Evidence map›Paper›PMID 35535877›Full record

ReviewNeural regeneration research2022

Advancements in our understanding of circular and long non-coding RNAs in spinal cord injury.

Yan Zhang, Ho Jun Yun, Yu Ji, Eric Cosky, Wen-Xiu Zhang, Wei Han, Yu-Chuan Ding

Open access · goldAbstract readReview
In one paragraph

Review in Neural regeneration research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

7 authors at 2 institutions in 2 countries.

Yan ZhangCentral Laboratory, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Ho Jun YunDepartment of Neurosurgery, Wayne State University School of Medicine, Detroit, MI, USA.
Yu JiCentral Laboratory; Department of General Surgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Eric CoskyDepartment of Neurosurgery, Wayne State University School of Medicine, Detroit, MI, USA.
Wen-Xiu ZhangCentral Laboratory, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Wei HanCentral Laboratory; Department of General Surgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Yu-Chuan DingDepartment of Neurosurgery, Wayne State University School of Medicine, Detroit, MI, USA.
Beijing Luhe Hospital Affiliated to Capital Medical University · CNWayne State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spinal cord injury (SCI), either from trauma or degenerative changes, can result in severe disability and impaired quality of life. Understanding the cellular processes and molecular mechanisms that underlie SCI is imperative to identifying molecular targets for potential therapy. Recent studies have shown that non-coding RNAs, including both long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), regulate various cellular processes in SCI. In this review, we will describe the changes in lncRNA and circRNA expression that occur after SCI and how these changes may be related to SCI progression. Current evidence for the roles of lncRNAs and circRNAs in neuronal cell death and glial cell activation will also be reviewed. Finally, the possibility that lncRNAs and circRNAs are novel modulators of SCI pathogenesis will be discussed.

Indexed as

circular RNAsfunctionlong non-coding RNAsneuronal cellsneuronal gliapathogenesisregulationspinal cord injury

Identifiers

PMID35535877
PMCPMC9120674
OpenAlexW4225541680

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.