Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5814601 | Neuropharmacology | 2014 | 7 Pages |
Abstract
Laminectomy is a widely accepted treatment for lumbar disorders, and epidural fibrosis (EF) is a common complication. EF is thought to cause post-operative pain recurrence after laminectomy or discectomy. All-trans retinoic acid (ATRA) has shown anti-fibrotic, anti-inflammatory, and anti-proliferative functions. The object of this study was to investigate the effects of ATRA on the prevention of EF in post-laminectomy rats. In vitro, the anti-fibrotic effect of ATRA was demonstrated with cultured fibroblasts count, which comprised of those that were cultured with/without ATRA. In vivo, rats underwent laminectomy at the L1-L2 levels. We first demonstrated the beneficial effects using 0.05% ATRA compared to vehicle (control group). We found that a higher concentration of ATRA (0.1%) achieved dose-dependent results. Hydroxyproline content, Rydell score, vimentin-positive cell density, fibroblast density, inflammatory cell density and inflammatory factor expression levels all suggested better outcomes in the 0.1% ATRA rats compared to the other three groups. Presumably, these effects involved ATRA's ability to suppress transforming growth factor (TGF-β1) and interleukin (IL)-6 which was confirmed with reverse-transcriptase polymerase chain reaction (RT-PCR). Finally we demonstrated that ATRA down-regulated nuclear factor (NF)-κB by immunohistochemistry and western blotting for p65 and inhibition of κB (IκBα), respectively. Our findings indicate that topical application of ATRA can inhibit fibroblast proliferation, decrease TGF-β1 and IL-6 expression level, and prevent epidural scar adhesion in rats. The highest concentration employed in this study (0.1%) was the most effective. ATRA suppressed EF through down-regulating NF-κB signaling, whose specific mechanism is suppression of IκB phosphorylation and proteolytic degradation.
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Authors
Chao Zhang, Xiaohong Kong, Guangzhi Ning, Zhipin Liang, Tongjun Qu, Feiran Chen, Daigui Cao, Tianyi Wang, Hari S. Sharma, Shiqing Feng,