Evidence map›Paper›PMID 33289759›Full record

ArticleLab on a chip2021

Hyperspectral imaging-based exosome microarray for rapid molecular profiling of extracellular vesicles.

Yifei Wang, Qinming Zhang, Wang Yuan, Yixuan Wang, Hannah J Loghry, Zijian Zhao, Michael J Kimber, Liang Dong, Meng Lu

Open access · hybridAbstract read
In one paragraph

Article in Lab on a chip, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 22% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
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

9 authors at 1 institution in 1 country.

Yifei WangDepartment of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA. ldong@iastate.edu menglu@iastate.edu.
Qinming Zhang
Wang Yuan
Yixuan Wang
Hannah J Loghry
Zijian Zhao
Michael J Kimber
Liang Dong
Meng Lu
Iowa State University · US

Funding

Exploring the Structure and Function of Parasitic Nematode ExosomesR21AI117204 · NIAID · IOWA STATE UNIVERSITY · PI KIMBER, MICHAEL JOHN · 2016 to 2017
$403k
NIAID NIH HHS R21 AI117204
6 · The paper itself

Abstract

One of the challenges of exploiting extracellular vesicles (EVs) as a disease biomarker is to differentiate EVs released by similar cell types or phenotypes. This paper reports a high-throughput and label-free EV microarray technology to differentiate EVs by simultaneous characterization of a panel of EV membrane proteins. The EsupplV microarray platform, which consists of an array of antibodies printed on a photonic crystal biosensor and a microscopic hyperspectral imaging technique, can rapidly assess the binding of the EV membrane proteins with their corresponding antibodies. The EV microarray assay requires only a 2 μL sample volume and a detection time of less than 2 h. The EV microarray assay was validated by not only quantifying seven membrane proteins carried by macrophage-derived EVs but also distinguishing the EVs secreted by three macrophage phenotypes. In particular, the EV microarray technology can generate a molecular fingerprint of target EVs that can be used to identify the EVs' parental cells, and thus has utility for basic science research as well as for point-of-care disease diagnostics and therapeutics.

Indexed as

ExosomesExtracellular VesiclesAntibodiesHyperspectral ImagingMacrophagesAntibodies

Identifiers

PMID33289759
PMCPMC7785694
OpenAlexW3109853733

What OpenQuestion holds

Textmetadata
LicenceTDM
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.