4.5 Article

Variations in glial cell line-derived neurotrophic factor release from biodegradable nerve conduits modify the rate of functional motor recovery after rat primary nerve repairs

期刊

EUROPEAN JOURNAL OF NEUROSCIENCE
卷 26, 期 5, 页码 1109-1117

出版社

WILEY
DOI: 10.1111/j.1460-9568.2007.05748.x

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conduit; glial cell line-derived neurotrophic factor; motor function; peripheral nerve

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Accelerating axonal regeneration to shorten the delay of reinnervation and improve functional recovery after a peripheral nerve lesion is a clinical demand and an experimental challenge. We developed a resorbable nerve conduit (NC) for controlled release of glial cell line-derived neurotrophic factor (GDNF) with the aim of assessing motor functional recovery according to the release kinetics of this factor in a short gap model. Different types of resorbable NCs were manufactured from a collagen tube and multiple coating layers of poly(lactide-coglycolide), varying in poly(lactide-coglycolide) type and coating thickness to afford three distinct release kinetics of the neurotrophic factor. GDNF release was quantified in vitro. End-to-end suture and GDNF-free NC served as controls. Thirty-five Wistar rats underwent surgery. Motor recovery was followed from 1 to 12 weeks after surgery by video gait analysis. Morphometrical data were obtained at mid-tube level and distal to the NC. NCs were completely resorbed within 3 months with minimal inflammation. GDNF induced a threefold overgrowth of fibers at mid-tube level. However, the number of fibers was similar in the distal segment of all groups. The speed of recovery was inversely proportional to the number of fibers at the NC level but the level of recovery was similar for all groups at 3 months. The resorbable conduits proved their ability to modulate axonal regrowth through controlled release of GDNF. In relation to the dose delivered, GDNF strikingly multiplied the number of myelinated fibers within the NC but this increase was not positively correlated with the return of motor function in this model.

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