3D printing nano conductive multi-walled carbon nanotube scaffolds for nerve regeneration – ScienceOpen
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      3D printing nano conductive multi-walled carbon nanotube scaffolds for nerve regeneration

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          Application of conductive polymers, scaffolds and electrical stimulation for nerve tissue engineering.

          Among the numerous attempts to integrate tissue engineering concepts into strategies to repair nearly all parts of the body, neuronal repair stands out. This is partially due to the complexity of the nervous anatomical system, its functioning and the inefficiency of conventional repair approaches, which are based on single components of either biomaterials or cells alone. Electrical stimulation has been shown to enhance the nerve regeneration process and this consequently makes the use of electrically conductive polymers very attractive for the construction of scaffolds for nerve tissue engineering. In this review, by taking into consideration the electrical properties of nerve cells and the effect of electrical stimulation on nerve cells, we discuss the most commonly utilized conductive polymers, polypyrrole (PPy) and polyaniline (PANI), along with their design and modifications, thus making them suitable scaffolds for nerve tissue engineering. Other electrospun, composite, conductive scaffolds, such as PANI/gelatin and PPy/poly(ε-caprolactone), with or without electrical stimulation, are also discussed. Different procedures of electrical stimulation which have been used in tissue engineering, with examples on their specific applications in tissue engineering, are also discussed. Copyright © 2011 John Wiley & Sons, Ltd.
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            Carbon nanotube reinforced hybrid microgels as scaffold materials for cell encapsulation.

            Hydrogels that mimic biological extracellular matrix (ECM) can provide cells with mechanical support and signaling cues to regulate their behavior. However, despite the ability of hydrogels to generate artificial ECM that can modulate cellular behavior, they often lack the mechanical strength needed for many tissue constructs. Here, we present reinforced CNT-gelatin methacrylate (GelMA) hybrid as a biocompatible, cell-responsive hydrogel platform for creating cell-laden three-dimensional (3D) constructs. The addition of carbon nanotubes (CNTs) successfully reinforced GelMA hydrogels without decreasing their porosity or inhibiting cell growth. The CNT-GelMA hybrids were also photopatternable allowing for easy fabrication of microscale structures without harsh processes. NIH-3T3 cells and human mesenchymal stem cells (hMSCs) readily spread and proliferated after encapsulation in CNT-GelMA hybrid microgels. By controlling the amount of CNTs incorporated into the GelMA hydrogel system, we demonstrated that the mechanical properties of the hybrid material can be tuned making it suitable for various tissue engineering applications. Furthermore, due to the high pattern fidelity and resolution of CNT incorporated GelMA, it can be used for in vitro cell studies or fabricating complex 3D biomimetic tissue-like structures. © 2011 American Chemical Society
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              Molecular functionalization of carbon nanotubes and use as substrates for neuronal growth.

              Carbon nanotubes are strong, flexible, conduct electrical current, and can be functionalized with different molecules, properties that may be useful in basic and applied neuroscience research. We report the first application of carbon nanotube technology to neuroscience research. Methods were developed for growing embryonic rat-brain neurons on multiwalled carbon nanotubes. On unmodified nanotubes, neurons extend only one or two neurites, which exhibit very few branches. In contrast, neurons grown on nanotubes coated with the bioactive molecule 4-hydroxynonenal elaborate multiple neurites, which exhibit extensive branching. These findings establish the feasability of using nanotubes as substrates for nerve cell growth and as probes of neuronal function at the nanometer scale.
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                Author and article information

                Journal
                Journal of Neural Engineering
                J. Neural Eng.
                IOP Publishing
                1741-2560
                1741-2552
                February 01 2018
                February 01 2018
                January 12 2018
                : 15
                : 1
                : 016018
                Article
                10.1088/1741-2552/aa95a5
                29064377
                5ad03161-404d-4ed4-b1c4-decc5c14572b
                © 2018

                http://iopscience.iop.org/info/page/text-and-data-mining

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