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Functional Connectivity in Islets of Langerhans from Mouse Pancreas Tissue Slices

Overview of attention for article published in PLoS Computational Biology, February 2013
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Title
Functional Connectivity in Islets of Langerhans from Mouse Pancreas Tissue Slices
Published in
PLoS Computational Biology, February 2013
DOI 10.1371/journal.pcbi.1002923
Pubmed ID
Authors

Andraž Stožer, Marko Gosak, Jurij Dolenšek, Matjaž Perc, Marko Marhl, Marjan Slak Rupnik, Dean Korošak

Abstract

We propose a network representation of electrically coupled beta cells in islets of Langerhans. Beta cells are functionally connected on the basis of correlations between calcium dynamics of individual cells, obtained by means of confocal laser-scanning calcium imaging in islets from acute mouse pancreas tissue slices. Obtained functional networks are analyzed in the light of known structural and physiological properties of islets. Focusing on the temporal evolution of the network under stimulation with glucose, we show that the dynamics are more correlated under stimulation than under non-stimulated conditions and that the highest overall correlation, largely independent of Euclidean distances between cells, is observed in the activation and deactivation phases when cells are driven by the external stimulus. Moreover, we find that the range of interactions in networks during activity shows a clear dependence on the Euclidean distance, lending support to previous observations that beta cells are synchronized via calcium waves spreading throughout islets. Most interestingly, the functional connectivity patterns between beta cells exhibit small-world properties, suggesting that beta cells do not form a homogeneous geometric network but are connected in a functionally more efficient way. Presented results provide support for the existing knowledge of beta cell physiology from a network perspective and shed important new light on the functional organization of beta cell syncitia whose structural topology is probably not as trivial as believed so far.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Unknown 73 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 27%
Researcher 19 26%
Student > Master 5 7%
Professor 4 5%
Student > Bachelor 3 4%
Other 9 12%
Unknown 14 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 20%
Medicine and Dentistry 12 16%
Agricultural and Biological Sciences 11 15%
Engineering 6 8%
Physics and Astronomy 4 5%
Other 7 9%
Unknown 19 26%
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 01 March 2013.
All research outputs
#20,688,303
of 25,411,814 outputs
Outputs from PLoS Computational Biology
#8,219
of 8,976 outputs
Outputs of similar age
#158,263
of 205,249 outputs
Outputs of similar age from PLoS Computational Biology
#126
of 156 outputs
Altmetric has tracked 25,411,814 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 156 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.