852 research outputs found

    Advances in computational modelling for personalised medicine after myocardial infarction

    Get PDF
    Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

    High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence

    Get PDF
    Acute myocardial ischemia is a precursor of sudden arrhythmic death. Variability in its manifestation hampers understanding of arrhythmia mechanisms and challenges risk stratification. Our aim is to unravel the mechanisms underlying how size, transmural extent and location of ischemia determine arrhythmia vulnerability and ecG alterations. High performance computing simulations using a human torso/biventricular biophysically-detailed model were conducted to quantify the impact of varying ischemic region properties, including location (LAD/LcX occlusion), transmural/subendocardial ischemia, size, and normal/slow myocardial propagation. ecG biomarkers and vulnerability window for reentry were computed in over 400 simulations for 18 cases evaluated. Two distinct mechanisms explained larger vulnerability to reentry in transmural versus subendocardial ischemia. Macro-reentry around the ischemic region was the primary mechanism increasing arrhythmic risk in transmural versus subendocardial ischemia, for both LAD and LcX occlusion. transmural micro-reentry at the ischemic border zone explained arrhythmic vulnerability in subendocardial ischemia, especially in LAD occlusion, as reentries were favoured by the ischemic region intersecting the septo-apical region. St elevation reflected ischemic extent in transmural ischemia for LCX and LAD occlusion but not in subendocardial ischemia (associated with mild St depression). the technology and results presented can inform safety and efficacy evaluation of anti-arrhythmic therapy in acute myocardial ischemia

    Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

    Get PDF
    Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

    Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

    Get PDF
    Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

    Arrhythmic risk biomarkers for the assessment of drug cardiotoxicity: from experiments to computer simulations

    Get PDF
    In this paper, we illustrate how advanced computational modelling and simulation can be used to investigate drug-induced effects on cardiac electrophysiology and on specific biomarkers of pro-arrhythmic risk. To do so, we first perform a thorough literature review of proposed arrhythmic risk biomarkers from the ionic to the electrocardiogram levels. The review highlights the variety of proposed biomarkers, the complexity of the mechanisms of drug-induced pro-arrhythmia and the existence of significant animal species differences in drug-induced effects on cardiac electrophysiology. Predicting drug-induced pro-arrhythmic risk solely using experiments is challenging both preclinically and clinically, as attested by the rise in the cost of releasing new compounds to the market. Computational modelling and simulation has significantly contributed to the understanding of cardiac electrophysiology and arrhythmias over the last 40 years. In the second part of this paper, we illustrate how state-of-the-art open source computational modelling and simulation tools can be used to simulate multi-scale effects of drug-induced ion channel block in ventricular electrophysiology at the cellular, tissue and whole ventricular levels for different animal species. We believe that the use of computational modelling and simulation in combination with experimental techniques could be a powerful tool for the assessment of drug safety pharmacology

    Human Purkinje in silico model enables mechanistic investigations into automaticity and pro-arrhythmic abnormalities

    Get PDF
    Cardiac Purkinje cells (PCs) are implicated in lethal arrhythmias caused by cardiac diseases, mutations, and drug action. However, the pro-arrhythmic mechanisms in PCs are not entirely understood, particularly in humans, as most investigations are conducted in animals. The aims of this study are to present a novel human PCs elec- trophysiology biophysically-detailed computational model, and to disentangle ionic mechanisms of human Purkinje-related electrophysiology, pacemaker activity and arrhythmogenicity. The new Trovato2020 model incorporates detailed Purkinje-specific ionic currents and Ca2+ handling, and was developed, calibrated and validated using human experimental data acquired at multiple frequencies, both in control conditions and fol- lowing drug application. Multiscale investigations were performed in a Purkinje cell, in fibre and using an experimentally-calibrated population of PCs to evaluate biological variability. Simulations demonstrate the human Purkinje Trovato2020 model is the first one to yield: (i) all key AP features consistent with human Purkinje recordings; (ii) Automaticity with funny current up-regulation (iii) EADs at slow pacing and with 85% hERG block; (iv) DADs following fast pacing; (v) conduction velocity of 160 cm/s in a Purkinje fibre, as reported in human. The human in silico PCs population highlights that: (1) EADs are caused by ICaL reactivation in PCs with large inward currents; (2) DADs and triggered APs occur in PCs experiencing Ca2+ accumulation, at fast pacing, caused by large L-type calcium current and small Na+/Ca2+ exchanger. The novel human Purkinje model unlocks further investigations into the role of cardiac Purkinje in ventricular arrhythmias through computer modeling and multiscale simulations

