Due to the progression of antimicrobial resistance, Photodynamic (PDI) and Sonodynamic (SDI) Inactivation therapies arose as promising approaches for microbial control. Recently, the combination of both therapies, called Sonophotodynamic Inactivation (SPDI), have shown greater effects than the single treatments. This study evaluated the effectiveness of PDI, SDI and SPDI mediated by curcumin (Cur) against Staphylococcus aureus biofilm and the structural impact of these treatments on the biofilm community. For, this S. aureus biofilms received PDI, SDI and SPDI, mediated by Cur (80μM), LED light (450nm), and 1MHz ultrasound (20% of duty cycle, 1.5W/cm² of intensity). The effectiveness of the treatments was measured by cell viability assay (quantification of colonies - CFU/mL), metabolic activity (XTT assay) and total biomass of the biofilms was quantified by crystal violet. Additionally, the biofilm architecture after treatments was evaluated under confocal fluorescence microscopy. SPDI was more effective than PDI and SDI. The SDI, PDI and SPDI groups demonstrated reductions of 1±1, 1±1, and 3±1 log, respectively, compared to the control group. The PDI group exhibited a metabolic activity 89±1% lower than the control, while SDI and SPDI showed 82±2% and 90±1%, respectively. All treatments reduced the total biomass of the biofilms. The PDI samples exhibited a 43±9% reduction in total biomass, the SDI group showed a 25±11% reduction, and the SPDI group demonstrated a 49±11% reduction in comparison with the control group. Finally, all treatments impacted the biofilm components and structure, reducing the cells and matrix. In conclusion, SPDI was more effective in the inactivation and had greater impact on the S. aureus biofilm.
Antibiotic is one of the most important medical inventions in the 20th century 1. However, bacterial resistance to antibiotics is becoming a global health-care problem 2. One of the important measures to tackle this problem is fast detection bacterial antibiotic susceptibility 1. In this research topic and inspired by the work report of Soares et. al. 3,4 we were motivated to developed this study to identification of resistance to antibiotic in Staphylococcus aureus. By mean of machine learning implementation in data analyses of Fourier-Transform Infrared Spectroscopy (FTIR) spectra, we found promisor results in samples with and without antibiotic resistance develop.
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