ArticleArrhythmia & electrophysiology review2014
The Use of Gene Therapy for Ablation of Atrial Fibrillation.
Article in Arrhythmia & electrophysiology review, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Individual Contributions of Cardiac Ion Channels on Atrial Repolarization and Reentrant Waves: A Multiscale In-Silico Study.Journal of cardiovascular development and disease · 2022Article
- Recent advances in gene therapy for atrial fibrillation.Journal of cardiovascular electrophysiology · 2021Review
- Mitochondrial Dysfunction in Atrial Fibrillation-Mechanisms and Pharmacological Interventions.Journal of clinical medicine · 2021Review
- Gene therapy for atrial fibrillation - How close to clinical implementation?International journal of cardiology · 2019Review
- Pathophysiology of Calcium Mediated Ventricular Arrhythmias and Novel Therapeutic Options with Focus on Gene Therapy.International journal of molecular sciences · 2019Review
- Atrial-Specific Gene Delivery Using an Adeno-Associated Viral Vector.Circulation research · 2019Article
- Translational Models of Arrhythmia Mechanisms and Susceptibility: Success and Challenges of Modeling Human Disease.Frontiers in cardiovascular medicine · 2019Review
- Gene Therapy for the Treatment of Cardiac Arrhythmias: Current and Emerging Applications.The Journal of innovations in cardiac rhythm management · 2018Review
- Enhancing atrial-specific gene expression using a calsequestrin cis-regulatory module 4 with a sarcolipin promoter.The journal of gene medicine · 2018Article
- Gene Therapy Approaches to Biological Pacemakers.Journal of cardiovascular development and disease · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation is the most common clinically significant cardiac arrhythmia, increasing the risk of stroke, heart failure and morbidity and mortality. Current therapies, including rate control and rhythm control by antiarrhythmic drugs or ablation therapy, are moderately effective but far from optimal. Gene therapy has the potential to become an attractive alternative to currently available therapies for atrial fibrillation. Various gene transfer vectors have been developed for cardiovascular disease with viral vectors being most widely used due to their high efficiency. Several gene delivery methods have been employed on different therapeutic targets. With increasing understanding of arrhythmia mechanisms, novel therapeutic targets have been discovered. This review will evaluate state-of-art gene therapy strategies and approaches including sinus rhythm restoration and ventricular rate control that could eventually prevent or eliminate atrial fibrillation in patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.