Evidence map›Paper›PMID 41741789›Full record

ArticleMikrochimica acta2026

Extracellular vesicle-induced silver-nanoparticle aggregation (EVISA) assay for rapid measurement of EV concentration.

Kara Cook, Neyva Daniela Cuatepotzo Vega, Huiyan Li

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In one paragraph

Article in Mikrochimica acta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kara CookCollege of Engineering, University of Guelph, Guelph, ON, N1G2W1, Canada.
Neyva Daniela Cuatepotzo VegaCollege of Engineering, University of Guelph, Guelph, ON, N1G2W1, Canada.
Huiyan LiCollege of Engineering, University of Guelph, Guelph, ON, N1G2W1, Canada. huiyanli@uoguelph.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) are central mediators of intercellular communication and disease progression, with important implications for diagnostics and therapeutics. EV concentration often increases in disease, making it not only a measure of experimental reliability and reproducibility but also a potential biomarker. Conventional techniques such as nanoparticle tracking analysis (NTA) and tunable resistive pulse sensing (TRPS) are widely used but are time-consuming, require specialized equipment, and are limited by EV heterogeneity. Thus, a faster, more scalable, and cost-effective method for EV quantification is needed. In this study, we developed an Extracellular Vesicle-Induced Silver-nanoparticle Aggregation (EVISA) assay for rapid and accessible measurement of EV concentration based on plasmonic nanoparticle aggregation with visible colour change. We screened multiple nanoparticles for aggregation upon mixing with EVs, assessed by naked-eye colour change and quantified with a standard ELISA plate reader. Transmission electron microscopy (TEM) confirmed nanoparticle aggregation, and zeta potential analysis demonstrated that EV-induced aggregation arose from destabilization of colloidal stability. Silver nanoparticles exhibited EV-induced plasmon coupling, evidenced by localized surface plasmon resonance (LSPR) shifts, resulting in increased absorbance at 450 nm as detected by the plate reader. Together, these findings establish EVISA as a rapid, cost-effective, and scalable EV quantification platform that leverages widely available biological laboratory instrumentation, enabling broader adoption across the EV research community.

Indexed as

Color changeELISA plate readerEV quantificationExtracellular Vesicles (EVs)Nanoparticle aggregation

Identifiers

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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.