Publications by authors named "Mohamed Elshrif"

One of the aims of population genetics is to identify genetic differences/similarities among individuals of multiple ancestries. Many approaches including principal component analysis, clustering, and maximum likelihood techniques can be used to assign individuals to a given ancestry based on their genetic makeup. Although there are several tools that implement such algorithms, there is a lack of interactive visual platforms to run a variety of algorithms in one place.

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  • * Researchers identified 1,196 rare functional variants related to arrhythmias, with 137 deemed pathogenic, and evaluated their associations with atrial fibrillation (AF) using polygenic risk scores (PRS).
  • * This research highlights the necessity of including diverse populations in genomic studies, as it aims to improve understanding of cardiac arrhythmias and address health disparities in genomic medicine.
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  • Resting ECG is an effective, non-invasive method for evaluating heart electrical activity, with abnormalities linked to clinical biomarkers that may indicate early disease stages, particularly coronary artery disease (CAD).
  • The study analyzed 12-lead ECG data from over 13,000 participants and identified significant associations between ECG traits (like RR, QTc) and various clinical biomarkers, revealing risk factors for conditions like type 2 diabetes and CAD.
  • Machine learning models outperformed traditional regression methods in predicting CAD risk, achieving an impressive area under the curve of 0.84, indicating strong predictive accuracy, with a high odds ratio indicating a significant increase in CAD risk in the top scoring decile.
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During heart failure (HF) at the cellular level, the electrophysiological properties of single myocytes get remodeled, which can trigger the occurrence of ventricular arrhythmias that could be manifested in many forms such as early afterdepolarizations (EADs) and alternans (ALTs). In this paper, based on experimentally observed human HF data, specific ionic and exchanger current strengths are modified from a recently developed human ventricular cell model: the O'Hara-Virág-Varró-Rudy (OVVR) model. A new transmural HF-OVVR model is developed that incorporates HF changes and variability of the observed remodeling.

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Heart failure (HF) is one of the major diseases across the world. During HF the electrophysiology of the failing heart is remodeled, which renders the heart more susceptible to ventricular arrhythmias. In this study, we quantitatively analyze the effects of electrophysiological remodeling of the major currents of human ventricular myocytes on the dynamics of the failing heart.

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Numerical integration of mathematical models of heart cell electrophysiology provides an important computational tool for studying cardiac arrhythmias, but the abundance of available models complicates selecting an appropriate model. We study the behavior of two recently published models of human ventricular action potentials, the Grandi-Pasqualini-Bers (GPB) and the O'Hara-Virág-Varró-Rudy (OVVR) models, and compare the results with four previously published models and with available experimental and clinical data. We find the shapes and durations of action potentials and calcium transients differ between the GPB and OVVR models, as do the magnitudes and rate-dependent properties of transmembrane currents and the calcium transient.

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