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Pluripotent Stem Cells as a Potential Tool for Disease Modelling and Cell Therapy in Diabetes

Overview of attention for article published in Stem Cell Reviews and Reports, March 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)

Mentioned by

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1 Facebook page
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2 Wikipedia pages

Citations

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52 Dimensions

Readers on

mendeley
120 Mendeley
Title
Pluripotent Stem Cells as a Potential Tool for Disease Modelling and Cell Therapy in Diabetes
Published in
Stem Cell Reviews and Reports, March 2014
DOI 10.1007/s12015-014-9503-6
Pubmed ID
Authors

Essam M. Abdelalim, Amélie Bonnefond, Annelise Bennaceur-Griscelli, Philippe Froguel

Abstract

Diabetes mellitus is the most prevailing disease with progressive incidence worldwide. To date, the pathogenesis of diabetes is far to be understood, and there is no permanent treatment available for diabetes. One of the promising approaches to understand and cure diabetes is to use pluripotent stem cells (PSCs), including embryonic stem cells (ESCs) and induced PCSs (iPSCs). ESCs and iPSCs have a great potential to differentiate into all cell types, and they have a high ability to differentiate into insulin-secreting β cells. Obtaining PSCs genetically identical to the patient presenting with diabetes has been a longstanding dream for the in vitro modeling of disease and ultimately cell therapy. For several years, somatic cell nuclear transfer (SCNT) was the method of choice to generate patient-specific ESC lines. However, this technology faces ethical and practical concerns. Interestingly, the recently established iPSC technology overcomes the major problems of other stem cell types including the lack of ethical concern and no risk of immune rejection. Several iPSC lines have been recently generated from patients with different types of diabetes, and most of these cell lines are able to differentiate into insulin-secreting β cells. In this review, we summarize recent advances in the differentiation of pancreatic β cells from PSCs, and describe the challenges for their clinical use in diabetes cell therapy. Furthermore, we discuss the potential use of patient-specific PSCs as an in vitro model, providing new insights into the pathophysiology of diabetes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 119 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 36 30%
Student > Ph. D. Student 17 14%
Student > Master 17 14%
Researcher 15 13%
Student > Doctoral Student 7 6%
Other 14 12%
Unknown 14 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 28%
Agricultural and Biological Sciences 28 23%
Medicine and Dentistry 19 16%
Engineering 7 6%
Pharmacology, Toxicology and Pharmaceutical Science 6 5%
Other 9 8%
Unknown 17 14%
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 16 March 2015.
All research outputs
#8,262,445
of 25,374,917 outputs
Outputs from Stem Cell Reviews and Reports
#377
of 1,036 outputs
Outputs of similar age
#76,246
of 236,354 outputs
Outputs of similar age from Stem Cell Reviews and Reports
#8
of 12 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 1,036 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has gotten more attention than average, scoring higher than 62% of its peers.
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 236,354 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 66% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.