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The short and long of noncoding sequences in the control of vascular cell phenotypes

Overview of attention for article published in Cellular and Molecular Life Sciences, May 2015
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Title
The short and long of noncoding sequences in the control of vascular cell phenotypes
Published in
Cellular and Molecular Life Sciences, May 2015
DOI 10.1007/s00018-015-1936-9
Pubmed ID
Authors

Joseph M. Miano, Xiaochun Long

Abstract

The two principal cell types of importance for normal vessel wall physiology are smooth muscle cells and endothelial cells. Much progress has been made over the past 20 years in the discovery and function of transcription factors that coordinate proper differentiation of these cells and the maintenance of vascular homeostasis. More recently, the converging fields of bioinformatics, genomics, and next generation sequencing have accelerated discoveries in a number of classes of noncoding sequences, including transcription factor binding sites (TFBS), microRNA genes, and long noncoding RNA genes, each of which mediates vascular cell differentiation through a variety of mechanisms. Alterations in the nucleotide sequence of key TFBS or deviations in transcription of noncoding RNA genes likely have adverse effects on normal vascular cell phenotype and function. Here, the subject of noncoding sequences that influence smooth muscle cell or endothelial cell phenotype will be summarized as will future directions to further advance our understanding of the increasingly complex molecular circuitry governing normal vascular cell differentiation and how such information might be harnessed to combat vascular diseases.

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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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 37%
Student > Ph. D. Student 5 17%
Student > Postgraduate 4 13%
Student > Bachelor 2 7%
Student > Master 2 7%
Other 2 7%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 33%
Medicine and Dentistry 8 27%
Biochemistry, Genetics and Molecular Biology 4 13%
Neuroscience 2 7%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 4 13%
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 30 May 2015.
All research outputs
#21,141,111
of 23,794,258 outputs
Outputs from Cellular and Molecular Life Sciences
#3,769
of 4,151 outputs
Outputs of similar age
#224,258
of 267,232 outputs
Outputs of similar age from Cellular and Molecular Life Sciences
#51
of 58 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,151 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.