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A second-generation computational modeling of cardiac electrophysiology: response of action potential to ionic concentration changes and metabolic inhibition

Overview of attention for article published in Theoretical Biology and Medical Modelling, October 2014
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Title
A second-generation computational modeling of cardiac electrophysiology: response of action potential to ionic concentration changes and metabolic inhibition
Published in
Theoretical Biology and Medical Modelling, October 2014
DOI 10.1186/1742-4682-11-46
Pubmed ID
Authors

Nour Eddine Alaa, Hamid Lefraich, Imane El Malki

Abstract

Cardiac arrhythmias are becoming one of the major health care problem in the world, causing numerous serious disease conditions including stroke and sudden cardiac death. Furthermore, cardiac arrhythmias are intimately related to the signaling ability of cardiac cells, and are caused by signaling defects. Consequently, modeling the electrical activity of the heart, and the complex signaling models that subtend dangerous arrhythmias such as tachycardia and fibrillation, necessitates a quantitative model of action potential (AP) propagation. Yet, many electrophysiological models, which accurately reproduce dynamical characteristic of the action potential in cells, have been introduced. However, these models are very complex and are very time consuming computationally. Consequently, a large amount of research is consecrated to design models with less computational complexity.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 1 8%
Librarian 1 8%
Student > Doctoral Student 1 8%
Professor 1 8%
Student > Ph. D. Student 1 8%
Other 4 33%
Unknown 3 25%
Readers by discipline Count As %
Medicine and Dentistry 3 25%
Computer Science 2 17%
Engineering 2 17%
Social Sciences 1 8%
Mathematics 1 8%
Other 0 0%
Unknown 3 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 October 2014.
All research outputs
#18,381,794
of 22,768,097 outputs
Outputs from Theoretical Biology and Medical Modelling
#215
of 287 outputs
Outputs of similar age
#185,718
of 259,774 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#6
of 8 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 259,774 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.