Evidence map›Paper›PMID 40124995›Full record

ReviewBiochemistry and biophysics reports2025

Exploring the role of exosomal and non-exosomal non-coding RNAs in Kawasaki disease: Implications for diagnosis and therapeutic strategies against coronary artery aneurysms.

Negar Jafari, Ali Zolfi Gol, Venus Shahabi Rabori, Mohammadreza Saberiyan

Abstract readReview
In one paragraph

Review in Biochemistry and biophysics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Negar JafariDepartment of Cardiology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
Ali Zolfi GolDepartment of Cardiology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
Venus Shahabi RaboriDepartment of Cardiology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
Mohammadreza SaberiyanDepartment of Medical Genetics, Faculty of Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kawasaki disease (KD) is an acute vasculitis primarily affecting children, with a potential risk of developing coronary artery aneurysms (CAAs) and cardiovascular complications. The emergence of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), has provided insights into Kawasaki disease pathogenesis and opened new avenues for diagnosis and therapeutic intervention. Furthermore, polymorphism analysis of ncRNA genes offers significant insights into genetic predisposition to Kawasaki disease, facilitating tailored treatment approaches and risk assessment to improve patient outcomes. Exosomal ncRNAs, which are ncRNAs encapsulated within extracellular vesicles, have garnered significant attention as potential biomarkers for Kawasaki disease and CAA due to their stability and accessibility in biological fluids. This review comprehensively discusses the biogenesis, components, and potential of exosomal and non-exosomal ncRNAs in Kawasaki disease diagnosis and prognosis prediction. It also highlights the roles of non-exosomal ncRNAs, such as miRNAs, lncRNAs, and circRNAs, in Kawasaki disease pathogenesis and their implications as therapeutic targets. Additionally, the review explores the current diagnostic and therapeutic approaches for Kawasaki disease and emphasizes the need for further research to validate these ncRNA-based biomarkers in diverse populations and clinical settings.

Indexed as

BiomarkersCardiovascular complicationscircRNAsDiagnostic markerslncRNAsTherapeutic targets

Identifiers

PMID40124995
PMCPMC11930191

What Socratic holds

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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.