Evidence map›Paper›PMID 35904433›Full record

ReviewACS nano2022

Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles.

Renee-Tyler T Morales, Jina Ko

Abstract readReview
In one paragraph

Review in ACS nano, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing 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

54 citing papers in PubMed.

  1. Review
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  8. The Fantastic Single-Molecule Techniques.Chemical & biomedical imaging · 2026
    Review
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  11. Article
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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.

Renee-Tyler T MoralesDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.ORCID 0000-0002-2779-1557
Jina KoDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.ORCID 0000-0002-4941-4234

Funding

Research Education ComponentP30AG072979 · NIA · UNIVERSITY OF PENNSYLVANIA · PI JASON H KARLAWISH · 2021 to 2026
$24.8M
Mapping Single Extracellular Vesicles to Parent Cells for Immunotherapy MonitoringR00CA256353 · NCI · UNIVERSITY OF PENNSYLVANIA · PI KO, JINA · 2022 to 2024
$741k
Mapping Single Extracellular Vesicles to Parent Cells for Immunotherapy MonitoringK99CA256353 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI KO, JINA · 2021 to 2021
$178k
NCI NIH HHS K99 CA256353NCI NIH HHS R00 CA256353NIA NIH HHS P30 AG072979
6 · The paper itself

Abstract

Extracellular vesicles (EVs) are complex lipid membrane vehicles with variable expressions of molecular cargo, composed of diverse subpopulations that participate in the intercellular signaling of biological responses in disease. EV-based liquid biopsies demonstrate invaluable clinical potential for overhauling current practices of disease management. Yet, EV heterogeneity is a major needle-in-a-haystack challenge to translate their use into clinical practice. In this review, existing digital assays will be discussed to analyze EVs at a single vesicle resolution, and future opportunities to optimize the throughput, multiplexing, and sensitivity of current digital EV assays will be highlighted. Furthermore, this review will outline the challenges and opportunities that impact the clinical translation of single EV technologies for disease diagnostics and treatment monitoring.

Indexed as

Extracellular VesiclesBiological Assaybiomarkersclinical translationdigital assaysextracellular vesiclesheterogeneityimmunoassaysliquid biopsiesmolecular diagnosticsmultiplexingpoint-of-care testing

Identifiers

PMID35904433
PMCPMC10174080

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.