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2,408 results for “Atrial fibrillation”
Simulations of focal and reentrant sources with Acetylcholine regulation in atrial fibrillation
<p><strong>Simulations of focal and reentrant sources with Acetylcholine regulation in atrial fibrillation</strong></p> <p>This contains focal and reentrant sources with Acetylcholine regulation in atrial fibrillation simulations used in the manuscript <strong>Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation (</strong><a href="https://doi.org/10.1371/journal.pcbi.1009893">https://doi.org/10.1371/journal.pcbi.1009893</a><strong>)</strong>. <strong>Please cite our manuscript if you use our code</strong>.</p> <p>Detailed simulation files for focal and reentrant sources with Acetylcholine regulation in <a href="https://carpentry.medunigraz.at">CARPentry</a>. Tested with CARP GIT commit hash: 2e280733.<br> The formats of .elem, .lon, .pts, .dat and .igb used or produced by carp can be found in <a href="https://carpentry.medunigraz.at/getting-started/file-formats.html">the CARPentry website</a>.</p> <p><strong>Data (<code>data/</code>)</strong></p> <ul> <li><code>Mesh1(.elem, .lon, .pts)</code>: files (elements, fibres, nodes) of a mesh Mesh1 with basic tagging of atrial structures.</li> <li><code>Mesh1_FS_L22.vtx</code>: vertices of a focal site at (αLA=0.2,βLA=0.2\alpha_{LA} = 0.2, \beta_{LA} = 0.2αLA=0.2,βLA=0.2).</li> <li><code>Mesh1_L22_r0(.elem, .lon, .pts)</code>: files (elements, fibres, nodes) of a mesh Mesh1 with basic tagging of atrial structures and tagging of regions (Section 1 - 48) for reentrant sources.</li> <li><code>Mesh1_UAC*.dat</code>: plain text files where each row specifies a Universal Atrial Coordinate ( <code>Mesh1_UAC1.dat</code>: alpha, <code>Mesh1_UAC2.dat</code> : beta, <code>Mesh1_UAC3.dat</code> : LA or RA) of Mesh1 in Roney et al. 2019, which could be used to select vertices and tag elements. We annotated elements with tags of 1-4 and 11-28 for the Universal Atrial Coordinate.</li> <li><code>vest.pts</code>: a <code>.pts</code> file specifying the locations of 252 vest leads.</li> <li><code>Mesh1_ACh_islands.adj</code>: adjustment file specifying the node indices (first column) and concentration of the ACh (second column) for ACh islands.</li> </ul> <p><strong>Par files: parameter files for CARPentry software.</strong></p> <ul> <li><code>Focal_source.par</code>: to simulate a focal source with a CL of 180 ms on the left atrial focal site lasting for 3000 ms.</li> <li><code>Focal_source_ACh.par</code>: to simulate a focal source with a CL of 180 ms lasting for 3000 ms with ACh.</li> <li><code>Reentrant_source.par</code>: to simulate a reentrant source around a left atrial core of (αLA=0.2,βLA=0.2\alpha_{LA} = 0.2, \beta_{LA} = 0.2αLA=0.2,βLA=0.2). To run this file in CARP, the user is advised to compute the initial state files of each segment (<code>init/*.sv</code>) using <code>Reentrant_source_get_init_states.py</code>. This serves as initial states for regions with tags 100 - 147 for the left atral sections of a phase distribution method (Section 100 - 147 refers to the Section 1 - 48 in the main article Fig S1) in <code>Mesh1_L22_r0.elem</code>.</li> </ul> <p><strong>Ionic model</strong></p> <ul> <li><code>CRN_ACH.model</code>: an ionic model file with Acetylcholine introduction of Bayer et al. (2019), with Acetylcholine concentration 0 by default.</li> </ul> <p><strong>Initial conditions for reentrant sources</strong></p> <ul> <li><code>Reentrant_source_get_init_states.py</code>: Python script that output Linux commands to call <code>bench</code> software in CARPentry to initiate the reentrant sources. The produced initial state files <code>init/*.sv</code> are to be used by <code>Reentrant_source.par</code>.