Evidence map›Paper›PMID 34796209›Full record

ReviewFrontiers in cardiovascular medicine2021

The Combined Regulation of Long Non-coding RNA and RNA-Binding Proteins in Atherosclerosis.

Yuanyuan Ding, Ruihua Yin, Shuai Zhang, Qi Xiao, Hongqin Zhao, Xudong Pan, Xiaoyan Zhu

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
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

5 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
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

7 authors at 2 institutions in 1 country.

Yuanyuan DingDepartment of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Ruihua YinDepartment of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Shuai ZhangDepartment of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Qi XiaoDepartment of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Hongqin ZhaoDepartment of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Xudong PanDepartment of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Xiaoyan ZhuDepartment of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
Qingdao University · CNAffiliated Hospital of Qingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atherosclerosis is a complex disease closely related to the function of endothelial cells (ECs), monocytes/macrophages, and vascular smooth muscle cells (VSMCs). Despite a good understanding of the pathogenesis of atherosclerosis, the underlying molecular mechanisms are still only poorly understood. Therefore, atherosclerosis continues to be an important clinical issue worthy of further research. Recent evidence has shown that long non-coding RNAs (lncRNAs) and RNA-binding proteins (RBPs) can serve as important regulators of cellular function in atherosclerosis. Besides, several studies have shown that lncRNAs are partly dependent on the specific interaction with RBPs to exert their function. This review summarizes the important contributions of lncRNAs and RBPs in atherosclerosis and provides novel and comprehensible interaction models of lncRNAs and RBPs.

Indexed as

atherosclerosisendothelial cellslong non-coding RNAmacrophagesRNA-binding proteinsmooth muscle cells

Identifiers

PMID34796209
PMCPMC8592911
OpenAlexW3208428293

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.