Evidence map›Paper›PMID 39407601›Full record

ReviewMolecules (Basel, Switzerland)2024

Nanoparticle Tracking Analysis: An Effective Tool to Characterize Extracellular Vesicles.

Gabrielle Kowkabany, Yuping Bao

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing 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

59 citing papers in PubMed.

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  15. Clinical Applications of Extracellular Vesicles: Promises and Pitfalls.International journal of molecular sciences · 2026
    Review
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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

2 authors.

Gabrielle KowkabanyChemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.ORCID 0009-0009-1114-1013
Yuping BaoChemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.ORCID 0000-0003-2829-4082

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) are membrane-enclosed particles that have attracted much attention for their potential in disease diagnosis and therapy. However, the clinical translation is limited by the dosing consistency due to their heterogeneity. Among various characterization techniques, nanoparticle tracking analysis (NTA) offers distinct benefits for EV characterization. In this review, we will discuss the NTA technique with a focus on factors affecting the results; then, we will review the two modes of the NTA techniques along with suitable applications in specific areas of EV studies. EVs are typically characterized by their size, size distribution, concentration, protein markers, and RNA cargos. The light-scattering mode of NTA offers accurate size, size distribution, and concentration information in solution, which is useful for comparing EV isolation methods, storage conditions, and EV secretion conditions. In contrast, fluorescent mode of NTA allows differentiating EV subgroups based on specific markers. The success of fluorescence NTA heavily relies on fluorescent tags (e.g., types of dyes and labeling methods). When EVs are labeled with disease-specific markers, fluorescence NTA offers an effective tool for disease detection in biological fluids, such as saliva, blood, and serum. Finally, we will discuss the limitations and future directions of the NTA technique in EV characterization.

Indexed as

Extracellular VesiclesNanoparticlesAnimalsBiomarkersHumansBiomarkersexosomesextracellular vesiclesfluorescent labelingnanoparticle-tracking analysis

Identifiers

PMID39407601
PMCPMC11477862

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.