</li> </ul> <p><strong>Tags for the atrial structures in the element file (specified in <code>.elem</code>)</strong></p> <ul> <li>1 - Right atrial body</li> <li>2 - Right atrial appendage</li> <li>3 - Sinoatrial node</li> <li>4 - Line of block</li> <li>5 - Coronary sinus</li> <li>6 - Superior vena cava</li> <li>7 - Inferior vena cava</li> <li>8 - Crist terminalis</li> <li>9 - Pectinate muscle</li> <li>10 - Bachman Bundle</li> <li>11- Left atrial body endocardial layer</li> <li>12 - Left atrial body epicardial layer</li> <li>13 - Left atrial appendage endocardial layer</li> <li>14 - Left atrial appendage epicardial layer</li> <li>21, 23, 25 & 27 - endocardial layer of four left atrial PVs</li> <li>22, 24, 26 & 28 - epicardial layer of four left atrial PVs</li> </ul> <p><strong>References</strong></p> <ul> <li>Feng Y, Roney CH, Bayer JD, Niederer SA, Hocini M, Vigmond EJ (2022) Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation. PLoS Comput Biol 18(3): e1009893.<strong> </strong><a href="https://doi.org/10.1371/journal.pcbi.1009893">https://doi.org/10.1371/journal.pcbi.1009893</a></li> <li>Roney CH, Pashaei A, Meo M, Dubois R, Boyle PM, Trayanova NA, et al. Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes. Medical Image Analysis. 2019 Jul 1;55:65–75. <a href="https://10.1016/j.media.2019.04.004">https://10.1016/j.media.2019.04.004</a></li> <li>Bayer, et al. (2019). Acetylcholine Delays Atrial Activation to Facilitate Atrial Fibrillation. Frontiers in Physiology, 10, 1105. <a href="https://doi.org/10.3389/fphys.2019.01105">https://doi.org/10.3389/fphys.2019.01105</a></li> </ul>
IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database
<h2>Abstract</h2><p>Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.</p><h2>Article</h2><p><a href="https://www.nature.com/articles/s41597-023-02621-1">https://www.nature.com/articles/s41597-023-02621-1</a></p><h2>Repository</h2><p><a href="https://github.com/cedricgilon/iridia-af">https://github.com/cedricgilon/iridia-af</a></p><h2>Versions history</h2><ul><li>2023-10-04: v1.0.1 – remove hidden files from .zip archive</li><li>2023-07-26: v1.0.0 – initial release</li></ul><h2>Keywords</h2><p>Paroxysmal Atrial Fibrillation, AF, long-term electrocardiogram, ECG, Holter monitoring, Database, Dataset, IRIDIA, IRIDIA-AF</p>
Screening for Atrial Fibrillation using Oscillometry
<p>Data set supporting publication regarding the use of pulse rate variability as measured by an oscillometric blood pressure device to identify atrial fibrillation.</p>
Atrial Fibrillation Designation with Micro-Raman Spectroscopy and Scanning Acoustic Microscopy
<p>This repository was constructed tp provide the <strong>Raman Spectroscopy</strong> data and figure files related to the manuscript “Atrial Fibrillation Designation with Micro-Raman Spectroscopy and Scanning Acoustic Microscopy”. </p>
Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021
<p>This dataset is part of the available dataset for <em>Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021</em>, available at https://physionet.org/static/published-projects/cpsc2021/paroxysmal-atrial-fibrillation-events-detection-from-dynamic-ecg-recordings-the-4th-china-physiological-signal-challenge-2021-1.0.0.zip (last accessed today 2022-07-22).</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International Public License:</p> <p>Permissions:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Conditions:</p> <ul> <li> <p>Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> </li> </ul> <p>Limitations:</p> <ul> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</li> </ul> <p>For more information about the license, check: https://creativecommons.org/licenses/by/4.0/</p> <p>The following modifications were made:</p> <ul> <li>Only the training and test set folders are used</li> <li>Only the files *.hea, *.dat and *.atr are used, being the *.dat and *.atr converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>
ASSESSMENT OF ADHERENCE TO NEW ORAL ANTICOAGULANTS IN ATRIAL FIBRILLATION PATIENTS WITHIN THE OUTPATIENT REGISTRY PROFILE. PROSPECTIVE OBSERVATIONAL STUDY (ANTEY study)
