Keywords: Electrical power systems, Support vector machines, Random Forest, Machine learning, Wavelet transform, Transmission lines fault, Electrical power quality, Short circuit, Classification of faults, Localization of faults, Decision trees, Ensemble learning, K-nearest neighbors.

Breast cancer is the most common cancer in women globally, often necessitating mastectomy and subsequent breast reconstruction. Silicone mammary implants (SMIs) play a pivotal role in breast reconstruction, yet their interaction with the host immune system and microbiome remains poorly understood. This study investigates the impact of SMI surface topography on host antimicrobial responses, wound proteome dynamics, and microbial colonization. Biological samples were col-lected from ten human patients undergoing breast reconstruction with SMIs. Mass spectrometry profiles were analyzed for acute and chronic wound proteomes, revealing a nuanced interplay between topography and antimicrobial respondseproteins. 16S rRNA sequencing assessed mi-crobiome dynamics, unveiling topography-specific variations in microbial composition. Surface topography alterations influenced wound proteome composition. Microbiome analysis revealed heightened diversity around rougher SMIs, emphasizing topography-dependent microbial inva-sion. In vitro experiments confirmed staphylococcal adhesion, growth, and biofilm formation on SMI surfaces, with increased texture correlating positively with bacterial colonization. This comprehensive investigation highlights the intricate interplay between SMI topography, wound proteome dynamics, and microbial transmission. The findings contribute to understanding host-microbe interactions on SMI surfaces, essential for optimizing clinical applications and minimizing complications in breast reconstruction.