超极化(13)C代谢物核磁共振实验的数学建模和数据分析。

Magnetic resonance insights Pub Date : 2013-02-24 eCollection Date: 2013-01-01 DOI:10.4137/MRI.S11084
Guilhem Pagès, Philip W Kuchel
{"title":"超极化(13)C代谢物核磁共振实验的数学建模和数据分析。","authors":"Guilhem Pagès,&nbsp;Philip W Kuchel","doi":"10.4137/MRI.S11084","DOIUrl":null,"url":null,"abstract":"<p><p>Rapid-dissolution dynamic nuclear polarization (DNP) has made significant impact in the characterization and understanding of metabolism that occurs on the sub-minute timescale in several diseases. While significant efforts have been made in developing applications, and in designing rapid-imaging radiofrequency (RF) and magnetic field gradient pulse sequences, very few groups have worked on implementing realistic mathematical/kinetic/relaxation models to fit the emergent data. The critical aspects to consider when modeling DNP experiments depend on both nuclear magnetic resonance (NMR) and (bio)chemical kinetics. The former constraints are due to the relaxation of the NMR signal and the application of 'read' RF pulses, while the kinetic constraints include the total amount of each molecular species present. We describe the model-design strategy we have used to fit and interpret our DNP results. To our knowledge, this is the first report on a systematic analysis of DNP data. </p>","PeriodicalId":74096,"journal":{"name":"Magnetic resonance insights","volume":"6 ","pages":"13-21"},"PeriodicalIF":0.0000,"publicationDate":"2013-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/MRI.S11084","citationCount":"15","resultStr":"{\"title\":\"Mathematical Modeling and Data Analysis of NMR Experiments using Hyperpolarized (13)C Metabolites.\",\"authors\":\"Guilhem Pagès,&nbsp;Philip W Kuchel\",\"doi\":\"10.4137/MRI.S11084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Rapid-dissolution dynamic nuclear polarization (DNP) has made significant impact in the characterization and understanding of metabolism that occurs on the sub-minute timescale in several diseases. While significant efforts have been made in developing applications, and in designing rapid-imaging radiofrequency (RF) and magnetic field gradient pulse sequences, very few groups have worked on implementing realistic mathematical/kinetic/relaxation models to fit the emergent data. The critical aspects to consider when modeling DNP experiments depend on both nuclear magnetic resonance (NMR) and (bio)chemical kinetics. The former constraints are due to the relaxation of the NMR signal and the application of 'read' RF pulses, while the kinetic constraints include the total amount of each molecular species present. We describe the model-design strategy we have used to fit and interpret our DNP results. To our knowledge, this is the first report on a systematic analysis of DNP data. </p>\",\"PeriodicalId\":74096,\"journal\":{\"name\":\"Magnetic resonance insights\",\"volume\":\"6 \",\"pages\":\"13-21\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4137/MRI.S11084\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Magnetic resonance insights\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4137/MRI.S11084\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2013/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic resonance insights","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/MRI.S11084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2013/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

摘要

快速溶解动态核极化(DNP)对几种疾病在亚分钟时间尺度上发生的代谢的表征和理解产生了重大影响。虽然在开发应用和设计快速成像射频(RF)和磁场梯度脉冲序列方面已经做出了重大努力,但很少有小组致力于实现现实的数学/动力学/松弛模型来适应紧急数据。DNP实验建模时要考虑的关键方面取决于核磁共振(NMR)和(生物)化学动力学。前一种约束是由于核磁共振信号的松弛和“读”射频脉冲的应用,而动力学约束包括存在的每个分子物种的总量。我们描述了我们用来拟合和解释DNP结果的模型设计策略。据我们所知,这是第一份对DNP数据进行系统分析的报告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mathematical Modeling and Data Analysis of NMR Experiments using Hyperpolarized (13)C Metabolites.

Mathematical Modeling and Data Analysis of NMR Experiments using Hyperpolarized (13)C Metabolites.

Mathematical Modeling and Data Analysis of NMR Experiments using Hyperpolarized (13)C Metabolites.

Mathematical Modeling and Data Analysis of NMR Experiments using Hyperpolarized (13)C Metabolites.

Rapid-dissolution dynamic nuclear polarization (DNP) has made significant impact in the characterization and understanding of metabolism that occurs on the sub-minute timescale in several diseases. While significant efforts have been made in developing applications, and in designing rapid-imaging radiofrequency (RF) and magnetic field gradient pulse sequences, very few groups have worked on implementing realistic mathematical/kinetic/relaxation models to fit the emergent data. The critical aspects to consider when modeling DNP experiments depend on both nuclear magnetic resonance (NMR) and (bio)chemical kinetics. The former constraints are due to the relaxation of the NMR signal and the application of 'read' RF pulses, while the kinetic constraints include the total amount of each molecular species present. We describe the model-design strategy we have used to fit and interpret our DNP results. To our knowledge, this is the first report on a systematic analysis of DNP data.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信