<p><strong>Rationale</strong></p> <p>Prevention of stroke and thromboembolic complications in non-valvular atrial fibrillation is one of the most common indications for the use of all NOACs and warfarin in cardiology. The issue of regular intake of all OACs, i.e. the issue of adherence to anticoagulation therapy, is paramount for better treatment. The problem of assessment of different aspects of adherence to OACs in patients with atrial fibrillation within an outpatient registry is of current interest.</p> <p><strong>Primary</strong> <strong>Study</strong> <strong>Objectives</strong></p> <p>The aim of the present study is to assess adherence to therapy and factors associated with adherence in patients with CV disease complicated by non-valvular atrial fibrillation requiring OAC treatment within the outpatient registry PROFILE (prospective, observational study).</p> <ol> <li>Data collection in patients with non-valvular atrial fibrillation requiring OAC treatment included in the registry</li> <li>Evaluation of actual patient adherence to OACs</li> </ol> <p><strong>Secondary Study Objective(s)</strong></p> <ol> <li>Evaluation of potential patient adherence to OACs</li> <li>Determination of most significant factors associated with adherence to OACs in patients with non-valvular AF</li> <li>Validation of new original questionnaire</li> <li>Evaluation of doctor’s adherence to OAC prescription according to Guidelines (ESC). Management of atrial fibrillation,2016)</li> </ol> <p><strong>Material and methods</strong></p> <p>.The study included 201 patients with nonvalvular AF from the outpatient "PROFILE" registry, 118 (58,7%) males. The mean age was 71,1 ±8,7 years. The study protocol consisted of the inclusion visit (V0), 6-month follow-up visit (V1), and phone contact 1 year after V0 (follow-up, FU). In V0, all patients were prescribed one of the NOACs. At V1 doctors could recommend warfarin or another NOAC to patients, who have refused to take prescribed NOAC. Medical adherence was determined using the original questionnaire</p> <p><strong>Inclusion Criteria (detailed)</strong></p> <ul> <li>Men and women above 18 years of age who were included in the "PROFILE" registry by the start of the observational study</li> <li>Presence of written informed consent to participate in the study, fill in the study questionnaires, and have personal data analyzed</li> <li>Presence of any form of non-valvular atrial fibrillation with CHA<sub>2</sub>DS<sub>2</sub>-VASc score of ≥1 or patients with CHA<sub>2</sub>DS<sub>2</sub>-VASc score = 0, who are already taking OAK</li> </ul> <p><strong>Exclusion Criteria</strong></p> <p>Patients with high bleeding risk, including patients with:</p> <ul> <li>Congenital or acquired bleeding disorders</li> <li>Uncontrolled resistant hypertension</li> <li>Exacerbation of gastric and duodenal ulcer</li> <li>Vascular retinopathy</li> <li>Recent history of intracranial or intracerebral hemorrhage</li> <li>Pathology of the brain and spinal cord vessels</li> <li>Recent history of the brain, spinal cord, or eye surgery</li> <li>History of bronchiectasis or pulmonary hemorrhage</li> <li>A CHA<sub>2</sub>DS<sub>2</sub>-VASc score of 0 (OACs are not indicated)</li> <li>Pregnancy, lactation</li> <li>Planned surgery</li> <li>Known hypersensitivity to ingredients of medications used in the study</li> </ul> <p><strong>Visit schedule</strong></p> <p>Visit schedule</p> <p>Two visits at 6-month intervals are scheduled for each patient as part of routine clinical practice:</p> <p>Visit 0– visit at study entry:</p> <ul> <li>receiving written informed consent to participate in research from patients</li> <li>inclusion in the program</li> <li>assessment of inclusion and exclusion criteria</li> <li>verification of non-valvular atrial fibrillation diagnosis (according to medical documentation, confirming the history of atrial fibrillation: ECG, Holter monitoring, etc..)