Evidence mapPaperPMID 42518683Full record

ReviewInternational journal of nanomedicine2026

Extracellular Vesicles in Gestational Diabetes Mellitus: Pathogenesis, Diagnosis, and Therapy.

Xiaojuan Zhang, Ziwen Zhang, Limin Jin, Yimin Huang

Abstract readReview
In one paragraph

Review in International journal of nanomedicine, 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.

Xiaojuan ZhangDepartment of Pharmaceutics, College of Medicine, Jiaxing University, Jiaxing, People's Republic of China.
Ziwen ZhangDepartment of Obstetrics, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children Hospital, Jiaxing University, Jiaxing, People's Republic of China.
Limin JinDepartment of Clinical Laboratory, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children Hospital, Jiaxing University, Jiaxing, People's Republic of China.
Yimin HuangDepartment of Central Laboratory, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children Hospital, Jiaxing University, Jiaxing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gestational diabetes mellitus (GDM) is the most common metabolic complication of pregnancy, with a continuously rising global prevalence. However, current diagnostic and therapeutic strategies are limited by delayed detection and insufficient targeting. Extracellular vesicles (EVs), as nanoscale messengers that mediate intercellular communication, have recently been implicated in GDM pathogenesis and exhibit dual potential as liquid biopsy biomarkers and drug delivery vehicles. This review systematically integrates research findings on EVs in GDM, constructs an EV-mediated multi-organ crosstalk network framework, and comparatively evaluates the diagnostic and therapeutic potential of EVs derived from the placenta, adipose tissue, blood, breast milk, and urine. And further discussed current challenges and future directions for clinical translation of EV-based strategies. It provides an integrative perspective on the pathogenesis, precision diagnosis, and treatment of GDM and offers critical guidance for advancing the clinical translation of EV-based strategies.

Indexed as

Diabetes, GestationalExtracellular VesiclesAnimalsBiomarkersFemaleHumansPregnancyBiomarkersbiomarkersextracellular vesiclesgestational diabetes mellitusmulti-organ crosstalktherapeutic strategies

Identifiers

PMID42518683
PMCPMC13383985

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.