Evidence map›Paper›PMID 36584356›Full record

ArticleACS sensors2023

Multiarray Biosensor for Diagnosing Lung Cancer Based on Gap Plasmonic Color Films.

Thanh Mien Nguyen, Jae Heun Chung, Gyeong-Ha Bak, You Hwan Kim, Minjun Kim, Ye-Ji Kim, Ryuk Jun Kwon, Eun-Jung Choi, Kwang Ho Kim, Yun Seong Kim and 1 more

Abstract read
In one paragraph

Article in ACS sensors, 2023. 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. 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

11 authors.

Thanh Mien NguyenBio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea.ORCID 0000-0002-8137-4455
Jae Heun ChungDepartment of Internal Medicine, College of Medicine, Pusan National University, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea.
Gyeong-Ha BakDepartment of Nano Fusion Technology, Pusan National University, Busan 46241, Republic of Korea.
You Hwan KimDepartment of Nano Fusion Technology, Pusan National University, Busan 46241, Republic of Korea.
Minjun KimDepartment of Physics, Chungnam National University, Daejeon 34134, Republic of Korea.
Ye-Ji KimDepartment of Nano Fusion Technology, Pusan National University, Busan 46241, Republic of Korea.ORCID 0000-0002-8710-6355
Ryuk Jun KwonFamily Medicine Clinic and Research Institute of Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Beomeo-ri, Mulgeum-eup, Yangsan, Gyeongsangnam-do 50612, Republic of Korea.
Eun-Jung ChoiBio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea.
Kwang Ho KimSchool of Materials Science and Engineering, Pusan National University, Busan 46241, Republic of Korea.
Yun Seong KimDepartment of Internal Medicine, College of Medicine, Pusan National University, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea.
Jin-Woo OhBio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea.ORCID 0000-0002-0065-5088

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adaptable and sensitive materials are essential for the development of advanced sensor systems such as bio and chemical sensors. Biomaterials can be used to develop multifunctional biosensor applications using genetic engineering. In particular, a plasmonic sensor system using a coupled film nanostructure with tunable gap sizes is a potential candidate in optical sensors because of its simple fabrication, stability, extensive tuning range, and sensitivity to small changes. Although this system has shown a good ability to eliminate humidity as an interferant, its performance in real-world environments is limited by low selectivity. To overcome these issues, we demonstrated the rapid response of gap plasmonic color sensors by utilizing metal nanostructures combined with genetically engineered M13 bacteriophages to detect volatile organic compounds (VOCs) and diagnose lung cancer from breath samples. The M13 bacteriophage was chosen as a recognition element because the structural protein capsid can readily be modified to target the desired analyte. Consequently, the VOCs from various functional groups were distinguished by using a multiarray biosensor based on a gap plasmonic color film observed by hierarchical cluster analysis. Furthermore, the lung cancer breath samples collected from 70 healthy participants and 50 lung cancer patients were successfully classified with a high rate of over 89% through supporting machine learning analysis.

Indexed as

Biosensing TechniquesLung NeoplasmsNanostructuresVolatile Organic CompoundsBacteriophage M13HumansVolatile Organic Compoundsbreath analysisgap plasmonic resonancelung cancer diagnosisM13 bacteriophagevolatile organic compounds (VOCs)

Identifiers

PMID36584356
PMCPMC9887647

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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.