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In silico study of bone tissue regeneration in an idealised porous hydrogel scaffold using a mechano-regulation algorithm

Overview of attention for article published in Biomechanics and Modeling in Mechanobiology, August 2017
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
In silico study of bone tissue regeneration in an idealised porous hydrogel scaffold using a mechano-regulation algorithm
Published in
Biomechanics and Modeling in Mechanobiology, August 2017
DOI 10.1007/s10237-017-0941-3
Pubmed ID
Authors

Feihu Zhao, Myles J. Mc Garrigle, Ted J. Vaughan, Laoise M. McNamara

Abstract

Mechanical stimulation, in the form of fluid perfusion or mechanical strain, enhances osteogenic differentiation and overall bone tissue formation by mesenchymal stems cells cultured in biomaterial scaffolds for tissue engineering applications. In silico techniques can be used to predict the mechanical environment within biomaterial scaffolds, and also the relationship between bone tissue regeneration and mechanical stimulation, and thereby inform conditions for bone tissue engineering experiments. In this study, we investigated bone tissue regeneration in an idealised hydrogel scaffold using a mechano-regulation model capable of predicting tissue differentiation, and specifically compared five loading cases, based on known experimental bioreactor regimes. These models predicted that low levels of mechanical loading, i.e. compression (0.5% strain), pore pressure of 10 kPa and a combination of compression (0.5%) and pore pressure (10 kPa), could induce more osteogenic differentiation and lead to the formation of a higher bone tissue fraction. In contrast greater volumes of cartilage and fibrous tissue fractions were predicted under higher levels of mechanical loading (i.e. compression strain of 5.0% and pore pressure of 100 kPa). The findings in this study may provide important information regarding the appropriate mechanical stimulation for in vitro bone tissue engineering experiments.

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The data shown below were collected from the profiles of 3 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 18%
Researcher 4 11%
Student > Doctoral Student 4 11%
Student > Bachelor 4 11%
Student > Master 4 11%
Other 8 21%
Unknown 7 18%
Readers by discipline Count As %
Engineering 15 39%
Materials Science 4 11%
Biochemistry, Genetics and Molecular Biology 2 5%
Medicine and Dentistry 1 3%
Chemical Engineering 1 3%
Other 2 5%
Unknown 13 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 August 2017.
All research outputs
#13,242,747
of 23,849,058 outputs
Outputs from Biomechanics and Modeling in Mechanobiology
#156
of 486 outputs
Outputs of similar age
#147,002
of 319,120 outputs
Outputs of similar age from Biomechanics and Modeling in Mechanobiology
#4
of 12 outputs
Altmetric has tracked 23,849,058 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 486 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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 319,120 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 53% 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 has gotten more attention than average, scoring higher than 66% of its contemporaries.