Evidence map›Paper›PMID 39568866›Full record

ArticleChemical science2024

Deep learning enabled ultra-high quality NMR chemical shift resolved spectra.

Zhengxian Yang, Weigang Cai, Wen Zhu, Xiaoxu Zheng, Xiaoqi Shi, Mengjie Qiu, Zhong Chen, Maili Liu, Yanqin Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027
    Review
  2. Unmasking DNA Resonances by Suppression of Hyperpolarized Water.Journal of the American Chemical Society · 2026
    Article
  3. Article
  4. Deep Learning Assisted Proton Pure Shift NMR Spectroscopy.Journal of the American Chemical Society · 2026
    Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhengxian YangDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.
Weigang CaiDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.
Wen ZhuDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.
Xiaoxu ZhengDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.
Xiaoqi ShiDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.
Mengjie QiuDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.
Zhong ChenDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.ORCID https://orcid.org/0000-0002-1473-2224
Maili LiuState Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences Wuhan 430071 China.
Yanqin LinDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University Xiamen Fujian 361005 China linyq@xmu.edu.cn.ORCID https://orcid.org/0000-0003-0596-0740

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High quality chemical shift resolved spectra have long been pursued in nuclear magnetic resonance (NMR). In order to obtain chemical shift information with high resolution and sensitivity, a neural network named spin echo to obtain chemical shifts network (SE2CSNet) is developed to process the NMR data acquired by the spin echo pulse sequence. Through detecting the change of phase in the spin echo spectra, SE2CSNet can accurately detect the chemical shift position of spectral signals. The results show that the network can discern the chemical shift even when spectral signals overlap, but without strong coupling and chunking artifacts. In addition, this method can process the sample with low S/N (signal to noise ratio), and recover weak signals even hidden in noise, leading to ultra-high quality chemical shift resolved spectra. It is envisioned that the proposed methodology will find wide applications in many fields.

Identifiers

PMID39568866
PMCPMC11575604

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