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Une approche fondée sur la mécanique quantique pour l’optimisation de bases de metabolites : application à la quantification de spectres RMN-HRMAS

机译:一种基于量子力学的代谢物碱基优化方法:在NMR-HRMAS光谱定量中的应用

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摘要

From day to day, the role of HRMAS (High-Resolution Magic Angle Sinning) Nuclear Magnetic Resonance Spectroscopy (NMRS) in medical diagnosis is increasing. This technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. Automatic spectrum quantitation enables monitoring of diseases. However for several metabolites, the values of chemical shifts of proton groups may slightly differ according to the micro-environment in the tissue or cells, in particular to its pH. This hampers accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis-set. The present word is devoted to the optimization of NMR metabolite basis set signals, particularly to the algorithms of chemical shift mismatch correction. Two sighal processing (“warping”) methods were developed for simple and fast spectrum optimization : signal stretching/shrinking (resampling) and spectrum splitting. Then, another optimization method, QM-QUEST, coupling Quantrum Mechanical simulation and quantitation algorithms was implemented. The latter provides more robust fitting while limiting user involvement and respects the correct fingerprints of metabolites. Its efficiency is demonstrated by accurately quantitating signals from tissue samples of human brains with oligodendroglioma, obtained at 11.7 Tesla and spectra of cells acquired at 9.4T by HRMAS-NMR. As the necessity of fast NMR signal simulation based on quantum Mechanics is raised in the thesis, a part of the word is dedicated to an approximate method speeding-up the calculations. The algorithm based on spin-system fragmentation could become an important part of the QM-QUEST optimization method and will be implemented as an option of simulation in NMR-SCOPE, module of the jMRUI software package.
机译:每天,HRMAS(高分辨率魔角正弦)核磁共振波谱(NMRS)在医学诊断中的作用越来越大。该技术能够建立离体病理组织和健康组织的代谢产物谱。自动频谱定量可以监控疾病。但是,对于几种代谢物,质子基团的化学位移值可能会根据组织或细胞的微环境(尤其是其pH值)而略有不同。这主要在使用基于代谢物基集的定量算法时妨碍了对代谢物浓度的准确估算。本词专门用于NMR代谢物基集信号的优化,特别是化学位移失配校正的算法。为了简单,快速地进行频谱优化,开发了两种叹息处理(“扭曲”)方法:信号拉伸/收缩(重采样)和频谱分裂。然后,实现了另一种优化方法QM-QUEST,结合了Quantrum Mechanical模拟和定量算法。后者在限制用户参与的同时提供了更坚固的贴合性,并尊重代谢产物的正确指纹。通过从11.7特斯拉获得的少突胶质细胞瘤的人脑组织样品中的信号进行准确定量,并通过HRMAS-NMR在9.4T处获得的细胞光谱,可以准确地定量其信号。由于提出了基于量子力学的快速NMR信号仿真的必要性,因此该词的一部分专用于加快计算速度的近似方法。基于自旋系统碎片的算法可能会成为QM-QUEST优化方法的重要组成部分,并将作为jMRUI软件包模块NMR-SCOPE中的仿真选项实现。

著录项

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    Lazariev, Andrii;

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  • 年度 2011
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  • 原文格式 PDF
  • 正文语种 en
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