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Yu JK, Liang JA, Franceschi WH, Huang Q, Pashakhanloo F, Sung E, Boyle PM, Trayanova NA. Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models. Cardiovasc Res 2021; 118:1247-1261. [PMID: 33881518 DOI: 10.1093/cvr/cvab140] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 01/14/2021] [Accepted: 04/19/2021] [Indexed: 12/24/2022] Open
Abstract
AIMS Direct remuscularization with pluripotent stem cell-derived cardiomyocytes (PSC-CMs) seeks to address the onset of heart failure post-myocardial infarction (MI) by treating the persistent muscle deficiency that underlies it. However, direct remuscularization with PSC-CMs could potentially be arrhythmogenic. We investigated two possible mechanisms of arrhythmogenesis-focal vs reentrant-arising from direct remuscularization with PSC-CM patches in two personalized, human ventricular computer models of post-MI. Moreover, we developed a principled approach for evaluating arrhythmogenicity of direct remuscularization that factors in the VT propensity of the patient-specific post-MI fibrotic substrate and use it to investigate different conditions of patch remuscularization. METHODS & RESULTS Two personalized, human ventricular models of post-MI (P1 & P2) were constructed from late gadolinium enhanced (LGE)-magnetic resonance images (MRI). In each model, remuscularization with PSC-CM patches were simulated under different treatment conditions that included patch engraftment, patch myofibril orientation, remuscularization site, patch size (thickness and diameter), and patch maturation. To determine arrhythmogenicity of treatment conditions, VT burden of heart models was quantified prior to and after simulated remuscularization and compared. VT burden was quantified based on inducibility (i.e., weighted sum of pacing sites that induced) and severity (i.e., the number of distinct VT morphologies induced). Prior to remuscularization, VT burden was significant in P1 (0.275) and not in P2 (0.0, not VT inducible). We highlight that reentrant VT mechanisms would dominate over focal mechanisms; spontaneous beats emerging from PSC-CM grafts were always a fraction of resting sinus rate. Moreover, incomplete patch engraftment can be particularly arrhythmogenic, giving rise to particularly aberrant electrical activation and conduction slowing across the PSC-CM patches along with elevated VT burden when compared to complete engraftment. Under conditions of complete patch engraftment, remuscularization was almost always arrhythmogenic in P2 but certain treatment conditions could be anti-arrhythmogenic in P1. Moreover, the remuscularization site was the most important factor affecting VT burden in both P1 and P2. Complete maturation of PSC-CM patches, both ionically and electrotonically, at the appropriate site could completely alleviate VT burden. CONCLUSION We identified that reentrant VT would be the primary VT mechanism in patch remuscularization. To evaluate the arrhythmogenicity of remuscularization, we developed a principled approach that factors in the propensity of the patient-specific fibrotic substrate for VT. We showed that arrhythmogenicity is sensitive to the patient-specific fibrotic substrate and remuscularization site. We demonstrate that targeted remuscularization can be safe in the appropriate individual and holds the potential to nondestructively eliminate VT post-MI in addition to addressing muscle deficiency underlying heart failure progression. TRANSLATIONAL PERSPECTIVE If safety from ventricular arrhythmias can be addressed, direct remuscularization with PSC-CMs-achieved either through engineered myocardial patches or intramyocardial injections-holds the potential to halt heart failure progression post-MI. Using personalized 3 D models of the post-MI ventricles derived from LGE-MRI, we provide evidence that arrhythmogenesis following remuscularization with PSC-CM patches is driven by a reentrant as opposed to focal VT mechanism. Moreover, the existing patient-specific fibrotic substrate together with the remuscularization site were primary determinants of arrhythmogenesis. These results suggest that the clinical safety of remuscularization can be achieved through patient-specific optimization guided in-part by computational modeling.
