Evidence map›Paper›PMID 34640857›Full record

ArticleSensors (Basel, Switzerland)2021

Copper Oxide Nanoparticle-Based Immunosensor for Zearalenone Analysis by Combining Automated Sample Pre-Processing and High-Throughput Terminal Detection.

Zhihong Xuan, Yanxiang Wu, Hongmei Liu, Li Li, Jin Ye, Songxue Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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
–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

6 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

6 authors.

Zhihong XuanAcademy of National Food and Strategic Reserves Administration, No.11 Baiwanzhuang Str, Xicheng District, Beijing 100037, China.
Yanxiang WuAcademy of National Food and Strategic Reserves Administration, No.11 Baiwanzhuang Str, Xicheng District, Beijing 100037, China.
Hongmei LiuAcademy of National Food and Strategic Reserves Administration, No.11 Baiwanzhuang Str, Xicheng District, Beijing 100037, China.
Li LiAcademy of National Food and Strategic Reserves Administration, No.11 Baiwanzhuang Str, Xicheng District, Beijing 100037, China.
Jin YeAcademy of National Food and Strategic Reserves Administration, No.11 Baiwanzhuang Str, Xicheng District, Beijing 100037, China.ORCID 0000-0001-9619-4943
Songxue WangAcademy of National Food and Strategic Reserves Administration, No.11 Baiwanzhuang Str, Xicheng District, Beijing 100037, China.

Funding

National Key Research and Development Program of China 2018YFC1602102-1National Natural Science Foundation of China 81803712, 31901806the Fundamental Research Funds for the Academy of National Food and Strategic Re-serves Administration ZX1913the Key R&D Program of Guangxi AB20999005
6 · The paper itself

Abstract

A rapid and high-throughput fluorescence detection method for zearalenone (ZEN) based on a CuO nanoparticle (NP)-assisted signal amplification immunosensor was developed using an automated sample pretreatment and signal conversion system. CuO NPs with high stability and biocompatibility were used as carriers to immobilize anti-ZEN antibodies. The obtained CuO NP-anti-ZEN can maintain the ability to recognize target toxins and act as both a signal source and carrier to achieve signal conversion using automated equipment. In this process, target toxin detection is indirectly transformed to Cu

Indexed as

Biosensing TechniquesNanoparticlesZearalenoneChromatography, LiquidCopperImmunoassayOxidesTandem Mass SpectrometryCopperOxidesZearalenoneautomatic signal conversionCuO NPshigh-throughput pre-treatmentzearalenone

Identifiers

PMID34640857
PMCPMC8512712

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.