ReviewBiomedicines2023
Towards Improved Human
Review in Biomedicines, 2023. 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, 10 citations in OpenAlex.
- Reconstructing AF-associated atrial fibrosis: Patient-specific iPSC models, fit-for-purpose atrial microphysiological systems, and nanomedicine.Materials today. Bio · 2026Review
- Inflammation-Mediated Mechanisms of Arrhythmias After Acute Myocardial Infarction.Reviews in cardiovascular medicine · 2026Review
- Identification of a Novel Genetic Variant Responsible for Familial Atrial Fibrillation.The Canadian journal of cardiology · 2026Article
- Current trends, applications, and challenges in three-dimensional bioprinting for cardiovascular disease models and therapies.iScience · 2026Review
- Metabolic Remodeling and Mitochondrial Stress in Atrial Fibrillation: Mechanisms and Translational Targets.Reviews in cardiovascular medicine · 2025Review
- Article
- Advancing Atrial Fibrillation Research: The Role of Animal Models, Emerging Technologies and Translational Challenges.Biomedicines · 2025Review
- Inflammatory and fibrotic signaling pathways mediated by cardiac macrophages in atrial fibrillation.Frontiers in cardiovascular medicine · 2025Review
- The Current State of Realistic Heart Models for Disease Modelling and Cardiotoxicity.International journal of molecular sciences · 2024Review
- On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders.Biomedicines · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Heart rhythm disorders, arrhythmias, place a huge economic burden on society and have a large impact on the quality of life of a vast number of people. Arrhythmias can have genetic causes but primarily arise from heart tissue remodeling during aging or heart disease. As current therapies do not address the causes of arrhythmias but only manage the symptoms, it is of paramount importance to generate innovative test models and platforms for gaining knowledge about the underlying disease mechanisms which are compatible with drug screening. In this review, we outline the most important features of atrial fibrillation (AFib), the most common cardiac arrhythmia. We will discuss the epidemiology, risk factors, underlying causes, and present therapies of AFib, as well as the shortcomings and opportunities of current models for cardiac arrhythmia, including animal models,
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.