</li> <li>collecting information on received medication therapy, including OACs</li> <li>physical examination of patients (measurements of blood pressure, heart rate, height, weight, waist circumference)</li> <li>questioning of patients to determine potential and actual adherence to OACs</li> <li>computation of points on the scale CHA<sub>2</sub>DS<sub>2</sub>-VASc and HAS-BLED to determine the indications for the appointment of the OACs and the identification of an increased risk of bleeding</li> <li>to determine the INR in patients who agreed to replace the use of warfarin with one of the NOACs</li> <li>prescription of OACs according to routine clinical practice and official labels for these medications</li> <li>instructing patients (according to the specifically designed scheme) to regularly take prescribed medications, be aware of precautionary measures when taking OACs, telling them about the pros and cons of treatment with OACs</li> </ul> <p>Visit 1 – visit at 6 months after the Visit 0:</p> <ul> <li>collecting information on patients’ compliance with doctor’s recommendations</li> <li>collecting information on patients’ actual medication therapy</li> <li>collecting information on the safety of treatment with OACs (in case the patient has been taking them), recording all adverse events that occurred during the study period</li> <li>measurements of blood pressure, heart rate as a part of routine clinical practice</li> <li>questioning of patients to determine potential and actual adherence to OACs</li> </ul> <p>FU - follow-up calls to patients (one year)</p> <p>Phone contact to determine patient’s life status, possible (fatal and non-fatal complications of non-valvular atrial fibrillation) and fill (after receiving patient’s consent) the original questionnaire.</p> <p><strong>Primary Outcome</strong></p> <p>The proportion of complete, partial adherent and non-adherent patients according to the original questionnaire</p> <p><strong>Secondary Outcomes </strong></p> <ul> <li>The proportion of potentially adherent patients (according to the original questionnaire)</li> <li>Identification of baseline characteristics associated with adherence</li> <li>The proportion of doctors prescribed OAC according to guidelines.</li> </ul> <p><strong>Safety Outcomes </strong></p> <p>Adverse events and outcomes were recorded from the start of treatment with the OAC until the end of the observational study period. The doctor evaluated the severity of adverse events and outcomes if necessary took measures for their medical treatment according to current clinical practice.</p> <p> </p> <p> </p>
Extended data: Tissue-specific multi-omics analysis of atrial fibrillation
<p>Summary statistics and result repository for the publication Tissue-specific multi-omics analysis of atrial fibrillation:</p> <p>Assum, I., Krause, J., Scheinhardt, M.O. <em>et al.</em> Tissue-specific multi-omics analysis of atrial fibrillation. <em>Nat Commun </em><strong>13, </strong>441 (2022). https://doi.org/10.1038/s41467-022-27953-1</p> <p>For the related source code, see https://doi.org/https://doi.org/10.5281/zenodo.5094276 or https://github.com/heiniglab/symatrial.</p> <p>Ines Assum<sup>1,2,†</sup>, Julia Krause<sup>3,4,†</sup>, Markus O. Scheinhardt<sup>5</sup>, Christian Müller<sup>3,4</sup>, Elke Hammer<sup>6,7</sup>, Christin S. Börschel<sup>4,8</sup>, Uwe Vöker<sup>6,7</sup>, Lenard Conradi<sup>9</sup>, Bastiaan Geelhoed<sup>4,8,10</sup>, Tanja Zeller<sup>3,4,</sup>*, Renate B. Schnabel<sup>4,8,</sup>*, Matthias Heinig<sup>1,2,11,</sup>*</p> <p><sup>† </sup>,* These authors contributed equally.</p> <p><sup> 1</sup> Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany.<br> <sup> 2</sup> Department of Informatics, Technical University Munich, München, Germany.<br> <sup> 3</sup> University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> <sup> 4</sup> Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany.<br> <sup> 5</sup> Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.<br> <sup> 6</sup> Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.