Evidence map›Paper›PMID 35497005›Full record

ArticleRSC advances2022

Colloidal silver-based lateral flow immunoassay for detection of profenofos pesticide residue in vegetables.

Kuo-Hui Wu, Wen-Chien Huang, Shu-Chen Chang, Rong-Hwa Shyu

Open access · goldAbstract read
In one paragraph

Article in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.5field-weighted citation impact, top 20% 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

8 citing papers in PubMed, 19 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

4 authors at 2 institutions in 1 country.

Kuo-Hui WuDepartment of Chemical and Materials Engineering, Chung Cheng Institute of Technology, National Defense University Taoyuan 33551 Taiwan ccitkhwu@gmail.com.ORCID https://orcid.org/0000-0003-3933-7199
Wen-Chien HuangDepartment of Chemical and Materials Engineering, Chung Cheng Institute of Technology, National Defense University Taoyuan 33551 Taiwan ccitkhwu@gmail.com.ORCID https://orcid.org/0000-0002-8163-3314
Shu-Chen ChangApplied Zoology Division, Taiwan Agricultural Research Institute Taichung 41362 Taiwan.
Rong-Hwa ShyuInstitute of Preventive Medicine, National Defense Medical Center 90048 Taipei Taiwan.
National Defense University · TWNational Defense Medical Center · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A colloidal silver nanoparticle (AgNP)-based lateral flow immunoassay (LFIA) was evaluated in terms of the rapid detection of profenofos (PEO) pesticide residue in vegetables. Colloidal AgNPs, of a diameter of approximately 20 nm, were surface-modified with trisodium citrate dehydrate (TSC) in order to improve their stability and dispersion. An anti-profenofos polyclonal antibody (pAb) was successfully immobilized on the surface of the AgNPs by ionic interaction and characterized using UV-vis, SEM, TEM, FTIR and XPS analyses. Surface modification of Ag-pAb conjugates of varying pH, pAb content and cross-reactivity was employed to design and prepare labels for use in an LFIA to examine whether these factors affect the performance of the assay. The visible detection limit and optical detection limit of the PEO test strip were 0.20 and 0.01 ppm, respectively, in PEO standard solution. This assay showed no cross-reaction with omethoate, methamidophos or pyraclofos. Finally, the PEO test strip was effectively applied for the detection of PEO in liquid vegetables A and B, with optical detection limits of 0.09 and 0.075 ppm, respectively.

Identifiers

PMID35497005
PMCPMC9052933
OpenAlexW4225292567

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.