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Mathematical Modeling of Heterogeneous Electrophysiological Responses in Human β-Cells

Overview of attention for article published in PLoS Computational Biology, January 2014
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Title
Mathematical Modeling of Heterogeneous Electrophysiological Responses in Human β-Cells
Published in
PLoS Computational Biology, January 2014
DOI 10.1371/journal.pcbi.1003389
Pubmed ID
Authors

Michela Riz, Matthias Braun, Morten Gram Pedersen

Abstract

Electrical activity plays a pivotal role in glucose-stimulated insulin secretion from pancreatic β-cells. Recent findings have shown that the electrophysiological characteristics of human β-cells differ from their rodent counterparts. We show that the electrophysiological responses in human β-cells to a range of ion channels antagonists are heterogeneous. In some cells, inhibition of small-conductance potassium currents has no effect on action potential firing, while it increases the firing frequency dramatically in other cells. Sodium channel block can sometimes reduce action potential amplitude, sometimes abolish electrical activity, and in some cells even change spiking electrical activity to rapid bursting. We show that, in contrast to L-type Ca2+-channels, P/Q-type Ca2+-currents are not necessary for action potential generation, and, surprisingly, a P/Q-type Ca2+-channel antagonist even accelerates action potential firing. By including SK-channels and Ca2+ dynamics in a previous mathematical model of electrical activity in human β-cells, we investigate the heterogeneous and nonintuitive electrophysiological responses to ion channel antagonists, and use our findings to obtain insight in previously published insulin secretion measurements. Using our model we also study paracrine signals, and simulate slow oscillations by adding a glycolytic oscillatory component to the electrophysiological model. The heterogenous electrophysiological responses in human β-cells must be taken into account for a deeper understanding of the mechanisms underlying insulin secretion in health and disease, and as shown here, the interdisciplinary combination of experiments and modeling increases our understanding of human β-cell physiology.

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X Demographics

The data shown below were collected from the profiles of 4 X users 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 4%
United States 1 2%
Russia 1 2%
Unknown 49 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 23%
Researcher 12 23%
Student > Master 5 9%
Student > Doctoral Student 3 6%
Student > Bachelor 3 6%
Other 9 17%
Unknown 9 17%
Readers by discipline Count As %
Engineering 9 17%
Agricultural and Biological Sciences 8 15%
Mathematics 5 9%
Neuroscience 4 8%
Physics and Astronomy 4 8%
Other 12 23%
Unknown 11 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 03 June 2014.
All research outputs
#8,262,445
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#5,490
of 8,960 outputs
Outputs of similar age
#92,478
of 319,345 outputs
Outputs of similar age from PLoS Computational Biology
#73
of 126 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 37th percentile – i.e., 37% 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 319,345 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.