Sarah O.N. Monteiro , Maria L.R. Defante , Roberto Augusto Mazetto Silva Vieira , Camila Guida MD

Introduction

AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction (LVEF <40 %) is not yet well established. What is the sensitivity and specificity of artificial-based electrocardiogram to diagnose heart failure with low ejection fraction (cut-off of 40 %. Aims: We conducted a meta-analysis and systematic review to evaluate the accuracy of artificial intelligence electrocardiograms in estimating an ejection fraction below 40 %.

Methods

We searched PubMed, Embase, and Cochrane Library for studies evaluating the performance of AI ECGs in diagnosing heart failure with reduced ejection fraction. We computed true positives, true negatives, false positives, and false negatives events to estimate pooled sensitivity, specificity, and area under the curve, using R software version 4.3.1, under a random-effects model.

Results

We identified 9 studies, including patients with a paired artificial intelligence-enabled electrocardiogram with an echocardiography. patients had an ejection fraction below 40 % according to the echocardiogram. The AI-ECG data yielded areas under the receiver operator of, the sensitivity of), specificity of, and area under the curve of. The mean/median age ranged from 60±9 to 68.05± 11.9 years.

Conclusions

In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity to estimate a left ventricular ejection fraction below 40 %.
{"title":"Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis","authors":"André Luiz Carvalho Ferreira MD ,&nbsp;Luanna Paula Garcez de Carvalho Feitoza ,&nbsp;Maria E. Benitez MD ,&nbsp;Buena Aziri MD ,&nbsp;Edin Begic MD ,&nbsp;Luciana Vergara Ferraz de Souza MD ,&nbsp;Elísio Bulhões ,&nbsp;Sarah O.N. Monteiro ,&nbsp;Maria L.R. Defante ,&nbsp;Roberto Augusto Mazetto Silva Vieira ,&nbsp;Camila Guida MD","doi":"10.1016/j.cpcardiol.2025.103004","DOIUrl":"10.1016/j.cpcardiol.2025.103004","url":null,"abstract":"<div><h3>Introduction</h3><div>AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction (LVEF &lt;40 %) is not yet well established. What is the sensitivity and specificity of artificial-based electrocardiogram to diagnose heart failure with low ejection fraction (cut-off of 40 %. Aims: We conducted a meta-analysis and systematic review to evaluate the accuracy of artificial intelligence electrocardiograms in estimating an ejection fraction below 40 %.</div></div><div><h3>Methods</h3><div>We searched PubMed, Embase, and Cochrane Library for studies evaluating the performance of AI ECGs in diagnosing heart failure with reduced ejection fraction. We computed true positives, true negatives, false positives, and false negatives events to estimate pooled sensitivity, specificity, and area under the curve, using R software version 4.3.1, under a random-effects model.</div></div><div><h3>Results</h3><div>We identified 9 studies, including patients with a paired artificial intelligence-enabled electrocardiogram with an echocardiography. patients had an ejection fraction below 40 % according to the echocardiogram. The AI-ECG data yielded areas under the receiver operator of, the sensitivity of), specificity of, and area under the curve of. The mean/median age ranged from 60±9 to 68.05± 11.9 years.</div></div><div><h3>Conclusions</h3><div>In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity to estimate a left ventricular ejection fraction below 40 %.</div></div>","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 4","pages":"Article 103004"},"PeriodicalIF":3.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Title Page
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-01 DOI: 10.1016/S0146-2806(24)00591-7
{"title":"Title Page","authors":"","doi":"10.1016/S0146-2806(24)00591-7","DOIUrl":"10.1016/S0146-2806(24)00591-7","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102956"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editor’s Message
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-01 DOI: 10.1016/S0146-2806(24)00594-2
{"title":"Editor’s Message","authors":"","doi":"10.1016/S0146-2806(24)00594-2","DOIUrl":"10.1016/S0146-2806(24)00594-2","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102959"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Guidelines for Authors
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-01 DOI: 10.1016/S0146-2806(24)00597-8