    Intercellular Communication in the Heart: Therapeutic Opportunities for Cardiac Ischemia

    Get PDF
    The maintenance of tissue, organ, and organism homeostasis relies on an intricate network of players and mechanisms that assist in the different forms of cell–cell communication. Myocardial infarction, following heart ischemia and reperfusion, is associated with profound changes in key processes of intercellular communication, involving gap junctions, extracellular vesicles, and tunneling nanotubes, some of which have been implicated in communication defects associated with cardiac injury, namely arrhythmogenesis and progression into heart failure. Therefore, intercellular communication players have emerged as attractive powerful therapeutic targets aimed at preserving a fine-tuned crosstalk between the different cardiac cells in order to prevent or repair some of harmful consequences of heart ischemia and reperfusion, re-establishing myocardial function

    The role of beta-adrenergic system remodeling in human heart failure: A mechanistic investigation

    Full text link
    [EN] ß-adrenergic receptor antagonists (ß-blockers) are extensively used to improve cardiac performance in heart failure (HF), but the electrical improvements with these clinical treatments are not fully understood. The aim of this study was to analyze the electrophysiological effects of ß-adrenergic system remodeling in heart failure with reduced ejection fraction and the underlying mechanisms. We used a combined mathematical model that integrated ß-adrenergic signaling with electrophysiology and calcium cycling in human ventricular myocytes. HF remodeling, both in the electrophysiological and signaling systems, was introduced to quantitatively analyze changes in electrophysiological properties due to the stimulation of ß-adrenergic receptors in failing myocytes. We found that the inotropic effect of ß-adrenergic stimulation was reduced in HF due to the altered Ca2+ dynamics resulting from the combination of structural, electrophysiological and signaling remodeling. Isolated cells showed proarrhythmic risk after sympathetic stimulation because early afterdepolarizations appeared, and the vulnerability was greater in failing myocytes. When analyzing coupled cells, ß-adrenergic stimulation reduced transmural repolarization gradients between endocardium and epicardium in normal tissue, but was less effective at reducing these gradients after HF remodeling. The comparison of the selective activation of ß-adrenergic isoforms revealed that the response to ß2-adrenergic receptors stimulation was blunted in HF while ß1-adrenergic receptors downstream effectors regulated most of the changes observed after sympathetic stimulation. In conclusion, this study was able to reproduce an altered ß-adrenergic activity on failing myocytes and to explain the mechanisms involved. The derived predictions could help in the treatment of HF and guide in the design of future experiments.This work was partially supported by the "Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016" from the Ministerio de Economía, Industria y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) DPI2016-75799-R (AEI/FEDER, UE), by the "Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020" from the Ministerio de Ciencia e Innovación y Universidades (PID2019-104356RB-C41/AEI/10.13039/5011000110 33), and by the "Programa de Ayudas de Investigación y Desarrollo (PAID-01-17)" from the Universitat Politècnica de València.Mora-Fenoll, MT.; Gong, JQX.; Sobie, EA.; Trenor Gomis, BA. (2021). The role of beta-adrenergic system remodeling in human heart failure: A mechanistic investigation. Journal of Molecular and Cellular Cardiology. 