Effect of gastric acid on the surface roughness and bacterial adhesion of bulk-fill composite resins.
Deise Caren SomacalMariá Cortina BellanMarina Silveira Gregis MonteiroSilvia Dias de OliveiraHélio Radke BittencourtAna Maria SpohrPublished in: Brazilian dental journal (2022)
The purpose of this in vitro study was to evaluate the effect of gastric acid on the surface roughness and biofilm formation of bulk-fill composite resins. Twenty-seven samples of each composite resin were obtained: G1: Filtek Z250 XT (Z250), G2: Filtek Bulk Fill (FTK), G3: Tetric N-Ceram Bulk Fill (TTC), and G4: Aura Bulk Fill (AUR). The samples were quantitatively analyzed for surface roughness (Ra) using a roughness tester (n=15) and for biofilm formation (Cn) by the counting of colony-forming units (CFUs/mL) (n=9) in three different moments: after polishing (Ra0 and Cn0), after gastric acid immersion (Ra1 and Cn1), and after gastric acid and simulated tooth brushing (Ra2 and Cn2). Qualitative analysis through surface topography (n=3) was evaluated by scanning electron microscopy (SEM). Ra values were subjected to two-way repeated measures ANOVA, followed by Tukey's test. Cn values were subjected to Kruskal-Wallis analysis, followed by multiple comparisons analysis (α=0.05). Z250 and FTK showed significant increases in surface roughness at Ra1. There were fewer CFUs/mL on TTC and AUR in relation to those of Z250 and FTK for Cn0, Cn1 and Cn2. The SEM images showed that gastric acid increased the formation of cracks, exposure of fillers and micro cavities for all composite resins. After tooth brushing, the topographical changes were more evident but did not influence biofilm formation. The gastric acid promoted both degradation of the surfaces and bacterial adhesion for all composite resins.
Keyphrases
- biofilm formation
- pseudomonas aeruginosa
- lymph node metastasis
- staphylococcus aureus
- candida albicans
- rheumatoid arthritis
- escherichia coli
- disease activity
- ankylosing spondylitis
- electron microscopy
- high resolution
- squamous cell carcinoma
- systematic review
- cystic fibrosis
- interstitial lung disease
- deep learning
- machine learning
- mass spectrometry
- convolutional neural network