Evidence map›Paper›PMID 37724243›Full record

ReviewMedical review (2021)2022

MicroRNAs in cardiovascular diseases.

Huaping Li, Jiabing Zhan, Chen Chen, Daowen Wang

Abstract readReview
In one paragraph

Review in Medical review (2021), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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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

4 authors.

Huaping LiDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jiabing ZhanDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Chen ChenDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Daowen WangDivision of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0002-9774-3980

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular diseases (CVDs) are the leading causes of death and disability worldwide, despite the wide diversity of molecular targets identified and the development of therapeutic methods. MicroRNAs (miRNAs) are a class of small (about 22 nucleotides) non-coding RNAs (ncRNAs) that negatively regulate gene expression at the post-transcriptional level in the cytoplasm and play complicated roles in different CVDs. While miRNA overexpression in one type of cell protects against heart disease, it promotes cardiac dysfunction in another type of cardiac cell. Moreover, recent studies have shown that, apart from cytosolic miRNAs, subcellular miRNAs such as mitochondria- and nucleus-localized miRNAs are dysregulated in CVDs. However, the functional properties of cellular- and subcellular-localized miRNAs have not been well characterized. In this review article, by carefully revisiting animal-based miRNA studies in CVDs, we will address the regulation and functional properties of miRNAs in various CVDs. Specifically, the cell-cell crosstalk and subcellular perspective of miRNAs are highlighted. We will provide the background for attractive molecular targets that might be useful in preventing the progression of CVDs and heart failure (HF) as well as insights for future studies.

Indexed as

apoptosiscardiovascular diseasesfibrosisheart failurehypertrophymiRNAssubcellular organelle

Identifiers

PMID37724243
PMCPMC10471109

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.