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Related Concept Videos

NMR Spectrometers: Resolution and Error Correction01:14

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Phase Model-Driven Deep Learning for Robust Phase Correction in High-Throughput NMR-Based Metabolomics.

Chuanwen Zhao1, Gang Chen1,2, Caixiang Liu1,2

  • 1State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.

The Journal of Physical Chemistry Letters
|January 9, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning method, Phase Model-Driven Residual Attention Network (PD-RAN), accurately corrects phase in high-throughput NMR metabolomics. This robust technique ensures reliable spectral analysis for large biological sample datasets.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Biology

Background:

  • High-throughput Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for metabolomics, enabling efficient and noninvasive analysis of biological samples.
  • Accurate phase correction of NMR spectra is essential for reliable quantitative analysis in metabolomics workflows.
  • Current phase correction methods may lack robustness and efficiency for large-scale, automated metabolomics studies.

Purpose of the Study:

  • To develop and validate a novel, robust, and efficient phase correction method for high-throughput NMR metabolomics.
  • To improve the accuracy and reliability of spectral data processing in complex biological samples.
  • To address the limitations of conventional phase correction techniques in automated, large-scale NMR analysis.

Main Methods:

  • Introduction of the Phase Model-Driven Residual Attention Network (PD-RAN), a hybrid deep learning approach.
  • Integration of a physically informed model with a residual attention network to learn low-dimensional phase features.
  • Application of PD-RAN to 1D NMR spectra from diverse metabolomics samples (brain extracts, plasma, urine).

Main Results:

  • PD-RAN demonstrated superior performance in phase correction compared to conventional methods across various sample types.
  • The method achieved high precision and reliability, producing pure absorption-mode spectra.
  • Exceptional processing speed was observed, with 1,000 spectra processed in 20 ms, highlighting its suitability for high-throughput applications.
  • Ablation studies confirmed the effectiveness of the phase model-driven component and the method's robustness to spectral artifacts like noise and baseline distortions.

Conclusions:

  • PD-RAN offers a significant advancement in NMR metabolomics data processing, providing accurate and efficient phase correction.
  • The physically informed deep learning approach enhances the reliability of quantitative metabolomics analysis.
  • This method is well-suited for the demands of modern high-throughput NMR metabolomics, facilitating scalable and consistent biological sample profiling.