153:14-25. https://doi.org/10.1016/j.yjmcc.2020.12.004S1425153Coronel, R., Wilders, R., Verkerk, A. O., Wiegerinck, R. F., Benoist, D., & Bernus, O. (2013). Electrophysiological changes in heart failure and their implications for arrhythmogenesis. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1832(12), 2432-2441. doi:10.1016/j.bbadis.2013.04.002Antoons, G., Oros, A., Bito, V., Sipido, K. R., & Vos, M. A. (2007). Cellular basis for triggered ventricular arrhythmias that occur in the setting of compensated hypertrophy and heart failure: considerations for diagnosis and treatment. Journal of Electrocardiology, 40(6), S8-S14. doi:10.1016/j.jelectrocard.2007.05.022Johnson, D. M., & Antoons, G. (2018). Arrhythmogenic Mechanisms in Heart Failure: Linking β-Adrenergic Stimulation, Stretch, and Calcium. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01453Saucerman, J. J., & McCulloch, A. D. (2004). Mechanistic systems models of cell signaling networks: a case study of myocyte adrenergic regulation. Progress in Biophysics and Molecular Biology, 85(2-3), 261-278. doi:10.1016/j.pbiomolbio.2004.01.005A. William Tank, D. Lee Wong, Peripheral and Central Effects of Circulating Catecholamines, in: Compr. Physiol., John Wiley & Sons, Inc., Hoboken, NJ, USA, 2014: pp. 1–15. doi:https://doi.org/10.1002/cphy.c140007.Lohse, M. J., Engelhardt, S., & Eschenhagen, T. (2003). What Is the Role of β-Adrenergic Signaling in Heart Failure? Circulation Research, 93(10), 896-906. doi:10.1161/01.res.0000102042.83024.caPort, J. D., & Bristow, M. R. (2001). Altered Beta-adrenergic Receptor Gene Regulation and Signaling in Chronic Heart Failure. Journal of Molecular and Cellular Cardiology, 33(5), 887-905. doi:10.1006/jmcc.2001.1358Bozkurt, B. (2018). What Is New in Heart Failure Management in 2017? Update on ACC/AHA Heart Failure Guidelines. Current Cardiology Reports, 20(6). doi:10.1007/s11886-018-0978-7Kubon, C., Mistry, N. B., Grundvold, I., Halvorsen, S., Kjeldsen, S. E., & Westheim, A. S. (2011). The role of beta-blockers in the treatment of chronic heart failure. Trends in Pharmacological Sciences, 32(4), 206-212. doi:10.1016/j.tips.2011.01.006S. Chatterjee, G. Biondi-Zoccai, A. Abbate, F. D'Ascenzo, D. Castagno, B. Van Tassell, D. Mukherjee, E. Lichstein, Benefits of β blockers in patients with heart failure and reduced ejection fraction: network meta-analysis., BMJ. 346 (2013) f55. doi:https://doi.org/10.1136/bmj.f55.Baker, J. G. (2005). The selectivity of β -adrenoceptor antagonists at the human β 1, β 2 and β 3 adrenoceptors. British Journal of Pharmacology, 144(3), 317-322. doi:10.1038/sj.bjp.0706048Poole-Wilson, P. A., Swedberg, K., Cleland, J. G., Di Lenarda, A., Hanrath, P., Komajda, M., … Skene, A. (2003). Comparison of carvedilol and metoprolol on clinical outcomes in patients with chronic heart failure in the Carvedilol Or Metoprolol European Trial (COMET): randomised controlled trial. The Lancet, 362(9377), 7-13. doi:10.1016/s0140-6736(03)13800-7Heng, M. K. (1990). Beta, partial