Evidence map›Paper›PMID 28382160›Full record

ArticleTheranostics2017

Sensitive detection of protein biomarkers using silver nanoparticles enhanced immunofluorescence assay.

Li-Jun Zhao, Ru-Jia Yu, Wei Ma, Huan-Xing Han, He Tian, Ruo-Can Qian, Yi-Tao Long

Abstract readEvaluation Study
In one paragraph

Article in Theranostics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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  14. Array-Based Screening of Silver Nanoparticle Mineralization Peptides.International journal of molecular sciences · 2020
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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

7 authors.

Li-Jun ZhaoKey Laboratory for Advanced Materials and Department of Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, P. R. China.
Ru-Jia YuKey Laboratory for Advanced Materials and Department of Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, P. R. China.
Wei MaKey Laboratory for Advanced Materials and Department of Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, P. R. China.
Huan-Xing HanTranslational Medicine Center, Changzheng Hospital, the Second Military Medical University, P. R. China.
He TianKey Laboratory for Advanced Materials and Department of Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, P. R. China.
Ruo-Can QianKey Laboratory for Advanced Materials and Department of Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, P. R. China.
Yi-Tao LongKey Laboratory for Advanced Materials and Department of Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of biomarkers is extremely important for the early diagnosis of diseases. Here, we developed an easy and highly sensitive fluorescence detection system for the determination of biomarkers by combining the rapid separation of magnetic beads and silver nanoparticles labeled antibodies. An ultrasensitive silver ions fluorescence probe 3', 6'-bis (diethylamino)-2-(2-iodoethyl) spiro[isoindoline-1, 9'-xanthen]-3-one (Ag

Indexed as

BiomarkersDiagnostic Tests, RoutineFluorescent Antibody TechniqueHumansNanoparticlesProteinsSilverBiomarkersProteinsSilverbiomarkers.fluorescence detectionmagnetic beadssilver nanoparticles

Identifiers

PMID28382160
PMCPMC5381250

What OpenQuestion holds

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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.