No abstract

Medical applications and biotechnological advances, including magnetic resonance imaging, cell separation and detection, tissue repair, magnetic hyperthermia and drug delivery, have strongly benefited from employing iron oxide nanoparticles (IONPs) due to their remarkable properties, such as superparamagnetism, size and possibility of receiving a biocompatible coating. Ongoing research efforts focus on reducing drug concentration, toxicity, and other side effects, while increasing efficacy of IONPs-based treatments. This review highlights the methods of synthesis and presents the most recent reports in the literature regarding advances in drug delivery using IONPs-based systems, as well as their antimicrobial activity against different microorganisms. Furthermore, the toxicity of IONPs alone and constituting nanosystems is also addressed.

Incorporation of silver nanoparticles in the acrylic resin was evidenced. Moreover, silver was not detected by the detection limit of the atomic absorption spectrophotometer used in this study, even after 120 days of storage in deionized water. Silver nanoparticles are incorporated in the PMMA denture resin to attain an effective antimicrobial material to help control common infections involving oral mucosal tissues in complete denture wearers.

The aim of this study was to evaluate the effect of silver nanoparticles (SN) against Candida albicans and Candida glabrata adhered cells and biofilms. SN (average diameter 5 nm) were synthesized by silver nitrate reduction with sodium citrate and stabilized with ammonia. Minimal inhibitory concentration (MIC) tests were performed for C. albicans (n = 2) and C. glabrata (n = 2) grown in suspension following the Clinical Laboratory Standards Institute microbroth dilution method. SN were applied to adhered cells (2 h) or biofilms (48 h) and after 24 h of contact their effect was assessed by enumeration of colony forming units (CFUs) and quantification of total biomass (by crystal violet staining). The MIC results showed that SN were fungicidal against all strains tested at very low concentrations (0.4-3.3 μg ml(-1)). Furthermore, SN were more effective in reducing biofilm biomass when applied to adhered cells (2 h) than to pre-formed biofilms (48 h), with the exception of C. glabrata ATCC, which in both cases showed a reduction ∼90%. Regarding cell viability, SN were highly effective on adhered C. glabrata and respective biofilms. On C. albicans the effect was not so evident but there was also a reduction in the number of viable biofilm cells. In summary, SN may have the potential to be an effective alternative to conventional antifungal agents for future therapies in Candida-associated denture stomatitis.

Because the biomechanical behavior of dental implants is different from that of natural tooth, clinical problems may occur. The mechanism of stress distribution and load transfer to the implant/bone interface is a critical issue affecting the success rate of implants. Therefore, the aim of this study was to conduct a brief literature review of the available stress analysis methods to study implant-supported prosthesis loading and to discuss their contributions in the biomechanical evaluation of oral rehabilitation with implants. Several studies have used experimental, analytical, and computational models by means of finite element models (FEM), photoelasticity, strain gauges and associations of these methods to evaluate the biomechanical behavior of dental implants. The FEM has been used to evaluate new components, configurations, materials, and shapes of implants. The greatest advantage of the photoelastic method is the ability to visualize the stresses in complex structures, such as oral structures, and to observe the stress patterns in the whole model, allowing the researcher to localize and quantify the stress magnitude. Strain gauges can be used to assess in vivo and in vitro stress in prostheses, implants, and teeth. Some authors use the strain gauge technique with photoelasticity or FEM techniques. These methodologies can be widely applied in dentistry, mainly in the research field. Therefore, they can guide further research and clinical studies by predicting some disadvantages and streamlining clinical time.