agonists to treat heart failure: Effects of xamoterol upon cardiac function and clinical status. Clinical Cardiology, 13(3), 171-176. doi:10.1002/clc.4960130305Soltis, A. R., & Saucerman, J. J. (2010). Synergy between CaMKII Substrates and β-Adrenergic Signaling in Regulation of Cardiac Myocyte Ca2+ Handling. Biophysical Journal, 99(7), 2038-2047. doi:10.1016/j.bpj.2010.08.016Rozier, K., & Bondarenko, V. E. (2017). Distinct physiological effects of β1- and β2-adrenoceptors in mouse ventricular myocytes: insights from a compartmentalized mathematical model. American Journal of Physiology-Cell Physiology, 312(5), C595-C623. doi:10.1152/ajpcell.00273.2016Heijman, J., Volders, P. G. A., Westra, R. L., & Rudy, Y. (2011). Local control of β-adrenergic stimulation: Effects on ventricular myocyte electrophysiology and Ca2+-transient. Journal of Molecular and Cellular Cardiology, 50(5), 863-871. doi:10.1016/j.yjmcc.2011.02.007O’Hara, T., & Rudy, Y. (2012). Arrhythmia formation in subclinical («silent») long QT syndrome requires multiple insults: Quantitative mechanistic study using the KCNQ1 mutation Q357R as example. Heart Rhythm, 9(2), 275-282. doi:10.1016/j.hrthm.2011.09.066Gong, J. Q. X., Susilo, M. E., Sher, A., Musante, C. J., & Sobie, E. A. (2020). Quantitative analysis of variability in an integrated model of human ventricular electrophysiology and β-adrenergic signaling. Journal of Molecular and Cellular Cardiology, 143, 96-106. doi:10.1016/j.yjmcc.2020.04.009Sanchez-Alonso, J. L., Bhargava, A., O’Hara, T., Glukhov, A. V., Schobesberger, S., Bhogal, N., … Gorelik, J. (2016). Microdomain-Specific Modulation of L-Type Calcium Channels Leads to Triggered Ventricular Arrhythmia in Heart Failure. Circulation Research, 119(8), 944-955. doi:10.1161/circresaha.116.308698Lang, D., Holzem, K., Kang, C., Xiao, M., Hwang, H. J., Ewald, G. A., … Efimov, I. R. (2015). Arrhythmogenic Remodeling of β 2 Versus β 1 Adrenergic Signaling in the Human Failing Heart. Circulation: Arrhythmia and Electrophysiology, 8(2), 409-419. doi:10.1161/circep.114.002065Passini, E., Trovato, C., Morissette, P., Sannajust, F., Bueno‐Orovio, A., & Rodriguez, B. (2019). Drug‐induced shortening of the electromechanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias. British Journal of Pharmacology, 176(19), 3819-3833. doi:10.1111/bph.14786Heidenreich, E. A., Ferrero, J. M., Doblaré, M., & Rodríguez, J. F. (2010). Adaptive Macro Finite Elements for the Numerical Solution of Monodomain Equations in Cardiac Electrophysiology. Annals of Biomedical Engineering, 38(7), 2331-2345. doi:10.1007/s10439-010-9997-2Glukhov, A. V., Fedorov, V. V., Lou, Q., Ravikumar, V. K., Kalish, P. W., Schuessler, R. B., … Efimov, I. R. (2010). Transmural Dispersion of Repolarization in Failing and Nonfailing Human Ventricle. Circulation Research, 106(5), 981-991. doi:10.1161/circresaha.109.204891Antzelevitch, C. (2010). M Cells in the Human Heart. Circulation Research, 106(5), 815-817. doi:10.1161/circresaha.109.216226Bristow, M. R., Ginsburg, R., Umans, V., Fowler, M., Minobe, W., Rasmussen, R., … Jamieson, S. (1986). Beta 1- and beta 2-adrenergic-receptor subpopulations in nonfailing and failing human ventricular myocardium: coupling of both receptor subtypes to muscle contraction and selective beta 1-receptor down-regulation in heart failure. Circulation