Evidence map›Paper›PMID 40893381›Full record

ArticleMaterials today. Bio2025

Self-assembled extracellular matrix-lipid nanoparticle composite for site-specific siRNA delivery to improve cardiac repair post-myocardial infarction.

Xinming Wang, Yiming Zhong, Bei Qian, Shixing Huang, Qiang Long, Haonan Zhang, Qiang Zhao, Xiaofeng Ye

Abstract read
In one paragraph

Article in Materials today. Bio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

8 authors.

Xinming WangDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Yiming ZhongDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Bei QianDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Shixing HuangDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Qiang LongDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Haonan ZhangDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Qiang ZhaoDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Xiaofeng YeDepartment of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease remains a leading cause of mortality, highlighting the critical need for novel therapeutic strategies. RNA-based therapeutics, including siRNA and mRNA, offer promising approaches for cardiac diseases, yet their clinical application is limited by low heart specificity and suboptimal delivery methods. Lipid nanoparticles (LNPs) are widely used for RNA delivery but often accumulate in non-cardiac tissues, reducing their effectiveness. To address this, an extracellular matrix (ECM)-LNP composite is developed for targeted RNA delivery to the myocardium. The LNPs are conjugated to the ECM scaffold to enhance RNA retention.

Indexed as

Epidermal growth factor receptorExtracellular matrix-lipid nanoparticle compositeLocalized deliveryMyocardial infarctionsiRNA therapy

Identifiers

PMID40893381
PMCPMC12395518

What OpenQuestion 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 OpenQuestion graph.