<br> <sup> 7</sup> Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany.<br> <sup> 8</sup> Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> <sup> 9</sup> Department of Cardiovascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> <sup>10 </sup>Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.<br> <sup>11</sup>Partner site Munich, DZHK (German Center for Cardiovascular Research), Munich, Germany.</p> <p> </p> <p>ABSTRACT:</p> <p>Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted transQTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.</p> <p>This version contains a reference file identifying effect alleles for all QTL results and adds additional genotype and allele frequency information for all QTL SNPs. </p> <p>TABLE OF CONTENTS:</p> <ul> <li>Reference for effect alleles<br> <em>map_AFHRI_B_effect_alleles.txt</em></li> <li>Reference for genotype and allele frequencies (derived using PLINK) <ul> <li><em>genotype_allele_frequencies_eQTL_SNPs.txt</em></li> <li><em>genotype_allele_frequencies_pQTL_SNPs.txt</em></li> <li><em>genotype_allele_frequencies_resQTL_SNPs.txt</em></li> </ul> </li> <li>Single-omic <em>cis</em>-QTL results <ul> <li><em>cis</em>-eQTLs (all pairs, incl. LD clump info)<br> <em>eQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-pQTLs (all pairs, incl. LD clump info)<br> <em>pQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-res eQTLs (all pairs, incl. LD clump info)<br> <em>res_eQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-res pQTLs (all pairs, incl. LD clump info)<br> <em>res_pQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-ratioQTLs (all pairs, incl. LD clump info)<br> <em>ratioQTL_right_atrial_appendage_allpairs_clump.txt</em></li> </ul> </li> <li>Functional <em>cis</em>-QTL categories and eQTL/pQTL overlap: <ul> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL and pQTL (FDR<0.05)<br> <em>Fig2a_source_data_Shared_eQTL_pQTL_clump.txt</em></li> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL but no pQTL (FDR<0.05)<br> <em>Fig2b_source_data_Independent_eQTL_clump.txt</em></li> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with no eQTL but a significant pQTL (FDR<0.05)<br> <em>Fig2c_source_data_Independent_pQTL_clump.txt</em><span> </span></li> </ul> </li> <li>QTS rankings and enrichment results <ul> <li>eQTS rankings and enrichments<br> <em>TableS6_source_data_eQTS_ranking.txt<br> TableS7_source_data_eQTS_GSEA_results.txt</em></li> <li>pQTS rankings and enrichments<br> <em>TableS8_source_data_pQTS_ranking.txt<br> TableS9_source_data_pQTS_GSEA_results.txt</em></li> </ul> </li> <li><em>Trans</em>-QTLs<br> all tested pairs including <em>trans</em>-pQTLs for <em>trans</em>-eQTLs and <em>trans</em>-eQTLs for <em>trans</em>-pQTLs<br> <em>Table2_source_data_Trans-QTL_results.txt</em></li> </ul> <p> </p>
Dataset related to the article "Epicardial Adipose Tissue-Derived IL-1β Triggers Postoperative Atrial Fibrillation"
<p>This record contains raw data related to the article "Epicardial Adipose Tissue-Derived IL-1β Triggers Postoperative Atrial Fibrillation"</p> <p><strong>Background and aims:</strong> Post-operative atrial fibrillation (POAF), defined as new-onset AF in the immediate period after surgery, is associated with poor adverse cardiovascular events and a higher risk of permanent AF. Mechanisms leading to POAF are not completely understood and epicardial adipose tissue (EAT) inflammation could be a potent trigger. Here, we aim at exploring the link between EAT-secreted interleukin (IL)-1β, atrial remodeling, and POAF in a population of coronary artery disease (CAD) patients. <strong>Methods:</strong> We collected EAT and atrial biopsies from 40 CAD patients undergoing cardiac surgery. Serum samples and EAT-conditioned media were screened for IL-1β and IL-1ra. Atrial fibrosis was evaluated at histology. The potential role of NLRP3 inflammasome activation in promoting fibrosis was explored <em>in vitro</em> by exposing