Research, 59(3), 297-309. doi:10.1161/01.res.59.3.297Bers, D. M. (2002). Cardiac excitation–contraction coupling. Nature, 415(6868), 198-205. doi:10.1038/415198aVeldkamp, M. (2001). Norepinephrine induces action potential prolongation and early afterdepolarizations in ventricular myocytes isolated from human end-stage failing hearts. European Heart Journal, 22(11), 955-963. doi:10.1053/euhj.2000.2499Wang, Y., Yuan, J., Qian, Z., Zhang, X., Chen, Y., Hou, X., & Zou, J. (2015). β2 adrenergic receptor activation governs cardiac repolarization and arrhythmogenesis in a guinea pig model of heart failure. Scientific Reports, 5(1). doi:10.1038/srep07681Lowe, M. D. (2001). beta2 Adrenergic receptors mediate important electrophysiological effects in human ventricular myocardium. Heart, 86(1), 45-51. doi:10.1136/heart.86.1.45Nikolaev, V. O., Bünemann, M., Schmitteckert, E., Lohse, M. J., & Engelhardt, S. (2006). Cyclic AMP Imaging in Adult Cardiac Myocytes Reveals Far-Reaching β 1 -Adrenergic but Locally Confined β 2 -Adrenergic Receptor–Mediated Signaling. Circulation Research, 99(10), 1084-1091. doi:10.1161/01.res.0000250046.69918.d5A.D. Loucks, T. O'Hara, N.A. Trayanova, Degradation of T-tubular microdomains and altered cAMP Compartmentation Lead to emergence of Arrhythmogenic triggers in heart failure Myocytes: an in silico study, Front. Physiol. 9 (2018) 1–12. doi:https://doi.org/10.3389/fphys.2018.01737.Rocchetti, M., Alemanni, M., Mostacciuolo, G., Barassi, P., Altomare, C., Chisci, R., … Zaza, A. (2008). Modulation of Sarcoplasmic Reticulum Function by PST2744 [Istaroxime; (E,Z)-3-((2-Aminoethoxy)imino) Androstane-6,17-dione Hydrochloride)] in a Pressure-Overload Heart Failure Model. Journal of Pharmacology and Experimental Therapeutics, 326(3), 957-965. doi:10.1124/jpet.108.138701Dong, X., & Thomas, D. D. (2014). Time-resolved FRET reveals the structural mechanism of SERCA–PLB regulation. Biochemical and Biophysical Research Communications, 449(2), 196-201. doi:10.1016/j.bbrc.2014.04.166Lucia, C. de, Eguchi, A., & Koch, W. J. (2018). New Insights in Cardiac β-Adrenergic Signaling During Heart Failure and Aging. Frontiers in Pharmacology, 9. doi:10.3389/fphar.2018.00904Ungerer, M., Böhm, M., Elce, J. S., Erdmann, E., & Lohse, M. J. (1993). Altered expression of beta-adrenergic receptor kinase and beta 1-adrenergic receptors in the failing human heart. Circulation, 87(2), 454-463. doi:10.1161/01.cir.87.2.454Böhm, M., Eschenhagen, T., Gierschik, P., Larisch, K., Lensche, H., Mende, U., … Erdmann, E. (1994). Radioimmunochemical Quantification of Giα in Right and Left Vehicles from Patients with Ischaemic and Dilated Cardiomyopathy and Predominant Left Ventricular Failure. Journal of Molecular and Cellular Cardiology, 26(2), 133-149. doi:10.1006/jmcc.1994.1017Woo, A. Y.-H., Song, Y., Xiao, R.-P., & Zhu, W. (2014). Biased β2-adrenoceptor signalling in heart failure: pathophysiology and drug discovery. British Journal of Pharmacology, 172(23), 5444-5456. doi:10.1111/bph.12965Schobesberger, S., Wright, P., Tokar, S., Bhargava, A., Mansfield, C., Glukhov, A. V., … Gorelik, J. (2017). T-tubule remodelling disturbs localized β2-adrenergic signalling in rat ventricular myocytes during the progression of heart failure. Cardiovascular Research, 113(7), 770-782. doi:10.1093/cvr/cvx074Bhogal, N., Hasan, A., & Gorelik, J. (2018). The