Biologic scaffolds (BS) are the most widely studied therapeutics for the treatment of volumetric muscle loss (VML) owing to their purported effects on cell proliferation, chemotaxis, migration, and differentiation. Despite these claims, variability in reports on the nature of the immune response to their implantation suggests that BS-associated inflammation may be limiting their regenerative efficacy. To address this shortcoming, this study sought to evaluate licofelone (ML3000), a dual 5-LOX/COX inhibitor, as an anti-inflammatory adjunct therapy to a BS in the treatment of VML. Utilizing a well-established rat VML model, a micronized BS was used to treat the VML injury, with or without administration of licofelone. Functional, molecular, and histological outcomes were assessed at both 7- and 28-day post-injury time points. While the BS + licofelone group exhibited decreased transcription of pro-inflammatory markers (Tnf, Ccl5, Nos2) relative to the BS only control group, no differences in expression profile of a panel of inflammatory-related soluble factors were observed between groups. A modest reduction in type I collagen was observed in the licofelone-treated group, but no meaningful differences in histologic presentation of repaired tissue were observed between groups. Furthermore, no differences in end organ functional capacity were observed between groups. Moving forward, efforts related to modulating the wound healing environment of VML should focus on polypharmaceutical strategies that target multiple aspects of the early pathophysiology of VML so as to provide an environment that is sufficiently permissive for local regenerative therapies to promote restoration of myofiber number.
Keywords:Inflammation; Skeletal muscle; Tissue regeneration; Trauma; Wounds and injuries.
Evaluation of licofelone as an adjunct anti-inflammatory therapy to biologic scaffolds in the treatment of volumetric muscle loss
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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