human atrial fibroblasts to IL-1β and IL-18. <strong>Results:</strong> 40% of patients developed POAF. Patients with and without POAF were homogeneous for clinical and echocardiographic parameters, including left atrial volume and EAT thickness. POAF was not associated with atrial fibrosis at histology. No significant difference was observed in serum IL-1β and IL-1ra levels between POAF and no-POAF patients. EAT-mediated IL-1β secretion and expression were significantly higher in the POAF group compared to the no-POAF group. The <em>in vitro</em> study showed that both IL-1β and IL-18 increase fibroblasts' proliferation and collagen production. Moreover, the stimulated cells perpetuated inflammation and fibrosis by producing IL-1β and transforming growth factor (TGF)-β. <strong>Conclusion:</strong> EAT could exert a relevant role both in POAF occurrence and in atrial fibrotic remodeling.</p>
Decreased FAM13B expression increases atrial fibrillation susceptibility by regulating sodium current and calcium handling
<p><strong>Objectives</strong>: To determine the causal genetic variant and gene and the mechanism for the atrial fibrillation (AF) genome wide association study (GWAS) locus on chromosome 5q31.</p> <p><strong>Background</strong>: <em>FAM13B</em> expression is strongly associated with the lead AF GWAS variant at 5q31. However, the regulatory variant controlling <em>FAM13B</em> expression and the mechanism by which <em>FAM13B</em> impacts AF susceptibility are not known.</p> <p><strong>Methods</strong>: Bioinformatics, reporter gene transfections, gel shifts, and gene editing were used to identify the variant regulating <em>FAM13B</em> expression. RNAseq after <em>FAM13B</em> knockdown in stem cell-derived cardiomyocytes (iCMs) identified downstream processes. Patch clamp and calcium transient assays were used to assess downstream mechanisms. <em>Fam13b</em> knockout (KO) mice were studied for heart structural and functional changes, and pacing-induced arrhythmia.</p> <p><strong>Results</strong>: rs17171731 was identified as the regulatory variant controlling <em>FAM13B</em> expression, with decreased enhancer activity of the risk allele. Knockdown of <em>FAM13B</em> in iCMs altered expression of >1000 genes including <em>SCN2B</em> and led to pro-arrhythmogenic changes in the late sodium current and Ca<sup>2+</sup> cycling. FAM13B is a member of the Rho GTPase-activating protein (RhoGAP) gene family, but failed to demonstrate RhoGAP activity. GFP-tagged <em>FAM13B</em> expressed in iCMs localized at the Z-disc and plasma membrane. Fam13b knockout mice had increased basal p-wave duration and QT interval, and were more susceptible to pacing-induced arrhythmias vs. controls.</p> <p><strong>Conclusions</strong>: This study went from an AF GWAS locus to identify the causal variant and gene, mechanisms for this association, and confirmed arrhythmia susceptibility in <em>Fam13b</em> KO mice. FAM13B and downstream effectors are potential targets for patient-specific therapeutics.</p>
Can we collect health-related quality of life information from anticoagulated atrial fibrillation participants who have recently experienced a bleed? An observational feasibility study in primary, and secondary care and through an online forum
<p>The purpose of the study was to evaluate the feasibility of recruiting participants diagnosed with atrial fibrillation (AF) taking oral anticoagulation therapies (OACs) and recently experiencing a bleed to collect health-related quality of life (HRQoL) information.</p> <p><strong>Design</strong></p> <p>Observational feasibility study. The study aimed to determine the feasibility of recruiting participants with minor and major bleeds, the most appropriate route for recruitment and the appropriateness of the Patient Reported Outcome Measures (PROMs) selected for collecting HRQoL information in AF patients, and the preferred format of the surveys.