{"title":"Guidelines for Authors","authors":"","doi":"10.1016/S0146-2806(24)00597-8","DOIUrl":"10.1016/S0146-2806(24)00597-8","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102962"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Information for Readers
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-01 DOI: 10.1016/S0146-2806(24)00592-9
{"title":"Information for Readers","authors":"","doi":"10.1016/S0146-2806(24)00592-9","DOIUrl":"10.1016/S0146-2806(24)00592-9","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102957"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Innovations in quantitative rapid testing: Early prediction of health risks 定量快速检测的创新：健康风险的早期预测。
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-02-01 DOI: 10.1016/j.cpcardiol.2025.103000
Khaled S Allemailem , Saad Almousa , Mohammed Alissa , Faris Alrumaihi , Hajed Obaid Alharbi , Nahlah Makki Almansour , Leen A. Aldaiji , Amr S. Abouzied , Mahdi H. Alsugoor , Omer Alasmari , Marwh Jamal Albakawi , Jens Stride
As health monitoring becomes increasingly intricate, the demand for innovative solutions to predict and assess health status is more pressing than ever. This review focuses on the transformative potential of multi-sensor technologies in health monitoring, emphasizing their role in early health status prediction. By integrating diverse sensor types ranging from wearable fitness trackers to implantable devices and environmental monitors healthcare professionals can gain a richer, more nuanced understanding of an individual's physiological state. We analyze various configurations of multi-sensor networks and their efficacy in identifying early indicators of health issues, such as cardiovascular diseases, diabetes, and respiratory ailments. For example, the combination of biometric sensors that track vital signs with environmental data on pollutants can yield invaluable insights into a patient's overall health. This integrated approach not only improves the accuracy of health assessments but also facilitates timely interventions. Furthermore, we address the challenges inherent in multi-sensor systems, including data integration, device interoperability, and the need for advanced algorithms capable of processing complex datasets. Recent advancements in machine learning and artificial intelligence are underscored as pivotal in enhancing the capabilities of these technologies for predictive health analytics. Ultimately, this review highlights how multi-sensor systems can redefine early health status prediction, paving the way for proactive healthcare strategies that significantly improve patient outcomes and optimize healthcare delivery.
{"title":"Innovations in quantitative rapid testing: Early prediction of health risks","authors":"Khaled S Allemailem ,&nbsp;Saad Almousa ,&nbsp;Mohammed Alissa ,&nbsp;Faris Alrumaihi ,&nbsp;Hajed Obaid Alharbi ,&nbsp;Nahlah Makki Almansour ,&nbsp;Leen A. Aldaiji ,&nbsp;Amr S. Abouzied ,&nbsp;Mahdi H. Alsugoor ,&nbsp;Omer Alasmari ,&nbsp;Marwh Jamal Albakawi ,&nbsp;Jens Stride","doi":"10.1016/j.cpcardiol.2025.103000","DOIUrl":"10.1016/j.cpcardiol.2025.103000","url":null,"abstract":"<div><div>As health monitoring becomes increasingly intricate, the demand for innovative solutions to predict and assess health status is more pressing than ever. This review focuses on the transformative potential of multi-sensor technologies in health monitoring, emphasizing their role in early health status prediction. By integrating diverse sensor types ranging from wearable fitness trackers to implantable devices and environmental monitors healthcare professionals can gain a richer, more nuanced understanding of an individual's physiological state. We analyze various configurations of multi-sensor networks and their efficacy in identifying early indicators of health issues, such as cardiovascular diseases, diabetes, and respiratory ailments. For example, the combination of biometric sensors that track vital signs with environmental data on pollutants can yield invaluable insights into a patient's overall health. This integrated approach not only improves the accuracy of health assessments but also facilitates timely interventions. Furthermore, we address the challenges inherent in multi-sensor systems, including data integration, device interoperability, and the need for advanced algorithms capable of processing complex datasets. Recent advancements in machine learning and artificial intelligence are underscored as pivotal in enhancing the capabilities of these technologies for predictive health analytics. Ultimately, this review highlights how multi-sensor systems can redefine early health status prediction, paving the way for proactive healthcare strategies that significantly improve patient outcomes and optimize healthcare delivery.</div></div>","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 4","pages":"Article 103000"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The future of cardiology: Integrating single-cell transcriptomics with multi-omics for enhanced cardiac disease insights