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Affiliation(s)
- Joseph K Yu
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, 3400 N Charles Street, 216 Hackerman, Baltimore, MD, USA
| | - Jialiu A Liang
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA
| | - William H Franceschi
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA
| | - Qinwen Huang
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA
| | - Farhad Pashakhanloo
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA
| | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, 3400 N Charles Street, 216 Hackerman, Baltimore, MD, USA
| | - Patrick M Boyle
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Bioengineering, University of Washington, Seattle, WA, USA.,Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.,Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, 21218, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, 3400 N Charles Street, 216 Hackerman, Baltimore, MD, USA
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Boyle PM, Franceschi WH, Constantin M, Hawks C, Desplantez T, Trayanova NA, Vigmond EJ. New insights on the cardiac safety factor: Unraveling the relationship between conduction velocity and robustness of propagation. J Mol Cell Cardiol 2019; 128:117-128. [PMID: 30677394 DOI: 10.1016/j.yjmcc.2019.01.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 01/11/2019] [Accepted: 01/14/2019] [Indexed: 01/31/2023]
Abstract
Cardiac conduction disturbances are linked with arrhythmia development. The concept of safety factor (SF) has been derived to describe the robustness of conduction, but the usefulness of this metric has been constrained by several limitations. For example, due to the difficulty of measuring the necessary input variables, SF calculations have only been applied to synthetic data. Moreover, quantitative validation of SF is lacking; specifically, the practical meaning of particular SF values is unclear, aside from the fact that propagation failure (i.e., conduction block) is characterized by SF < 1. This study aims to resolve these limitations for our previously published SF formulation and explore its relationship to relevant electrophysiological properties of cardiac tissue. First, HL-1 cardiomyocyte monolayers were grown on multi-electrode arrays and the robustness of propagation was estimated using extracellular potential recordings. SF values reconstructed purely from experimental data were largely between 1 and 5 (up to 89.1% of sites characterized). This range is consistent with values derived from synthetic data, proving that the formulation is sound and its applicability is not limited to analysis of computational models. Second, for simulations conducted in 1-, 2-, and 3-dimensional tissue blocks, we calculated true SF values at locations surrounding the site of current injection for sub- and supra-threshold stimuli and found that they differed from values estimated by our SF formulation by <10%. Finally, we examined SF dynamics under conditions relevant to arrhythmia development in order to provide physiological insight. Our analysis shows that reduced conduction velocity (Θ) caused by impaired intrinsic cell-scale excitability (e.g., due to sodium current a loss-of-function mutation) is associated with less robust conduction (i.e., lower SF); however, intriguingly, Θ variability resulting from modulation of tissue scale conductivity has no effect on SF. These findings are supported by analytic derivation of the relevant relationships from first principles. We conclude that our SF formulation, which can be applied to both experimental and synthetic data, produces values that vary linearly with the excess charge needed for propagation. SF calculations can provide insights helpful in understanding the initiation and perpetuation of cardiac arrhythmia.
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Affiliation(s)
- Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
| | - William H Franceschi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Marion Constantin
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Pessac-Bordeaux, France
| | - Claudia Hawks
- Department of Physics and Applied Mathematics at the University of Navarra, Pamplona, Spain
| | - Thomas Desplantez
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Pessac-Bordeaux, France; INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Pessac-Bordeaux, France; Université de Bordeaux, Talence, France.
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Boyle PM, Hakim JB, Zahid S, Franceschi WH, Murphy MJ, Prakosa A, Aronis KN, Zghaib T, Balouch M, Ipek EG, Chrispin J, Berger RD, Ashikaga H, Marine JE, Calkins H, Nazarian S, Spragg DD, Trayanova NA. The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping. Front Physiol 2018; 9:1151. [PMID: 30210356 PMCID: PMC6123380 DOI: 10.3389/fphys.2018.01151] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/31/2018] [Indexed: 12/19/2022] Open
Abstract
Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RDsim and RDFIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RDsim sites. Regions harboring RDsim and RDFIRM were compared. RDsim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RDFIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RDsim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RDsim in 20 of 24 (83%) atrial regions identified as RDFIRM-harboring during clinical mapping. In a large number of cases, we uncovered RDsim-harboring regions in which RDFIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases “latent” RDsim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RDsim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RDsim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joe B Hakim
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Sohail Zahid
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William H Franceschi
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Michael J Murphy
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | | | - Tarek Zghaib
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Muhammed Balouch
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Esra G Ipek
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jonathan Chrispin
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Ronald D Berger
