Evidence map›Paper›PMID 28217434›Full record

ArticleNanotheranostics2017

Gold Nanoparticle Based Platforms for Circulating Cancer Marker Detection.

Xiaohua Huang, Ryan O'Connor, Elyahb Allie Kwizera

Abstract read
In one paragraph

Article in Nanotheranostics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–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

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Xiaohua HuangDepartment of Chemistry, The University of Memphis, Memphis, TN 38152.
Ryan O'ConnorDepartment of Chemistry, The University of Memphis, Memphis, TN 38152.
Elyahb Allie KwizeraDepartment of Chemistry, The University of Memphis, Memphis, TN 38152.

Funding

Hybrid nanoparticles for capture and detection of circulating tumor cellsR15CA195509 · NCI · UNIVERSITY OF MEMPHIS · PI HUANG, XIAOHUA · 2015 to 2015
$405k
NCI NIH HHS R15 CA195509
6 · The paper itself

Abstract

Detection of cancer-related circulating biomarkers in body fluids has become a cutting-edge technology that has the potential to noninvasively screen cancer, diagnose cancer at early stage, monitor tumor progression, and evaluate therapy responses. Traditional molecular and cellular detection methods are either insensitive for early cancer intervention or technically costly and complicated making them impractical for typical clinical settings. Due to their exceptional structural and functional properties that are not available from bulk materials or discrete molecules, nanotechnology is opening new horizons for low cost, rapid, highly sensitive, and highly specific detection of circulating cancer markers. Gold nanoparticles have emerged as a unique nanoplatform for circulating biomarker detection owning to their advantages of easy synthesis, facile surface chemistry, excellent biocompatibility, and remarkable structure and environment sensitive optical properties. In this review, we introduce current gold nanoparticle-based technology platforms for the detection of four major classes of circulating cancer markers - circulating tumor cells, vesicles, nucleic acids, and proteins. The techniques will be summarized in terms of signal detection strategies. Distinctive examples are provided to highlight the state-of-the-art technologies that significantly advance basic and clinical cancer research.

Indexed as

circulating cancer marker detectioncirculating nucleic acidcirculating tumor cellextracellular vesicleGold nanoparticleprotein tumor marker

Identifiers

PMID28217434
PMCPMC5313055

What OpenQuestion holds

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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.