</p> <p><strong>Setting</strong></p> <p>Primary care, secondary care, and via an online patient forum.</p> <p><strong>Participants</strong></p> <p>The study population was adult patients (≥ 18) with Atrial Fibrillation (AF) taking oral anticoagulation therapies (OATs) who had experienced a recent major or minor bleed within the last four weeks.</p> <p><strong>Primary and Secondary outcome measures</strong></p> <p>Primary outcome:</p> <p>Patient reported outcome measures (PROMs): EuroQol 5 dimensions-5 levels (EQ-5D-5L); Perception of anticoagulant treatment questionnaire, part 2 only (PACT-Q, part 2); Atrial fibrillation effect on quality of life (AFEQT)</p> <p>Secondary outcomes:</p> <p>Location of bleed; bleed severity; current treatment; patient perceptions of HRQoLin relation to bleeding events.</p> <p><strong>Results</strong></p> <p>We received initial expressions of interest from 103 participants. We subsequently recruited 32 participants to the study- 14 from primary care and 18 through the AF forum. No participants were recruited through secondary care. Despite 32 participants consenting, only 26 initial surveys were completed. We received follow-up surveys from 11 participants (8 primary care and 3 AF forum). COVID-19 had a major impact on the study. </p> <p><strong>Conclusions</strong></p> <p>Primary care was the most successful route for recruitment. Most participants recruited to the study experienced a minor bleed. Further ways to recruit in secondary care should be explored, especially to capture more serious bleeds. </p> <p><strong>Registration</strong></p> <p>The study was adopted onto the NIHR Portfolio (I.D. #47771) and registered with www.ClinicalTrials.gov (#NCT04921176) in February 2021.</p> <p>Dataset contains all anonymised data for the participants who completed the survey including demographics, details of bleeds, co-morbidities and completed patient reported outcomes</p>
Edoxaban Treatment Versus Vitamin K Antagonist in Patients With Atrial Fibrillation Undergoing Percutaneous Coronary Intervention
ClinicalTrials.gov study NCT02866175. IPD Sharing: YES. Countries: 18. Publications: 3.
Prospective Elimination Of Distal Coronary Sinus-Left Atrial Connections for Atrial Fibrillation Ablation Trial
ClinicalTrials.gov study NCT03646643. IPD Sharing: YES. Countries: 1. Publications: 5.
Edoxaban vs. Warfarin in Subjects Undergoing Cardioversion of Nonvalvular Atrial Fibrillation (NVAF)
ClinicalTrials.gov study NCT02072434. IPD Sharing: YES. Countries: 19. Publications: 4.
Global Study to Assess the Safety and Effectiveness of Edoxaban (DU-176b) vs Standard Practice of Dosing With Warfarin in Patients With Atrial Fibrillation
ClinicalTrials.gov study NCT00781391. IPD Sharing: YES. Countries: 46. Publications: 35.
Edoxaban Treatment Versus Vitamin K Antagonist (VKA) in Patients With Atrial Fibrillation (AF) Undergoing Catheter Ablation
ClinicalTrials.gov study NCT02942576. IPD Sharing: YES. Countries: 11. Publications: 10.
Safety and Tolerability of Abelacimab (MAA868) vs. Rivaroxaban in Patients With Atrial Fibrillation
ClinicalTrials.gov study NCT04755283. IPD Sharing: YES. Countries: 7. Publications: 3.
Edoxaban Compared to Standard Care After Heart Valve Replacement Using a Catheter in Patients With Atrial Fibrillation (ENVISAGE-TAVI AF)
ClinicalTrials.gov study NCT02943785. IPD Sharing: YES. Countries: 14. Publications: 5.
Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Code repository
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Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Atrial meshes
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Decreased FAM13B expression increases atrial fibrillation susceptibility by regulating sodium current and calcium handling
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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International Brain Laboratory public data
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OpenNeuro
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