Machine intelligence for nerve conduit design and production.

Autor: Stewart CE; Current Affiliation: Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport Louisiana, USA., Kan CFK; Current Affiliation: Department of General Surgery, Brigham and Women's Hospital, Boston, MA 02115 USA., Stewart BR; Current Affiliation: Department of Surgery, Mayo Clinic College of Medicine, Rochester, MN 55905 USA., Sanicola HW 3rd; Current Affiliation: Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport Louisiana, USA., Jung JP; Department of Biological Engineering, Louisiana State University, Baton Rouge, LA 70803 USA., Sulaiman OAR; Ochsner Neural Injury & Regeneration Laboratory, Ochsner Clinic Foundation, New Orleans, LA 70121 USA.; Department of Neurosurgery, Ochsner Clinic Foundation, New Orleans, 70121 USA., Wang D; Quantitative Imaging Research Team, Data 61, Commonwealth Scientific and Industrial Research Organization, Marsfield, NSW 2122 Australia.
Jazyk: angličtina
Zdroj: Journal of biological engineering [J Biol Eng] 2020 Sep 09; Vol. 14, pp. 25. Date of Electronic Publication: 2020 Sep 09 (Print Publication: 2020).
DOI: 10.1186/s13036-020-00245-2
Abstrakt: Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering.
Competing Interests: Competing interestsThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(© The Author(s) 2020.)
Databáze: MEDLINE
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