Evidence map›Paper›PMID 35650221›Full record

ArticleScientific reports2022

Rapid imaging of lung cancer using a red fluorescent probe to detect dipeptidyl peptidase 4 and puromycin-sensitive aminopeptidase activities.

Shun Kawashima, Daisuke Yoshida, Takafusa Yoshioka, Akira Ogasawara, Kyohhei Fujita, Masahiro Yanagiya, Masaaki Nagano, Chihiro Konoeda, Haruaki Hino, Kentaro Kitano and 7 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

6 citing papers in PubMed, 9 citations in OpenAlex.

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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

17 authors at 2 institutions in 1 country.

Shun KawashimaLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Daisuke YoshidaLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Takafusa YoshiokaLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Akira OgasawaraGraduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan.
Kyohhei FujitaLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masahiro YanagiyaDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masaaki NaganoDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Chihiro KonoedaDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Haruaki HinoLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kentaro KitanoDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masaaki SatoDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Rumi HinoDepartment of Sports and Health Science, Daito Bunka University, Saitama, Japan.
Ryosuke KojimaLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Toru KomatsuGraduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan.
Mako KamiyaLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yasuteru UranoLaboratory of Chemical Biology and Molecular Imaging, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. uranokun@m.u-tokyo.ac.jp.
Jun NakajimaDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. nakajima-tho@h.u-tokyo.ac.jp.
The University of Tokyo · JPDaito Bunka University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid identification of lung-cancer micro-lesions is becoming increasingly important to improve the outcome of surgery by accurately defining the tumor/normal tissue margins and detecting tiny tumors, especially for patients with low lung function and early-stage cancer. The purpose of this study is to select and validate the best red fluorescent probe for rapid diagnosis of lung cancer by screening a library of 400 red fluorescent probes based on 2-methyl silicon rhodamine (2MeSiR) as the fluorescent scaffold, as well as to identify the target enzymes that activate the selected probe, and to confirm their expression in cancer cells. The selected probe, glutamine-alanine-2-methyl silicon rhodamine (QA-2MeSiR), showed 96.3% sensitivity and 85.2% specificity for visualization of lung cancer in surgically resected specimens within 10 min. In order to further reduce the background fluorescence while retaining the same side-chain structure, we modified QA-2MeSiR to obtain glutamine-alanine-2-methoxy silicon rhodamine (QA-2OMeSiR). This probe rapidly visualized even borderline lesions. Dipeptidyl peptidase 4 and puromycin-sensitive aminopeptidase were identified as enzymes mediating the cleavage and consequent fluorescence activation of QA-2OMeSiR, and it was confirmed that both enzymes are expressed in lung cancer. QA-2OMeSiR is a promising candidate for clinical application.

Indexed as

Fluorescent DyesLung NeoplasmsAlanineAminopeptidasesDipeptidyl Peptidase 4GlutamineHumansRhodaminesSiliconAlanineAminopeptidasesDipeptidyl Peptidase 4enkephalin degrading enzymeFluorescent DyesGlutamineRhodaminesSilicon

Identifiers

PMID35650221
PMCPMC9160295
OpenAlexW4281731601

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.