This article investigates the quantitative predictive capabilities of region-specific models by comparing experimental electrograms obtained from in vivo mapping of the ventricular free wall with those obtained through simulation of a region specific three-dimensional bidomain model that incorporates measured fiber orientations. Epicardial electrograms were recorded from canine left ventricles during and after unipolar pacing using a 528-channel electrode plaque. Fiber directions throughout the tissue were estimated from diffusion-weighted MRI and from pace mapping. Electrograms were computed in the bidomain model with experimentally derived properties during paced activations at the same spatiotemporal resolution as those recorded experimentally. Epicardial potentials from model and experiment were directly compared, and sensitivities of these comparisons to reference electrode location and to the choice of material properties were analyzed. The comparisons performed here demonstrate, that (1) the stimulus artifact can be used to estimate the in vivo myocardial fiber architecture, (2) the correlation between simulated and experimental electrograms decreases with increasing pacing depth, and (3) the quantitative comparisons between bidomain model and experimental data are sensitive to both the description of the fiber architecture, and the location of the unipolar reference electrode, but relatively insensitive to moderate changes in the bidomain conductivities.
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Int J Biol Macromol
December 2024
Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, via Campi 103, 41125 Modena, Italy. Electronic address:
G protein coupled receptors (GPCRs) are critically regulated by arrestins. In this study, high-resolution data was combined with molecular dynamics simulations to infer the determinants of β-arrestin 1 (βarr1)-GPCR coupling, using the V2 vasopressin receptor (V2R) as a model system. The study highlighted the extremely high plasticity of βarr1-GPCR complexes, dependent on receptor type, state, and membrane environment.
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July 2024
Simula Research Laboratory, Oslo, Norway.
Computational techniques have significantly advanced our understanding of cardiac electrophysiology, yet they have predominantly concentrated on averaged models that do not represent the intricate dynamics near individual cardiomyocytes. Recently, accurate models representing individual cells have gained popularity, enabling analysis of the electrophysiology at the micrometer level. Here, we evaluate five mathematical models to determine their computational efficiency and physiological fidelity.
View Article and Find Full Text PDFSci Adv
July 2024
Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York (SUNY), Buffalo, NY, USA.
Cell therapy for the treatment of demyelinating diseases such as multiple sclerosis is hampered by poor survival of donor oligodendrocyte cell preparations, resulting in limited therapeutic outcomes. Excessive cell death leads to the release of intracellular alloantigens, which likely exacerbate local inflammation and may predispose the graft to eventual rejection. Here, we engineered innovative cell-instructive shear-thinning hydrogels (STHs) with tunable viscoelasticity and bioactivity for minimally invasive delivery of primary human oligodendrocyte progenitor cells (hOPCs) to the brain of a mouse, a model of congenital hypomyelinating disease.
View Article and Find Full Text PDFPLoS One
July 2024
Dipartimento di Matematica, Università degli Studi di Pavia, Pavia, Italy.
Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. While prior studies hint at CACNA1C mutations' role in ventricular arrhythmia genesis, the mechanisms, especially in G406R presence, are not fully understood. This computational study explores how the G406R mutation, causing increased transmural dispersion of repolarization, induces and sustains reentrant ventricular arrhythmias.
View Article and Find Full Text PDFNPJ Parkinsons Dis
June 2024
Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Quantification of motor symptom progression in Parkinson's disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution.
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