Venkatachalam Geetha, Venkatesan Nandakumar, Vatsal Shloak, Chavan Indira, Bakshi Arnab, Doble Mukesh
Ecogreen Innovations Pvt Ltd, Nirmaan, The Pre-Incubator, Sudha Shankar Innovation Hub, IIT Madras, Chennai 600036, India.
Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India.
ACS Omega. 2025 Aug 25;10(35):39946-39954. doi: 10.1021/acsomega.5c04335. eCollection 2025 Sep 9.
The current study explores the adhesion and biofilm-forming ability of different opportunistic pathogens including , , spp., , and on lotus leaf (LL) and peepal leaf (PL) inspired biomimetic hydrophobic surfaces. Surface topology that mimics the respective leaves was fabricated using polylactic acid by solvent casting. Water contact-angle measurements revealed varying degrees of material surface hydrophobicity with respect to the varying surface roughness. The biofilm formation was significantly influenced by the type of polymer surface ( < 0.005) and the hydrophobicity of the bacterial surface ( < 0.0001). Multilayer perceptron (MLP), a feed-forward neural network, gave the best results with 5-fold cross-validation and an accuracy of 85%. J48-base model predicted that organisms with a surface hydrophobicity of >57% had higher biofilm-forming ability than others. Similarly, polymers with low surface roughness (roughness < 0.46) had reduced biofilm formation. In conclusion, biomimetic hydrophobic surfaces reduce the biofilm formation on implants.
当前研究探讨了包括 、 、 spp.、 和 在内的不同机会致病菌在荷叶（LL）和菩提树叶（PL）启发的仿生疏水表面上的粘附和生物膜形成能力。通过溶剂浇铸法使用聚乳酸制造出模仿相应叶片的表面拓扑结构。水接触角测量揭示了相对于不同的表面粗糙度，材料表面疏水性存在不同程度的差异。生物膜形成受到聚合物表面类型（<0.005）和细菌表面疏水性（<0.0001）的显著影响。多层感知器（MLP），一种前馈神经网络，在5折交叉验证中给出了最佳结果，准确率为85%。J48基模型预测，表面疏水性>57%的生物体比其他生物体具有更高的生物膜形成能力。同样，表面粗糙度低（粗糙度<0.46）的聚合物生物膜形成减少。总之，仿生疏水表面减少了植入物上的生物膜形成。
