Abstract
Introduction:
Silver (Ag) nanoparticles (NPs) are well documented for their broad-spectrum bactericidal effects. This study aimed to test the effect of bioactive Ag-hydrosol NPs on drug-resistant E. faecium 1449 strain and explore the use of artificial intelligence (AI) for automated detection of the bacteria.
Methods:
The formation of E. faecium 1449 biofilms in the absence and presence of Ag-hydrosol NPs at different concentrations ranging from 12.4 mg/L to 123 mg/L was evaluated using a 3-dimentional culture system. The biofilm reduction was evaluated using the confocal microscopy in addition to the Transmission Electronic Microscopy (TEM) visualization and spectrofluorimetric quantification using a Biotek Synergy Neo2 microplate reader. The cytotoxicity of the NPs was evaluated in human nasal epithelial cells using the MTT assay. The AI technique based on Fast Regional Convolutional Neural Network architecture was used for the automated detection of the bacteria.
Results:
Treatment with Ag-hydrosol NPs at concentrations ranging from 12.4 mg/L to 123 mg/L resulted in 78.09% to 95.20% of biofilm reduction. No statistically significant difference in biofilm reduction was found among different batches of Ag-hydrosol NPs. Quantitative concentration-response relationship analysis indicated that Ag-hydrosol NPs exhibited a relative high anti-biofilm activity and low cytotoxicity with an average EC50 and TC50 values of 0.0333 and 6.55 mg/L, respectively, yielding an average therapeutic index value of 197. The AI-assisted TEM image analysis allowed automated detection of E. faecium 1449 with 97% ~ 99% accuracy.
Discussion:
Conclusively, the bioactive Ag-hydrosol NP is a promising nanotherapeutic agent against drug-resistant pathogens. The AI-assisted TEM image analysis was developed with the potential to assess its treatment effect.
Keywords:
multi-drug resistance; E. faecium; artificial intelligence; bactericidal treatment; bioactive silver hydrosol nanoparticles; computer vision; cytotoxicity.