Evidence map›Paper›PMID 38516065›Full record

ArticleChemical science2024

AI-assisted mass spectrometry imaging with

Cong-Lin Zhao, Han-Zhang Mou, Jian-Bin Pan, Lei Xing, Yuxiang Mo, Bin Kang, Hong-Yuan Chen, Jing-Juan Xu

Abstract read
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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
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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

8 authors.

Cong-Lin ZhaoState Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China xl1992@nju.edu.cn binkang@nju.edu.cn xujj@nju.edu.cn.
Han-Zhang MouState Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China xl1992@nju.edu.cn binkang@nju.edu.cn xujj@nju.edu.cn.
Jian-Bin PanState Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China xl1992@nju.edu.cn binkang@nju.edu.cn xujj@nju.edu.cn.
Lei XingState Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China xl1992@nju.edu.cn binkang@nju.edu.cn xujj@nju.edu.cn.
Yuxiang MoState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University Beijing 100084 China.ORCID https://orcid.org/0000-0002-8499-7623
Bin KangState Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China xl1992@nju.edu.cn binkang@nju.edu.cn xujj@nju.edu.cn.
Hong-Yuan ChenState Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China xl1992@nju.edu.cn binkang@nju.edu.cn xujj@nju.edu.cn.
Jing-Juan XuState Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China xl1992@nju.edu.cn binkang@nju.edu.cn xujj@nju.edu.cn.ORCID https://orcid.org/0000-0001-9579-9318

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with

Identifiers

PMID38516065
PMCPMC10952063

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.