Evidence map›Paper›PMID 42935968›Full record

SynthesisFrontiers in psychiatry2026

Efficacy of stem cell-derived extracellular vesicles in animal models of autism spectrum disorder: a systematic review and meta-analysis.

Zhen Zheng, Manfen Ji, Yayong Liang, Sinian Pan

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in psychiatry, 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

4 authors.

Zhen Zheng *Department of Pediatrics, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Manfen Ji *Department of Pediatrics, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Yayong LiangDepartment of Pediatrics, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Sinian PanDepartment of Pediatrics, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Autism spectrum disorder (ASD) is a complicated neurodevelopmental condition that currently lacks a targeted therapy. Although stem cell-derived extracellular vesicles (EVs) represent a potential therapeutic approach for neurological disorders, their efficacy in ASD remains unclear. The objective of this study was to investigate the efficacy of stem cell-derived EVs on autistic-like behaviors in animal models of ASD and to explore the underlying mechanisms. Methods: Relevant studies published up to April 20, 2026 were searched across five databases. Data were pooled using Review Manager 5.4. Outcome measures included behavioral tests and inflammatory cytokine levels. Results: Out of 1,165 initially retrieved articles, five animal studies met the inclusion criteria. Pooled analyses demonstrated that EV treatment improved autistic-like behaviors in the self-grooming test, three-chamber social interaction test, and marble burying test, and decreased levels of proinflammatory cytokines IL-6 and IL-1β (all Conclusion: Stem cell-derived EV therapy may improve repetitive behaviors and social deficits in ASD animal models, with observed associations with reduced IL-6 and IL-1β levels. These preclinical findings suggest promising translational potential of EV-based therapy for ASD. Systematic Review Registration: https://osf.io/c78xm, identifier 10.17605/OSF.IO/C78XM.

Indexed as

animal modelsautism spectrum disordermeta-analysisneuroinflammationstem cell-derived extracellular vesicles

Identifiers

PMID42935968
PMCPMC13696452

What OpenQuestion holds

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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.