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hiroshi Ashikaga
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Joseph E Marine
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hugh Calkins
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Saman Nazarian
- Penn Heart & Vascular Center, University of Pennsylvania, Philadelphia, PA, United States
| | - David D Spragg
- Department of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
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Boyle PM, Hakim JB, Zahid S, Franceschi WH, Murphy MJ, Vigmond EJ, Dubois R, Haïssaguerre M, Hocini M, Jaïs P, Trayanova NA, Cochet H. Comparing Reentrant Drivers Predicted by Image-Based Computational Modeling and Mapped by Electrocardiographic Imaging in Persistent Atrial Fibrillation. Front Physiol 2018; 9:414. [PMID: 29725307 PMCID: PMC5917348 DOI: 10.3389/fphys.2018.00414] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Accepted: 04/04/2018] [Indexed: 02/06/2023] Open
Abstract
Electrocardiographic mapping (ECGI) detects reentrant drivers (RDs) that perpetuate arrhythmia in persistent AF (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) identify all latent sites in the fibrotic substrate that could potentially sustain RDs, not just those manifested during mapped AF. The objective of this study was to compare RDs from simulations and ECGI (RDsim/RDECGI) and analyze implications for ablation. We considered 12 PsAF patients who underwent RDECGI ablation. For the same cohort, we simulated AF and identified RDsim sites in patient-specific models with geometry and fibrosis distribution from pre-ablation LGE-MRI. RDsim- and RDECGI-harboring regions were compared, and the extent of agreement between macroscopic locations of RDs identified by simulations and ECGI was assessed. Effects of ablating RDECGI/RDsim were analyzed. RDsim were predicted in 28 atrial regions (median [inter-quartile range (IQR)] = 3.0 [1.0; 3.0] per model). ECGI detected 42 RDECGI-harboring regions (4.0 [2.0; 5.0] per patient). The number of regions with RDsim and RDECGI per individual was not significantly correlated (R = 0.46, P = ns). The overall rate of regional agreement was fair (modified Cohen's κ0 statistic = 0.11), as expected, based on the different mechanistic underpinning of RDsim- and RDECGI. nineteen regions were found to harbor both RDsim and RDECGI, suggesting that a subset of clinically observed RDs was fibrosis-mediated. The most frequent source of differences (23/32 regions) between the two modalities was the presence of RDECGI perpetuated by mechanisms other than the fibrotic substrate. In 6/12 patients, there was at least one region where a latent RD was observed in simulations but was not manifested during clinical mapping. Ablation of fibrosis-mediated RDECGI (i.e., targets in regions that also harbored RDsim) trended toward a higher rate of positive response compared to ablation of other RDECGI targets (57 vs. 41%, P = ns). Our analysis suggests that RDs in human PsAF are at least partially fibrosis-mediated. Substrate-based ablation combining simulations with ECGI could improve outcomes.
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Affiliation(s)
- Patrick M Boyle
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joe B Hakim
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Sohail Zahid
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William H Franceschi
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Michael J Murphy
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Edward J Vigmond
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France
| | - Rémi Dubois
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France
| | - Michel Haïssaguerre
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
| | - Mélèze Hocini
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
| | - Pierre Jaïs
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Hubert Cochet
- L'Institut de RYthmologie et Modélisation Cardiaque (IHU-LIRYC), Pessac-Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Pessac-Bordeaux, France
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Deng D, Murphy MJ, Hakim JB, Franceschi WH, Zahid S, Pashakhanloo F, Trayanova NA, Boyle PM. Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate. Chaos 2017; 27:093932. [PMID: 28964164 PMCID: PMC5605332 DOI: 10.1063/1.5003340] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 09/04/2017] [Indexed: 05/30/2023]
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), which promote fibrillatory activity. Targeted catheter ablation of RD-harboring tissues has shown promise as a clinical treatment for PsAF, but the outcomes remain sub-par. Personalized computational modeling has been proposed as a means of non-invasively predicting optimal ablation targets in individual PsAF patients, but it remains unclear how RD localization dynamics are influenced by inter-patient variability in the spatial distribution of atrial fibrosis, action potential duration (APD), and conduction velocity (CV). Here, we conduct simulations in computational models of fibrotic atria derived from the clinical imaging of PsAF patients to characterize the sensitivity of RD locations to these three factors. We show that RDs consistently anchor to boundaries between fibrotic and non-fibrotic tissues, as delineated by late gadolinium-enhanced magnetic resonance imaging, but those changes in APD/CV can enhance or attenuate the likelihood that an RD will anchor to a specific site. These findings show that the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e., incorporating each individual's unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodeled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not produce the desired result.
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Affiliation(s)
- Dongdong Deng
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Michael J Murphy
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Joe B Hakim
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - William H Franceschi
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Sohail Zahid
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Farhad Pashakhanloo
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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