Amount of Dentifrice and Fluoride Concentration Affect the pH and Inorganic Composition of Dual-Species Biofilms of Streptococcus mutans and Candida albicans .
Caio SampaioAlberto Carlos Botazzo DelbemThayse Yumi HosidaAna Vitória Pereira FernandesBruna do AmaralLeonardo Antônio de MoraisDouglas Roberto MonteiroJuliano Pelim PessanPublished in: Pharmaceutics (2024)
This work assessed the influence of the amount of dentifrice and fluoride (F) concentration in the product on the pH and inorganic components of Streptococcus mutans and Candida albicans dual-species biofilms. The biofilms were treated with suspensions of fluoride dentifrices containing 550 or 1100 ppm of F (550 F or 1100 F, respectively) administered at comparable intensities: (i-1) 550 F/0.08 g or 1100 F/0.04 g; (i-2) 550 F/0.16 g or 1100 F/0.08 g; and (i-3) 550 F/0.32 g or 1100 F/0.16 g. A placebo dentifrice (without NaF, 0.32 g) was used as a negative control. After the last treatment, the biofilm pH was measured and the F, calcium (Ca), and phosphorus (P) concentrations were determined. Data were subjected to an ANOVA/Kruskal-Wallis test, and a Student-Newman-Keuls test. The highest biofilm pH and F concentrations (biomass and fluid) were observed for 1100 F at i-3. Overall, 1100 F resulted in F levels similar to 550 F for i-1 and i-2. In addition, 550 F applied at i-2 and i-3 led to higher F in the biomass/fluid compared to 1100 F applied at i-1 and i-2, respectively. In biomass, the lowest Ca concentrations were observed for 1100 F at i-3. The conclusion drawn is that the treatment intensity holds greater significance as a parameter compared to the concentration of F or the amount of dentifrice when considered individually.
Keyphrases
- candida albicans
- biofilm formation
- drinking water
- wastewater treatment
- anaerobic digestion
- randomized controlled trial
- clinical trial
- pseudomonas aeruginosa
- staphylococcus aureus
- escherichia coli
- protein kinase
- risk assessment
- open label
- study protocol
- artificial intelligence
- machine learning
- sewage sludge
- pet ct
- deep learning