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Using computer simulation models to investigate the most promising microRNAs to improve muscle regeneration during ageing

Overview of attention for article published in Scientific Reports, September 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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Title
Using computer simulation models to investigate the most promising microRNAs to improve muscle regeneration during ageing
Published in
Scientific Reports, September 2017
DOI 10.1038/s41598-017-12538-6
Pubmed ID
Authors

Carole J. Proctor, Katarzyna Goljanek-Whysall

Abstract

MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 24 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 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 %
Student > Bachelor 5 17%
Student > Master 4 13%
Researcher 3 10%
Student > Ph. D. Student 3 10%
Student > Doctoral Student 2 7%
Other 3 10%
Unknown 10 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 30%
Medicine and Dentistry 3 10%
Agricultural and Biological Sciences 2 7%
Neuroscience 2 7%
Sports and Recreations 1 3%
Other 3 10%
Unknown 10 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 12 November 2019.
All research outputs
#2,374,133
of 23,003,906 outputs
Outputs from Scientific Reports
#20,762
of 124,216 outputs
Outputs of similar age
#47,955
of 320,414 outputs
Outputs of similar age from Scientific Reports
#917
of 5,496 outputs
Altmetric has tracked 23,003,906 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 124,216 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one has done well, scoring higher than 83% 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 320,414 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 5,496 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.