Development of Compartmentation of cAMP Signaling in Cardiomyocytes: The Role of T-Tubules and Caveolae Microdomains. Journal of Cardiovascular Development and Disease, 5(2), 25. doi:10.3390/jcdd5020025DeSantiago, J., Ai, X., Islam, M., Acuna, G., Ziolo, M. T., Bers, D. M., & Pogwizd, S. M. (2008). Arrhythmogenic Effects of β 2 -Adrenergic Stimulation in the Failing Heart Are Attributable to Enhanced Sarcoplasmic Reticulum Ca Load. Circulation Research, 102(11), 1389-1397. doi:10.1161/circresaha.107.169011Altschuld, R. A., Starling, R. C., Hamlin, R. L., Billman, G. E., Hensley, J., Castillo, L., … Lakatta, E. G. (1995). Response of Failing Canine and Human Heart Cells to β 2 -Adrenergic Stimulation. Circulation, 92(6), 1612-1618. doi:10.1161/01.cir.92.6.1612V.O. Nikolaev, A. Moshkov, A.R. Lyon, M. Miragoli, P. Novak, H. Paur, M.J. Lohse, Y.E. Korchev, S.E. Harding, J. Gorelik, Beta2-Adrenergic Receptor Redistribution in Heart Failure Changes cAMP Compartmentation, Science (80-. ). 327 (2010) 1653–1657. doi:https://doi.org/10.1126/science.1185988.Bryant, S. M., Kong, C. H. T., Cannell, M. B., Orchard, C. H., & James, A. F. (2018). Loss of caveolin-3-dependent regulation of ICa in rat ventricular myocytes in heart failure. American Journal of Physiology-Heart and Circulatory Physiology, 314(3), H521-H529. doi:10.1152/ajpheart.00458.2017Wright, P. T., Nikolaev, V. O., O’Hara, T., Diakonov, I., Bhargava, A., Tokar, S., … Gorelik, J. (2014). Caveolin-3 regulates compartmentation of cardiomyocyte beta2-adrenergic receptor-mediated cAMP signaling. Journal of Molecular and Cellular Cardiology, 67, 38-48. doi:10.1016/j.yjmcc.2013.12.003Surdo, N. C., Berrera, M., Koschinski, A., Brescia, M., Machado, M. R., Carr, C., … Zaccolo, M. (2017). FRET biosensor uncovers cAMP nano-domains at β-adrenergic targets that dictate precise tuning of cardiac contractility. Nature Communications, 8(1). doi:10.1038/ncomms15031Neumann, J., Eschenhagen, T., Jones, L. R., Linck, B., Schmitz, W., Scholz, H., & Zimmermann, N. (1997). Increased Expression of Cardiac Phosphatases in Patients with End-stage Heart Failure. Journal of Molecular and Cellular Cardiology, 29(1), 265-272. doi:10.1006/jmcc.1996.0271El-Armouche, A. (2004). Decreased protein and phosphorylation level of the protein phosphatase inhibitor-1 in failing human hearts. Cardiovascular Research, 61(1), 87-93. doi:10.1016/j.cardiores.2003.11.005MacDougall, D. A., Agarwal, S. R., Stopford, E. A., Chu, H., Collins, J. A., Longster, A. L., … Calaghan, S. (2012). Caveolae compartmentalise β2-adrenoceptor signals by curtailing cAMP production and maintaining phosphatase activity in the sarcoplasmic reticulum of the adult ventricular myocyte. Journal of Molecular and Cellular Cardiology, 52(2), 388-400. doi:10.1016/j.yjmcc.2011.06.014Calaghan, S., Kozera, L., & White, E. (2008). Compartmentalisation of cAMP-dependent signalling by caveolae in the adult cardiac myocyte. Journal of Molecular and Cellular Cardiology, 45(1), 88-92. doi:10.1016/j.yjmcc.2008.04.004Akar, F. G., & Rosenbaum, D. S. (2003). Transmural Electrophysiological Heterogeneities Underlying Arrhythmogenesis in Heart Failure. Circulation Research, 93(7), 638-645. doi:10.1161/01.res.0000092248.59479.aeAntzelevitch, C. (2007). Heterogeneity and cardiac arrhythmias: An overview. Heart Rhythm, 4(7), 