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Interpenetrated Si-HPMC/alginate hydrogels as a potential scaffold for human tissue regeneration

  • Alexia Viguier
  • Cecile Boyer
  • Christophe Chassenieux
  • Lazhar Benyahia
  • Jérôme Guicheux
  • Pierre Weiss
  • Gildas Rethore
  • Taco NicolaiEmail author
Tissue Engineering Constructs and Cell Substrates Original Research
Part of the following topical collections:
  1. Tissue Engineering Constructs and Cell Substrates

Abstract

Interpenetrated gels of biocompatible polysaccharides alginate and silanized hydroxypropyl methyl cellulose (Si-HPMC) have been studied in order to assess their potential as scaffolds for the regeneration of human tissues. Si-HPMC networks were formed by reduction of the pH to neutral and alginate networks were formed by progressive in situ release of Ca2+. Linear and non-linear mechanical properties of the mixed gels at different polymer and calcium concentrations were compared with those of the corresponding single gels. The alginate/Si-HPMC gels were found to be stiffer than pure Si-HPMC gels, but weaker and more deformable than pure alginate gels. No significant difference was found for the maximum stress at rupture measured during compression for all these gels. The degrees of swelling or contraction in excess water at pH 7 as well as the release of Ca2+ was measured as a function of time. Pure alginate gels contracted by as much as 50 % and showed syneresis, which was much reduced or even eliminated for mixed gels. The important release of Ca2+ upon ageing for pure alginate gels was much reduced for the mixed gels. Furthermore, results of cytocompatibility assays indicated that there was no cytotoxicity of Si-HPMC/alginate hydrogels in 2D and 3D culture of human SW1353 cells. The results show that using interpenetrated Si-HPMC/alginate gels has clear advantages over the use of single gels for application in tissue regeneration.

Graphical Abstract

Keywords

Alginate SW1353 Cell Interpenetrate Polymer Network Alginate Solution Linear Response Regime 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This work has been funded by the Agence Nationale de la Recherche in the framework of ANR-11-BSV5-0022.

Supplementary material

10856_2016_5709_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1076 kb)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Alexia Viguier
    • 1
  • Cecile Boyer
    • 2
  • Christophe Chassenieux
    • 1
  • Lazhar Benyahia
    • 1
  • Jérôme Guicheux
    • 2
    • 3
  • Pierre Weiss
    • 2
    • 3
  • Gildas Rethore
    • 2
    • 3
  • Taco Nicolai
    • 1
    Email author
  1. 1.IMMMLUNAM Université du MaineLe Mans Cedex 9France
  2. 2.INSERM, LIOADUniversité de NantesNantes Cedex 1France
  3. 3.CHU NantesPHU4 OTONNNantesFrance

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