Evidence map›Paper›PMID 32111217›Full record

ArticleJournal of nanobiotechnology2020

Surface chemistry modified upconversion nanoparticles as fluorescent sensor array for discrimination of foodborne pathogenic bacteria.

Mingyuan Yin, Chuang Jing, Haijie Li, Qiliang Deng, Shuo Wang

Abstract read
In one paragraph

Article in Journal of nanobiotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
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  5. Rapid and Sensitive Fluorescence Detection ofFoods (Basel, Switzerland) · 2023
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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

5 authors.

Mingyuan YinState Key Laboratory of Food Nutrition and Safety, School of Food Engineering and Biotechnology, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin, 300457, People's Republic of China.
Chuang JingState Key Laboratory of Food Nutrition and Safety, School of Food Engineering and Biotechnology, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin, 300457, People's Republic of China.
Haijie LiState Key Laboratory of Food Nutrition and Safety, School of Food Engineering and Biotechnology, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin, 300457, People's Republic of China.
Qiliang DengState Key Laboratory of Food Nutrition and Safety, School of Food Engineering and Biotechnology, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin, 300457, People's Republic of China. yhdql@tust.edu.cn.
Shuo WangState Key Laboratory of Food Nutrition and Safety, School of Food Engineering and Biotechnology, College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin, 300457, People's Republic of China. wangshuo@nankai.edu.cn.

Funding

National Natural Science Foundation of China 21375094
6 · The paper itself

Abstract

backgroundThe identification of foodborne pathogenic bacteria types plays a crucial role in food safety and public health. In consideration of long culturing times, tedious operations and the desired specific recognition elements in conventional methods, the alternative fluorescent sensor arrays can offer a high-effective approach in bacterial identification by using multiple cross-reactive receptors. Herein, we achieve this goal by constructing an upconversion fluorescent sensor array based on anti-stokes luminogens featuring a series of functional lanthanide-doped upconversion nanoparticles (UCNPs) with phenylboronic acid, phosphate groups, or imidazole ionic liquid. The prevalent spotlight effect of microorganism and the electrostatic interaction between UCNPs and bacteria endow such sensor array an excellent discrimination property.

resultsSeven common foodborne pathogenic bacteria including two Gram-positive bacteria (Staphylococcus aureus and Listeria monocytogenes) and five Gram-negative bacteria (Escherichia coli, Salmonella, Cronobacter sakazakii, Shigella flexneri and Vibrio parahaemolyticus) are precisely identified with 100% accuracy via linear discriminant analysis (LDA). Furthermore, blends of bacteria have been identified accurately. Bacteria in real samples (tap water, milk and beef) have been effectively discriminated with 92.1% accuracy.

conclusionsCurrent fluorescence sensor array is a powerful tool for high-throughput bacteria identification, which overcomes the time-consuming bacteria culture and heavy dependence of specific recognition elements. The high efficiency of whole bacterial cell detection and the discrimination capability of life and death bacteria can brighten the application of fluorescence sensor array.

Indexed as

Food MicrobiologyAnimalsBacteriaFluorescenceFoodborne DiseasesFood ContaminationMilkMycological Typing TechniquesNanoparticlesRed MeatWater MicrobiologyFoodborne bacteriaIdentificationSensor arrayUpconversion nanoparticles

Identifiers

PMID32111217
PMCPMC7049179

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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.