Evidence map›Paper›PMID 37300621›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

A Microfluidic SERS Assay to Characterize the Phenotypic Heterogeneity in Cancer-Derived Small Extracellular Vesicles.

Jing Wang, Matt Trau, Alain Wuethrich

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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.

Jing WangKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China.
Matt TrauCentre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, Australia.
Alain WuethrichCentre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, Australia. a.wuethrich@uq.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small extracellular vesicles (sEVs) are nanoscopic bioparticles that transport biomolecular cargoes between cells. sEVs have been implicated in many pathological processes such as cancer, rendering them as promising targets for therapeutics and diagnostics. Characterizing phenotypic differences in sEV biomolecular cargos could support understanding their roles in cancer. However, this is difficult due to similar physical properties of sEVs and requirement for highly sensitive analysis. Our method describes the preparation and operation of a microfluidic immunoassay with surface-enhanced Raman scattering (SERS) readouts, termed sEV subpopulation characterization platform (ESCP). ESCP applies an alternating current induced electrohydrodynamic flow to enhance collisions of sEVs with the antibody-functionalized sensor surface. Captured sEVs are labeled with plasmonic nanoparticles to facilitate multiplexed and highly sensitive phenotypic characterization of sEVs by SERS. ESCP is demonstrated for characterizing the expression of three tetraspanins (CD9, CD63, CD81) and four cancer-associated biomarkers (MCSP, MCAM, ErbB3, LNGFR) in sEVs derived from cancer cell lines and plasma samples.

Indexed as

Extracellular VesiclesNeoplasmsBiomarkers, TumorElectricityHumansMicrofluidicsBiomarkers, TumorBiosensorCancer diagnosticsExtracellular vesiclesMicrofluidicsSurface-enhanced Raman scattering

Identifiers

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.