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In Vivo Islet Protection by a Nuclear Import Inhibitor in a Mouse Model of Type 1 Diabetes

Overview of attention for article published in PLOS ONE, October 2010
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Title
In Vivo Islet Protection by a Nuclear Import Inhibitor in a Mouse Model of Type 1 Diabetes
Published in
PLOS ONE, October 2010
DOI 10.1371/journal.pone.0013235
Pubmed ID
Authors

Daniel J. Moore, Jozef Zienkiewicz, Peggy L. Kendall, Danya Liu, Xueyan Liu, Ruth Ann Veach, Robert D. Collins, Jacek Hawiger

Abstract

Insulin-dependent Type 1 diabetes (T1D) is a devastating autoimmune disease that destroys beta cells within the pancreatic islets and afflicts over 10 million people worldwide. These patients face life-long risks for blindness, cardiovascular and renal diseases, and complications of insulin treatment. New therapies that protect islets from autoimmune destruction and allow continuing insulin production are needed. Increasing evidence regarding the pathomechanism of T1D indicates that islets are destroyed by the relentless attack by autoreactive immune cells evolving from an aberrant action of the innate, in addition to adaptive, immune system that produces islet-toxic cytokines, chemokines, and other effectors of islet inflammation. We tested the hypothesis that targeting nuclear import of stress-responsive transcription factors evoked by agonist-stimulated innate and adaptive immunity receptors would protect islets from autoimmune destruction.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Switzerland 1 4%
Unknown 24 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 31%
Student > Master 5 19%
Student > Ph. D. Student 4 15%
Student > Doctoral Student 2 8%
Professor > Associate Professor 2 8%
Other 5 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 50%
Medicine and Dentistry 5 19%
Unspecified 1 4%
Computer Science 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 2 8%
Unknown 3 12%