Evidence map›Paper›PMID 41799212›Full record

ReviewTheranostics2026

From bench to bedside: the promise and roadblocks of extracellular vesicle therapeutics.

Jing Zhang, Yuanwei Pan, Peng She, Lang Rao

Abstract readReview
In one paragraph

Review in Theranostics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

4 authors.

Jing ZhangCollege of Food Science and Biology, Hebei University of Science and Technology, Shijiazhuang 050000, China.
Yuanwei PanInstitute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China.
Peng SheDepartment of Orthopedics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518000, China.
Lang RaoInstitute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) have transformed the landscape of precision therapeutics, owing to their intrinsic biocompatibility, capacity to transport biomolecules, and ability to cross biological barriers. Benefiting from these advantages, several EV-based therapeutic applications are currently under active development. However, most products remain at the preclinical stage due to significant translational challenges. In this review, we systematically outline these obstacles and highlight opportunities that can accelerate the clinical translation of EVs. We first identify core issues impeding clinical progress, such as difficulties in isolation, storage stability, precise characterization, biosafety evaluation, and quality control. After that, we examine emerging technologies designed to overcome these bottlenecks, including advanced bioreactors for large-scale production, microfluidic systems for high-purity separation, single-vesicle analysis technologies to address heterogeneity, and machine learning approaches that enhance standardization and support clinical decision-making. Finally, we discuss future directions focused on standardizing EV production, improving safety assessment, and ensuring regulatory compliance. By addressing current challenges and identifying promising pathways, this review aims to facilitate the translation of EV-based therapies from bench to bedside.

Indexed as

Extracellular VesiclesTranslational Research, BiomedicalAnimalsHumansanalytical techniquesclinical translationextracellular vesicles (EVs)machine learningsingle EV analysis

Identifiers

PMID41799212
PMCPMC12964386

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.