Evidence map›Paper›PMID 26622327›Full record

ArticleExperimental and therapeutic medicine2015

RNA interference-based therapeutics for inherited long QT syndrome.

Guoliang Li, Shuting Ma, Chaofeng Sun

Open access · diamondAbstract read
In one paragraph

Article in Experimental and therapeutic medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. 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

3 authors at 1 institution in 1 country.

Guoliang LiDepartment of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xi'an, Shaanxi 710061, P.R. China.
Shuting MaDepartment of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xi'an, Shaanxi 710061, P.R. China.
Chaofeng SunDepartment of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xi'an, Shaanxi 710061, P.R. China.
First Affiliated Hospital of Xi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inherited long QT syndrome (LQTS) is an electrical heart disorder that manifests with syncope, seizures, and increased risk of torsades de pointes and sudden cardiac death. Dominant-negative current suppression is a mechanism by which pathogenic proteins disrupt the function of ion channels in inherited LQTS. However, current approaches for the management of inherited LQTS are inadequate. RNA interference (RNAi) is a powerful technique that is able to suppress or silence the expression of mutant genes. RNAi may be harnessed to knock out mRNAs that code for toxic proteins, and has been increasingly recognized as a potential therapeutic intervention for a range of conditions. The present study reviews the literature for RNAi-based therapeutics in the treatment of inherited LQTS. Furthermore, this review discusses the combined use of RNAi with the emerging technology of induced pluripotent stem cells for the treatment of inherited LQTS. In addition, key challenges that must be overcome prior to RNAi-based therapies becoming clinically applicable are addressed. In summary, RNAi-based therapy is potentially a powerful therapeutic intervention, although a number of difficulties remain unresolved.

Indexed as

dominant-negative suppressiongene therapyinduced pluripotent stem cellslong QT syndromeRNA interference

Identifiers

PMID26622327
PMCPMC4509090
OpenAlexW1959663727

What OpenQuestion holds

Textmetadata
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.