Evidence map›Paper›PMID 41132496›Full record

ReviewExtracellular vesicles and circulating nucleic acids2025

Harnessing artificial intelligence for engineering extracellular vesicles.

Hui Lu, Jin Zhang, Tianzhuo Shen, Wenbing Jiang, Han Liu, Jiacan Su

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Extracellular vesicles and circulating nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 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

6 authors.

Hui LuInstitute of Translational Medicine, Shanghai University, Shanghai 200444, China.
Jin ZhangInstitute of Translational Medicine, Shanghai University, Shanghai 200444, China.
Tianzhuo ShenInstitute of Translational Medicine, Shanghai University, Shanghai 200444, China.
Wenbing JiangDepartment of Cardiology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, Zhejiang, China.
Han LiuInstitute of Translational Medicine, Shanghai University, Shanghai 200444, China.
Jiacan SuInstitute of Translational Medicine, Shanghai University, Shanghai 200444, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) are a type of cell-released phospholipid bilayer nanoscale carrier. However, research on EVs encounters several challenges, such as their heterogeneity, the complexities associated with their isolation and identification, the necessity for engineering optimization, and the limitations in exploring their mechanisms. The advancement of artificial intelligence (AI) technologies offers new opportunities for EV research. Here, the definition and brief history of AI, as well as types and common models of machine learning, are first introduced, and the interactions between AI, machine learning, and deep learning are explored. The article then discusses in detail a variety of applications of AI in EV research, including the use of AI for target identification and selective delivery of EVs, the design and optimization of drug delivery systems, the mapping of cellular communication networks, the analysis of multi-omics data, and synthetic biology-based research on EVs. These applications demonstrate the potential of AI in advancing EV research and applications. Finally, we offer an outlook on the major challenges and future prospects of AI. Overall, the introduction of AI technologies has provided new perspectives and tools for the study of EVs, which is expected to enhance the application of EVs in disease diagnosis and treatment.

Indexed as

artificial intelligencedrug deliveryExtracellular vesiclesmachine learningtargeted therapy

Identifiers

PMID41132496
PMCPMC12540270

What OpenQuestion 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 OpenQuestion graph.