Artificial neural network optimized green synthesis of cysteine-conjugated silver nanoparticles for antibacterial activity against staphylococcus nepalensis to combat cystitis

Abstract

The emergence of multidrug-resistant pathogens has increased the urgency for alternative treatment options for infections like cystitis. This study focused on the green synthesis of silver nanoparticles and cysteine-conjugated silver nanoparticles utilizing Melaleuca lanceolata leaf extract, optimized via artificial neural networks for controlled nanoparticle size. The ANN model provided precise prediction and control of nanoparticle size, reducing experimental variability. The characterization was performed using UV–Vis spectroscopy, which showed peaks at 445 nm for AgNPs and 405 nm for Cys-AgNPs. Additionally, FTIR, SEM, and EDX analysis confirmed the successful synthesis of AgNPs and their conjugation with cysteine. Biological analyses revealed that Cys-AgNPs had increased antioxidant and anti-inflammatory effects over AgNPs and controls. Notably, they demonstrated better antibacterial activity against Staphylococcus nepalensis, a new uropathogen causing cystitis, with a 17 mm inhibitory zone and a lowest inhibitory concentration of 25 µg/ml. Direct cytotoxicity assays and in vivo studies in animal models were not carried out, but the observed reduction in hemolysis in vitro demonstrates that it may be biocompatible. These results demonstrate the novelty of using ANN-based optimization and green nanotechnology to produce stable, functionalized nanoparticles of therapeutic interest. It is recommended that cytotoxicity analyses, in vivo confirmation, and wider MDR pathogen testing should be performed in the future to ensure clinical relevance.