Evidence map›Paper›PMID 39484649›Full record

ArticleDigital health

Computational analysis of long QT syndrome type 2 and the therapeutic effects of KCNQ1 antibodies.

Zhujun Pan, Qi Fu, Huasen Jiang, Zhiqiang Wei, Shugang Zhang

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Zhujun PanCollege of Computer Science and Technology, Ocean University of China, Qingdao, China.
Qi FuCollege of Computer Science and Technology, Ocean University of China, Qingdao, China.
Huasen JiangCollege of Computer Science and Technology, Ocean University of China, Qingdao, China.
Zhiqiang WeiCollege of Computer Science and Technology, Ocean University of China, Qingdao, China.
Shugang ZhangCollege of Computer Science and Technology, Ocean University of China, Qingdao, China.ORCID https://orcid.org/0000-0002-9774-9709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Long QT interval syndrome (LQTS) is a highly dangerous cardiac disease that can lead to sudden cardiac death; however, its underlying mechanism remains largely unknown. This study is conceived to investigate the impact of two general genotypes of LQTS type 2, and also the therapeutic effects of an emerging immunology-based treatment named KCNQ1 antibody. Methods: A multiscale virtual heart is developed, which contains multiple biological levels ranging from ion channels to a three-dimensional cardiac structure with realistic geometry. Critical biomarkers at different biological levels are monitored to investigate the remodeling of cardiac electrophysiology induced by mutations. Results: Simulations revealed multiple important mechanisms that are hard to capture via conventional clinical techniques, including the augmented dispersion of repolarization, the increased vulnerability to arrhythmias, the impaired adaptability in tissue to high heart rates, and so on. An emerging KCNQ1 antibody-based therapy could rescue the prolonged QT interval but did not reduce the vulnerable window. Conclusions: Tiny molecular alterations can lead to cardiac electrophysiological remodeling at multiple biological levels, which in turn contributes to higher susceptibility to lethal arrhythmias in long QT syndrome type 2 patients. The KCNQ1 antibody-based therapy has proarrhythmic risks notwithstanding its QT-rescuing effects.

Indexed as

arrhythmiaKCNQ1 antibodyLong QT syndromesimulationvirtual heart

Identifiers

PMID39484649
PMCPMC11526401

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.