Antimicrobial and Antibiofilm Properties of Graphene Oxide on Enterococcus faecalis.
Cecilia MartiniFrancesca LongoRaffaella CastagnolaLuca MarigoNicola Maria GrandeMassimo CordaroMargherita CacaciMassimiliano PapiValentina PalmieriFrancesca BugliMaurizio SanguinettiPublished in: Antibiotics (Basel, Switzerland) (2020)
The aim of this study was to evaluate the antibacterial properties of graphene oxide (GO) against Enterococcus faecalis in vitro conditions and when used to coat dentin surface to prevent E. faecalis adhesion. The ATCC strain of E. faecalis 29212 has been used to perform a viability test. The pellet was suspended in ultrapure water, NaCl, PBS buffer, CaCl2 and MgCl2, Luria-Bertani broth solutions. The viability was evaluated by the colony forming unit counting method. Atomic force microscopy images and the measure of surface zeta potential variation were analyzed. Dentin discs were covered with a film of GO (n = 15) or were not treated (n = 15). Bacterial suspension was added to each sample of dentine discs and microbial counts were calculated. Statistically significant differences between two groups were assessed by a two-tailed unpaired t-test. Bacteria cell morphology was investigated with scanning electron microscopy. The highest growth inhibition was obtained in ddH2O and CaCl2 solution while, in PBS and NaCl, GO had poor antibacterial efficacy with a growth enhancing effect in the latter. GO on dentin discs demonstrated high antibacterial activity. GO film has demonstrated acceptable adhesion properties to root dentin and a role in the inhibition of bacterial film proliferation and biofilm formation.
Keyphrases
- biofilm formation
- atomic force microscopy
- electron microscopy
- staphylococcus aureus
- pseudomonas aeruginosa
- candida albicans
- escherichia coli
- silver nanoparticles
- room temperature
- high speed
- reduced graphene oxide
- deep learning
- single molecule
- microbial community
- signaling pathway
- high resolution
- cystic fibrosis
- gold nanoparticles
- cell therapy
- optical coherence tomography
- risk assessment
- mesenchymal stem cells
- machine learning
- stem cells
- human health