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Optimization of cell-laden bioinks for 3D bioprinting and efficient infection with influenza A virus

Overview of attention for article published in Scientific Reports, September 2018
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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1 news outlet
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4 X users

Citations

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137 Dimensions

Readers on

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288 Mendeley
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Title
Optimization of cell-laden bioinks for 3D bioprinting and efficient infection with influenza A virus
Published in
Scientific Reports, September 2018
DOI 10.1038/s41598-018-31880-x
Pubmed ID
Authors

Johanna Berg, Thomas Hiller, Maya S. Kissner, Taimoor H. Qazi, Georg N. Duda, Andreas C. Hocke, Stefan Hippenstiel, Laura Elomaa, Marie Weinhart, Christoph Fahrenson, Jens Kurreck

Abstract

Bioprinting is a new technology, which arranges cells with high spatial resolution, but its potential to create models for viral infection studies has not yet been fully realized. The present study describes the optimization of a bioink composition for extrusion printing. The bioinks were biophysically characterized by rheological and electron micrographic measurements. Hydrogels consisting of alginate, gelatin and Matrigel were used to provide a scaffold for a 3D arrangement of human alveolar A549 cells. A blend containing 20% Matrigel provided the optimal conditions for spatial distribution and viability of the printed cells. Infection of the 3D model with a seasonal influenza A strain resulted in widespread distribution of the virus and a clustered infection pattern that is also observed in the natural lung but not in two-dimensional (2D) cell culture, which demonstrates the advantage of 3D printed constructs over conventional culture conditions. The bioink supported viral replication and proinflammatory interferon release of the infected cells. We consider our strategy to be paradigmatic for the generation of humanized 3D tissue models by bioprinting to study infections and develop new antiviral strategies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 288 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 288 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 20%
Student > Master 45 16%
Researcher 39 14%
Student > Bachelor 30 10%
Student > Doctoral Student 12 4%
Other 24 8%
Unknown 81 28%
Readers by discipline Count As %
Engineering 56 19%
Biochemistry, Genetics and Molecular Biology 51 18%
Materials Science 21 7%
Chemistry 13 5%
Medicine and Dentistry 12 4%
Other 43 15%
Unknown 92 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 December 2022.
All research outputs
#2,701,534
of 23,454,152 outputs
Outputs from Scientific Reports
#23,065
of 126,759 outputs
Outputs of similar age
#56,804
of 342,466 outputs
Outputs of similar age from Scientific Reports
#650
of 3,606 outputs
Altmetric has tracked 23,454,152 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 126,759 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one has done well, scoring higher than 81% 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 342,466 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 83% of its contemporaries.
We're also able to compare this research output to 3,606 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.