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Quality control methods in musculoskeletal tissue engineering: from imaging to biosensors

Authors :
Daniele Zuncheddu
Elena Della Bella
Andrea Schwab
Dalila Petta
Gaia Rocchitta
Silvia Generelli
Felix Kurth
Annapaola Parrilli
Sophie Verrier
Julietta V. Rau
Marco Fosca
Margherita Maioli
Pier Andrea Serra
Mauro Alini
Heinz Redl
Sibylle Grad
Valentina Basoli
Source :
Bone Research, Vol 9, Iss 1, Pp 1-21 (2021)
Publication Year :
2021
Publisher :
Nature Publishing Group, 2021.

Abstract

Abstract Tissue engineering is rapidly progressing toward clinical application. In the musculoskeletal field, there has been an increasing necessity for bone and cartilage replacement. Despite the promising translational potential of tissue engineering approaches, careful attention should be given to the quality of developed constructs to increase the real applicability to patients. After a general introduction to musculoskeletal tissue engineering, this narrative review aims to offer an overview of methods, starting from classical techniques, such as gene expression analysis and histology, to less common methods, such as Raman spectroscopy, microcomputed tomography, and biosensors, that can be employed to assess the quality of constructs in terms of viability, morphology, or matrix deposition. A particular emphasis is given to standards and good practices (GXP), which can be applicable in different sectors. Moreover, a classification of the methods into destructive, noninvasive, or conservative based on the possible further development of a preimplant quality monitoring system is proposed. Biosensors in musculoskeletal tissue engineering have not yet been used but have been proposed as a novel technology that can be exploited with numerous advantages, including minimal invasiveness, making them suitable for the development of preimplant quality control systems.

Details

Language :
English
ISSN :
20956231
Volume :
9
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Bone Research
Publication Type :
Academic Journal
Accession number :
edsdoj.203fc40298ef44b6a792606c8ca48493
Document Type :
article
Full Text :
https://doi.org/10.1038/s41413-021-00167-9