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Influence of Additive Manufactured Scaffold Architecture on the Distribution of Surface Strains and Fluid Flow Shear Stresses and Expected Osteochondral Cell Differentiation

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, February 2017
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Title
Influence of Additive Manufactured Scaffold Architecture on the Distribution of Surface Strains and Fluid Flow Shear Stresses and Expected Osteochondral Cell Differentiation
Published in
Frontiers in Bioengineering and Biotechnology, February 2017
DOI 10.3389/fbioe.2017.00006
Pubmed ID
Authors

Wim J. Hendrikson, Anthony J. Deegan, Ying Yang, Clemens A. van Blitterswijk, Nico Verdonschot, Lorenzo Moroni, Jeroen Rouwkema

Abstract

Scaffolds for regenerative medicine applications should instruct cells with the appropriate signals, including biophysical stimuli such as stress and strain, to form the desired tissue. Apart from that, scaffolds, especially for load-bearing applications, should be capable of providing mechanical stability. Since both scaffold strength and stress-strain distributions throughout the scaffold depend on the scaffold's internal architecture, it is important to understand how changes in architecture influence these parameters. In this study, four scaffold designs with different architectures were produced using additive manufacturing. The designs varied in fiber orientation, while fiber diameter, spacing, and layer height remained constant. Based on micro-CT (μCT) scans, finite element models (FEMs) were derived for finite element analysis (FEA) and computational fluid dynamics (CFD). FEA of scaffold compression was validated using μCT scan data of compressed scaffolds. Results of the FEA and CFD showed a significant impact of scaffold architecture on fluid shear stress and mechanical strain distribution. The average fluid shear stress ranged from 3.6 mPa for a 0/90 architecture to 6.8 mPa for a 0/90 offset architecture, and the surface shear strain from 0.0096 for a 0/90 offset architecture to 0.0214 for a 0/90 architecture. This subsequently resulted in variations of the predicted cell differentiation stimulus values on the scaffold surface. Fluid shear stress was mainly influenced by pore shape and size, while mechanical strain distribution depended mainly on the presence or absence of supportive columns in the scaffold architecture. Together, these results corroborate that scaffold architecture can be exploited to design scaffolds with regions that guide specific tissue development under compression and perfusion. In conjunction with optimization of stimulation regimes during bioreactor cultures, scaffold architecture optimization can be used to improve scaffold design for tissue engineering purposes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 23%
Student > Master 11 14%
Researcher 10 13%
Student > Bachelor 9 12%
Student > Doctoral Student 7 9%
Other 11 14%
Unknown 12 15%
Readers by discipline Count As %
Engineering 39 50%
Materials Science 6 8%
Agricultural and Biological Sciences 5 6%
Chemical Engineering 3 4%
Medicine and Dentistry 3 4%
Other 6 8%
Unknown 16 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 October 2019.
All research outputs
#13,538,247
of 22,953,506 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#1,680
of 6,685 outputs
Outputs of similar age
#212,194
of 422,694 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#6
of 21 outputs
Altmetric has tracked 22,953,506 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,685 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 73% 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 422,694 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 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.