IF 3 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Current Problems in Cardiology
Pub Date : 2025-01-31 DOI: 10.1016/j.cpcardiol.2025.103005
Zhengchun Tang , Faris Alrumaihi , Wanian M. Alwanian , Hajed Obaid Alharbi , Khaled S. Allemailem , Mohammed Alissa , Omar Alasmari , Saad Almousa , Thomas Ainsworth , Xiangmei Chen
Recent advancements in single-cell transcriptome sequencing (scRNA-seq) have revolutionized our understanding of cellular heterogeneity in cardiovascular diseases, enabling the identification of novel therapeutic targets. This technology allows for high-resolution analysis of gene expression at the single-cell level, revealing the complex dynamics of human heart cell development and the diverse roles of cardiac cell types in health and disease. Despite its transformative potential, current applications of scRNA-seq face limitations, including challenges in data integration and the need for comprehensive multi-omic approaches to fully elucidate the mechanisms underlying cardiovascular pathologies. This review highlights the significant insights gained from scRNA-seq studies in the mammalian heart, emphasizing the importance of integrating spatial transcriptomics and other omics technologies to enhance our understanding of cardiac biology. Furthermore, it addresses the critical research gaps in the field, particularly in the context of personalized medicine and the need for improved methodologies to analyze rare cell populations. By exploring these challenges and opportunities, this review aims to pave the way for innovative diagnostic and therapeutic strategies that can ultimately improve outcomes for patients with cardiovascular diseases.
{"title":"The future of cardiology: Integrating single-cell transcriptomics with multi-omics for enhanced cardiac disease insights","authors":"Zhengchun Tang ,&nbsp;Faris Alrumaihi ,&nbsp;Wanian M. Alwanian ,&nbsp;Hajed Obaid Alharbi ,&nbsp;Khaled S. Allemailem ,&nbsp;Mohammed Alissa ,&nbsp;Omar Alasmari ,&nbsp;Saad Almousa ,&nbsp;Thomas Ainsworth ,&nbsp;Xiangmei Chen","doi":"10.1016/j.cpcardiol.2025.103005","DOIUrl":"10.1016/j.cpcardiol.2025.103005","url":null,"abstract":"<div><div>Recent advancements in single-cell transcriptome sequencing (scRNA-seq) have revolutionized our understanding of cellular heterogeneity in cardiovascular diseases, enabling the identification of novel therapeutic targets. This technology allows for high-resolution analysis of gene expression at the single-cell level, revealing the complex dynamics of human heart cell development and the diverse roles of cardiac cell types in health and disease. Despite its transformative potential, current applications of scRNA-seq face limitations, including challenges in data integration and the need for comprehensive multi-omic approaches to fully elucidate the mechanisms underlying cardiovascular pathologies. This review highlights the significant insights gained from scRNA-seq studies in the mammalian heart, emphasizing the importance of integrating spatial transcriptomics and other omics technologies to enhance our understanding of cardiac biology. Furthermore, it addresses the critical research gaps in the field, particularly in the context of personalized medicine and the need for improved methodologies to analyze rare cell populations. By exploring these challenges and opportunities, this review aims to pave the way for innovative diagnostic and therapeutic strategies that can ultimately improve outcomes for patients with cardiovascular diseases.</div></div>","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 4","pages":"Article 103005"},"PeriodicalIF":3.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
类型
全部 化学•材料 生命科学 医学 物理 工程技术 环境•农林 材料科学 地球科学 法学 管理学 化学 环境科学与生态学 计算机科学 教育学 经济学 农林科学 人文科学 生物学 数学 物理与天体物理 心理学 综合性期刊 其他 工业工程 理学 历史学 农学 文学 信息工程
数据库
全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley
期刊
Current Problems in Cardiology
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
﹀
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们：info@booksci.cn
Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1