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  1. AU=Trayanova Natalia A
  2. AU=Jimeno-Gonzlez Silvia
  3. AU=Bussolino F
  4. AU="Almulla, Hanan"
  5. AU="Chen, Wenmei"
  6. AU=Zeng Weiqing

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  1. Artikel ; Online: Learning for Prevention of Sudden Cardiac Death.

    Trayanova, Natalia A

    Circulation research

    2021  Band 128, Heft 2, Seite(n) 185–187

    Mesh-Begriff(e) Arrhythmias, Cardiac ; Death, Sudden, Cardiac/epidemiology ; Death, Sudden, Cardiac/etiology ; Death, Sudden, Cardiac/prevention & control ; Humans
    Sprache Englisch
    Erscheinungsdatum 2021-01-21
    Erscheinungsland United States
    Dokumenttyp Editorial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 80100-8
    ISSN 1524-4571 ; 0009-7330 ; 0931-6876
    ISSN (online) 1524-4571
    ISSN 0009-7330 ; 0931-6876
    DOI 10.1161/CIRCRESAHA.120.318576
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Up digital and personal: How heart digital twins can transform heart patient care.

    Trayanova, Natalia A / Prakosa, Adityo

    Heart rhythm

    2023  Band 21, Heft 1, Seite(n) 89–99

    Abstract: Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating ... ...

    Abstract Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating personal health conditions and predicting patient or disease trajectories on the basis of relationships learned both from data and from biophysics knowledge. Such virtual replicas would update themselves with data from monitoring devices and medical tests and assessments, reflecting dynamically the changes in our health conditions and the responses to treatment. In precision cardiology, the concepts and initial applications of heart digital twins have slowly been gaining popularity and the trust of the clinical community. In this article, we review the advancement in heart digital twinning and its initial translation to the management of heart rhythm disorders.
    Mesh-Begriff(e) Humans ; Atrial Fibrillation/therapy ; Heart ; Patient Care
    Sprache Englisch
    Erscheinungsdatum 2023-10-21
    Erscheinungsland United States
    Dokumenttyp Review ; Journal Article
    ZDB-ID 2229357-7
    ISSN 1556-3871 ; 1547-5271
    ISSN (online) 1556-3871
    ISSN 1547-5271
    DOI 10.1016/j.hrthm.2023.10.019
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Buch ; Online: Cardiac Defibrillation : Mechanisms, Challenges and Implications

    Trayanova, Natalia

    2011  

    Schlagwörter Cardiovascular medicine
    Umfang 1 electronic resource (262 pages)
    Verlag IntechOpen
    Dokumenttyp Buch ; Online
    Anmerkung English ; Open Access
    HBZ-ID HT021049112
    ISBN 9789535165057 ; 9535165054
    Datenquelle ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

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  4. Artikel ; Online: Deep learning a person's risk of sudden cardiac death.

    Trayanova, Natalia A / Topol, Eric J

    Lancet (London, England)

    2022  Band 399, Heft 10339, Seite(n) 1933

    Mesh-Begriff(e) Death, Sudden, Cardiac/etiology ; Deep Learning ; Humans ; Risk Factors
    Sprache Englisch
    Erscheinungsdatum 2022-05-19
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 3306-6
    ISSN 1474-547X ; 0023-7507 ; 0140-6736
    ISSN (online) 1474-547X
    ISSN 0023-7507 ; 0140-6736
    DOI 10.1016/S0140-6736(22)00881-9
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies.

    Yamamoto, Carolyna / Trayanova, Natalia A

    EBioMedicine

    2022  Band 85, Seite(n) 104310

    Abstract: Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from ... ...

    Abstract Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.
    Mesh-Begriff(e) Animals ; Humans ; Atrial Fibrillation/etiology ; Computer Simulation ; Electrophysiological Phenomena ; Models, Animal
    Sprache Englisch
    Erscheinungsdatum 2022-10-26
    Erscheinungsland Netherlands
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2851331-9
    ISSN 2352-3964
    ISSN (online) 2352-3964
    DOI 10.1016/j.ebiom.2022.104310
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Atrial fibrillation

    Carolyna Yamamoto / Natalia A. Trayanova

    EBioMedicine, Vol 85, Iss , Pp 104310- (2022)

    Insights from animal models, computational modeling, and clinical studies

    2022  

    Abstract: Summary: Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. ... ...

