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      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Predicting carbon spectrum in heteronuclear single quantum coherence spectroscopy for online feedback during surgery

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      Author(s)
      Karakaşlar, Emin Onur
      Advisor
      Çiçek, A. Ercüment
      Date
      2020-09
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      1H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra (1H-13C). Unfortunately, this analysis requires much longer time and prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the 1H and 13C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the 13C dimension by just performing 1H HRMAS NMR experiment. We show on a rat model of central nervous system tissues (80 samples, 5 tissues) that our methods achieve 0.971 and 0.957 mean R2 values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 groups of 39 metabolites with 97% accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension. In practice, this information can provide valuable feedback to the surgeon to further resect the cavity to avoid potential recurrence.
      Keywords
      Metabolomics
      HRMAS NMR
      HSQC NMR
      Permalink
      http://hdl.handle.net/11693/54195
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      • Dept. of Computer Engineering - Master's degree 566
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