Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning-Reference-Cited by-同舟云学术

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Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning

Published:2020-02 Issue: Volume:107 Page:110362
ISSN:0928-4931
Container-title:Materials Science and Engineering: C
language:en
Short-container-title:Materials Science and Engineering: C

Author:

Entekhabi Elahe,Haghbin Nazarpak Masoumeh,Sedighi Mehdi,Kazemzadeh Arghavan

Funder

Amirkabir University of Technology, Iran

Publisher

Elsevier BV

Subject

Biomaterials,Bioengineering,Mechanics of Materials

Reference45 articles.

1. Neural tissue engineering options for peripheral nerve regeneration;Gu;Biomaterials,2014

2. Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity;Entekhabi;Mater. Sci. Eng. C,2016

3. Chitosan/gelatin scaffolds support bone regeneration;Georgopoulou;J. Mater. Sci. Mater. Med.,2018

4. In vitro characterization of chitosan–gelatin scaffolds for tissue engineering;Huang;Biomaterials,2005

5. An in vivo evaluation of a biodegradable genipin-cross-linked gelatin peripheral nerve guide conduit material;Chen;Biomaterials,2005

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