    Abstract Summary: Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.
    Schlagwörter Atrial fibrillation ; Reentrant drivers ; Arrhythmia mechanisms ; Personalized computational modeling ; Pulmonary vein isolation ; Medicine ; R ; Medicine (General) ; R5-920
    Thema/Rubrik (Code) 610
    Sprache Englisch
    Erscheinungsdatum 2022-11-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel ; Online: Elastic shape analysis computations for clustering left atrial appendage geometries of atrial fibrillation patients.

    Ahmad, Zan / Yin, Minglang / Sukurdeep, Yashil / Rotenberg, Noam / Kholmovski, Eugene / Trayanova, Natalia A

    ArXiv

    2024  

    Abstract: Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF). Studying LAA morphology can elucidate mechanisms behind this association and lead to the ... ...

    Abstract Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF). Studying LAA morphology can elucidate mechanisms behind this association and lead to the development of advanced stroke risk stratification tools. However, current categorical descriptions of LAA morphologies are qualitative and inconsistent across studies, which impedes advancements in our understanding of stroke pathogenesis in AF. To mitigate these issues, we introduce a quantitative pipeline that combines elastic shape analysis with unsupervised learning for the categorization of LAA morphology in AF patients. As part of our pipeline, we compute pairwise
    Sprache Englisch
    Erscheinungsdatum 2024-03-13
    Erscheinungsland United States
    Dokumenttyp Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: Leave the light on: chronic optogenetic tachypacing of human engineered cardiac tissue constructs.

    Boyle, Patrick M / Trayanova, Natalia A

    Cardiovascular research

    2020  Band 116, Heft 8, Seite(n) 1405–1406

    Mesh-Begriff(e) Heart ; Humans ; Optogenetics ; Tissue Engineering
    Sprache Englisch
    Erscheinungsdatum 2020-02-27
    Erscheinungsland England
    Dokumenttyp Editorial ; Comment
    ZDB-ID 80340-6
    ISSN 1755-3245 ; 0008-6363
    ISSN (online) 1755-3245
    ISSN 0008-6363
    DOI 10.1093/cvr/cvaa029
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel ; Online: Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

    Trayanova, Natalia A / Lyon, Aurore / Shade, Julie / Heijman, Jordi

    Physiological reviews

    2023  Band 104, Heft 3, Seite(n) 1265–1333

    Abstract: The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac ... ...

    Abstract The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
    Mesh-Begriff(e) Humans ; Arrhythmias, Cardiac/physiopathology ; Animals ; Models, Cardiovascular ; Computer Simulation ; Translational Research, Biomedical ; Myocytes, Cardiac/physiology ; Electrophysiological Phenomena/physiology ; Action Potentials/physiology
    Sprache Englisch
    Erscheinungsdatum 2023-12-28
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Review
    ZDB-ID 209902-0
    ISSN 1522-1210 ; 0031-9333
    ISSN (online) 1522-1210
    ISSN 0031-9333
    DOI 10.1152/physrev.00017.2023
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: Endocardial-Epicardial Dissociation in Persistent Atrial Fibrillation: Driver or Bystander Activation Pattern?

    Aronis, Konstantinos N / Trayanova, Natalia A

    Circulation. Arrhythmia and electrophysiology

    2020  Band 13, Heft 8, Seite(n) e009110

    Mesh-Begriff(e) Atrial Fibrillation/diagnosis ; Endocardium ; Epicardial Mapping ; Humans ; Prevalence
    Sprache Englisch
    Erscheinungsdatum 2020-08-18
    Erscheinungsland United States
    Dokumenttyp Editorial ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2426129-4
    ISSN 1941-3084 ; 1941-3149
    ISSN (online) 1941-3084
    ISSN 1941-3149
    DOI 10.1161/CIRCEP.120.009110
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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