964-972. doi:10.1016/j.hrthm.2007.03.036Briasoulis, A., Palla, M., & Afonso, L. (2015). Meta-Analysis of the Effects of Carvedilol Versus Metoprolol on All-Cause Mortality and Hospitalizations in Patients With Heart Failure. The American Journal of Cardiology, 115(8), 1111-1115. doi:10.1016/j.amjcard.2015.01.545Shen, M. J., & Zipes, D. P. (2014). Role of the Autonomic Nervous System in Modulating Cardiac Arrhythmias. Circulation Research, 114(6), 1004-1021. doi:10.1161/circresaha.113.302549Grandi, E., & Ripplinger, C. M. (2019). Antiarrhythmic mechanisms of beta blocker therapy. Pharmacological Research, 146, 104274. doi:10.1016/j.phrs.2019.104274Nasr, I. A., Bouzamondo, A., Hulot, J.-S., Dubourg, O., Le Heuzey, J.-Y., & Lechat, P. (2007). Prevention of atrial fibrillation onset by beta-blocker treatment in heart failure: a meta-analysis. European Heart Journal, 28(4), 457-462. doi:10.1093/eurheartj/ehl484Tomek, J., Hao, G., Tomková, M., Lewis, A., Carr, C., Paterson, D. J., … Herring, N. (2019). β-Adrenergic Receptor Stimulation and Alternans in the Border Zone of a Healed Infarct: An ex vivo Study and Computational Investigation of Arrhythmogenesis. Frontiers in Physiology, 10. doi:10.3389/fphys.2019.00350Vinge, L. E., Raake, P. W., & Koch, W. J. (2008). Gene Therapy in Heart Failure. Circulation Research, 102(12), 1458-1470. doi:10.1161/circresaha.108.173195Engelhardt, S., Hein, L., Wiesmann, F., & Lohse, M. J. (1999). Progressive hypertrophy and heart failure in  1-adrenergic receptor transgenic mice. Proceedings of the National Academy of Sciences, 96(12), 7059-7064. doi:10.1073/pnas.96.12.7059Rengo, G., Perrone-Filardi, P., Femminella, G. D., Liccardo, D., Zincarelli, C., de Lucia, C., … Leosco, D. (2012). Targeting the β-Adrenergic Receptor System Through G-Protein–Coupled Receptor Kinase 2: A New Paradigm for Therapy and Prognostic Evaluation in Heart Failure. Circulation: Heart Failure, 5(3), 385-391. doi:10.1161/circheartfailure.112.966895Xiang, Y. K. (2011). Compartmentalization of β-Adrenergic Signals in Cardiomyocytes. Circulation Research, 109(2), 231-244. doi:10.1161/circresaha.110.231340Momose, M., Tyndale-Hines, L., Bengel, F. M., & Schwaiger, M. (2001). How heterogeneous is the cardiac autonomic innervation? Basic Research in Cardiology, 96(6), 539-546. doi:10.1007/s00395017000

    Circadian models of serum potassium, sodium, and calcium concentrations in healthy individuals and their application to cardiac electrophysiology simulations at individual level

    Get PDF
    In the article a brief description of the biological basis of the regulation of human biological clocks was presented in order to introduce the role of circadian rhythms in physiology and specifically in the pharmacological translational tools based on the computational physiology models to motivate the need to provide models of circadian fluctuation in plasma cations. The main aim of the study was to develop statistical models of the circadian rhythm of potassium, sodium, and calcium concentrations in plasma. The developed ion models were further tested by assessing their influence on QT duration (cardiac endpoint) as simulated by the biophysically detailed models of human left ventricular cardiomyocyte. The main results are model equations along with an electronic supplement to the article that contains a fully functional implementation of all models
    corecore