Evidence map›Paper›PMID 37377769›Full record

ReviewFrontiers in molecular neuroscience2023

Long non-coding RNAs in intracerebral hemorrhage.

Chenyu Zhang, Ying Zhang, Qi Wang, Zhenwei Fang, Xinyi Xu, Mengnan Zhao, Ting Xu

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in molecular neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 23% 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, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  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 1 country.

Chenyu ZhangDepartment of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
Ying ZhangDepartment of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
Qi WangDepartment of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
Zhenwei FangDepartment of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
Xinyi XuDepartment of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
Mengnan ZhaoDepartment of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
Ting XuDepartment of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
Sichuan University · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intracerebral hemorrhage (ICH), a subtype of stroke, can lead to long-term disability and is one of the leading causes of death. Unfortunately, the effectiveness of pharmacological therapy for ICH is still uncertain. Long non-coding RNA (lncRNA) was defined as an RNA molecule that consists of more than 200 nt without translational activity. As a vital class of diverse molecules, lncRNAs are involved in developmental and pathological processes and have been attractive for decades. LncRNAs have also become potential targets for therapies, as they were massively identified and profiled. In particular, emerging evidence has revealed the critical role of lncRNAs in ICH while attempts were made to treat ICH via regulating lncRNAs. But the latest evidence remains to be summarized. Thus, in this review, we will summarize the recent advances in lncRNA in ICH, highlighting the regulatory role of lncRNAs and their potential as therapeutic targets.

Indexed as

hemorrhagic strokeintracerebral hemorrhagelong non-coding RNApathologytherapeutic target

Identifiers

PMID37377769
PMCPMC10292654
OpenAlexW4380322657

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.