Evidence map›Paper›PMID 25092261›Full record

ArticleScientific reports2014

Rapid and reagentless detection of microbial contamination within meat utilizing a smartphone-based biosensor.

Pei-Shih Liang, Tu San Park, Jeong-Yeol Yoon

Abstract read
In one paragraph

Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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  12. Selective UropathogenicSensors (Basel, Switzerland) · 2018
    Article
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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

3 authors.

Pei-Shih LiangDepartment of Agricultural and Biosystems Engineering, The University of Arizona, Tucson, Arizona 85721-0038, USA.
Tu San ParkDepartment of Agricultural and Biosystems Engineering, The University of Arizona, Tucson, Arizona 85721-0038, USA.
Jeong-Yeol YoonDepartment of Agricultural and Biosystems Engineering, The University of Arizona, Tucson, Arizona 85721-0038, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A smartphone-utilized biosensor was developed for detecting microbial spoilage on ground beef, without using antibodies, microbeads or any other reagents, towards a preliminary screening tool for microbial contamination on meat products, and potentially towards wound infection. Escherichia coli K12 solutions (10(1)-10(8) CFU/mL) were added to ground beef products to simulate microbial spoilage. An 880 nm near infrared LED was irradiated perpendicular to the surface of ground beef, and the scatter signals at various angles were evaluated utilizing the gyro sensor and the digital camera of a smartphone. The angle that maximized the Mie scatter varied by the E. coli concentration: 15° for 10(8) CFU/mL, 30° for 10(4) CFU/mL, and 45° for 10 CFU/mL, etc. SEM and fluorescence microscopy experiments revealed that the antigens and cell fragments from E. coli bonded preferably to the fat particles within meat, and the size and morphologies of such aggregates varied by the E. coli concentration.

Indexed as

Adipose TissueAnimalsAntigens, BacterialBacterial ProteinsBiosensing TechniquesCattleCell PhoneEscherichia coliFood ContaminationInfrared RaysMeat ProductsProtein BindingSensitivity and SpecificityAntigens, BacterialBacterial Proteins

Identifiers

PMID25092261
PMCPMC4121612

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.