Bio-instructive materials that prevent bacterial biofilm formation and drive an appropriate host immune response have the potential to significantly reduce the burden of medical device-associated infections. As bacterial surface attachment is sensitive to surface topography, we experimentally surveyed 2,176 combinatorially generated shapes using an unbiased high-throughput micro topographical polystyrene polymer chip screen. This identified topographies that reduced colonization in vitro by up to 15-fold compared with a flat surface for both motile and non-motile bacterial pathogens. Equivalent reductions were achieved on polyurethane, a polymer commonly used in medical devices. Using machine learning methods, a set of design rules based on generalisable topo-descriptors was established for predicting bacteria-resistant micro topographies. In a murine foreign body infection model, anti-attachment topographies were shown to be refractory to P. aeruginosa colonization and serendipitously, the fibrotic response to the implant was reduced, offering control of foreign body response using simple topographical patterning of non-eluting implants.

中文翻译：

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细菌附着、生物膜形成和体内宿主反应的微观地形学指导

防止细菌生物膜形成并驱动适当的宿主免疫反应的生物指导材料有可能显着减轻医疗器械相关感染的负担。由于细菌表面附着对表面形貌很敏感，我们使用无偏的高通量微形貌聚苯乙烯聚合物芯片屏幕对 2,176 个组合生成的形状进行了实验性调查。这确定了减少体外定植的拓扑结构与平面相比，活动和非活动的细菌病原体最多可增加 15 倍。聚氨酯（一种医疗器械中常用的聚合物）也实现了等效的减少。使用机器学习方法，建立了一套基于通用拓扑描述符的设计规则，用于预测细菌抗性微拓扑。在鼠异物感染模型中，抗附着拓扑结构被证明对铜绿假单胞菌定植具有抵抗力，并且偶然地，对植入物的纤维化反应降低，使用非洗脱植入物的简单地形图案来控制异物反应。