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, overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC)-NMR is applied to distinguish metabolites in 2D spectra. Unfortunately, this requires much longer time and prohibits real time analysis. 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 HRMAS-NMR experiment. We show on a rat model of central nervous system that our methods achieve 0.971 and 0.957 mean R2 values, respectively. Our tests on human brain tumor samples show that we can predict 104 peaks of 39 metabolites with 97.1% accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension.

中文翻译：

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在异核单量子相干光谱学中预测碳谱，以在手术期间在线反馈。

1H高分辨率魔角旋转（HRMAS）核磁共振（NMR）是用于检测实体组织中代谢物的可靠技术。快速的响应时间可以实时指导外科医生，以检测残留在切除腔中的肿瘤细胞。然而，一维信号中的频谱共振重叠经常使区分代谢物变得不可能。在那种情况下，应用异核单量子相干光谱法（HSQC）-NMR来区分2D光谱中的代谢物。不幸的是，这需要更长的时间并且禁止实时分析。在这项研究中，我们表明使用多元多元回归和统计总相关光谱，我们可以了解1H和13C尺寸之间的关系。只需少量样本即可进行学习，而无需执行HSQC分析，我们只需执行HRMAS-NMR实验即可预测13C尺寸。我们在中枢神经系统的大鼠模型上显示，我们的方法分别达到0.971和0.957的平均R2值。我们对人脑肿瘤样品的测试表明，我们可以预测39种代谢物的104个峰，准确度为97.1％。最后，我们表明，尽管在1H维度上信号受阻，我们仍可以预测耐药性肿瘤生物标志物（肌酸）的存在。1％的准确性。最后，我们表明，尽管在1H维度上信号受阻，我们仍可以预测耐药性肿瘤生物标志物（肌酸）的存在。1％的准确性。最后，我们表明尽管在1H方向信号受阻，我们仍可以预测耐药性肿瘤生物标志物（肌酸）的存在。