Effects of non-thermal atmospheric plasma treatment on dentin wetting and surface free energy for application of universal adhesives

Objectives

The study aims to evaluate the effects of non-thermal atmospheric plasma (NTAP) treatments on dentin wetting and surface free energy (SFE) and compare the effects of NTAP treatment, etch-and-rinse, and self-etch protocols for application of universal adhesives.

Materials and methods

Mid-coronal dentin of intact third molars was used to measure contact angles of distilled water, ethylene-glycol, and diiodomethane and calculate SFE following different NTAP preset treatments (feeding gas consisting of pure He, He + 1% O₂, He + 1.5% O₂), power input (1 or 3 W), and tip-to-surface distance (2, 4, or 8 mm). Contact angles of reference liquids and SFE of dentin following He + 1.5% O₂ at 3-W and 4-mm treatment was compared to phosphoric acid etching. Contact angles of Single Bond Universal (SBU; 3M ESPE) and Clearfil Universal Bond (CUB; Kuraray Noritake) were measured following NTAP, etch-and-rinse, and self-etch protocols.

Results

NTAP significantly reduced contact angles of reference liquids and increased dentin SFE compared to untreated dentin (p < 0.05). O₂ intensified the effect of He NTAP (p < 0.05). NTAP and phosphoric acid increased dentin polarity and Lewis base surface characteristics. Phosphoric acid increased contact angles of adhesives compared to the self-etch protocol (p < 0.05). NTAP resulted in lower adhesive contact angles than phosphoric acid, the difference being statistically significant for CUB (p < 0.05). Compared to the self-etch protocol, NTAP slightly reduced CUB contact angle but not that of SBU (p > 0.05).

Conclusions

He NTAP with and without O₂ increased dentin wetting and SFE, surpassing the effect of phosphoric acid and lowering adhesive contact angles. NTAP produced no apparent micro-morphological changes on dentin surface comparable to acid etching.

Clinical significance

NTAP treatment of dentin prior to adhesive application increases dentin wetting and surface free energy facilitating better adhesive distribution on dentin surface compared to phosphoric acid etching and similar to the “self-etch” application protocol.

     

Decision trees based on automatic learning and their use in cardiology

Computerized information systems, especially decision support systems, have become an increasingly important role in medical applications, particularly in those where important decision must be made effectively and reliably. But the possibility of using computers in medical decision making is limited by many difficulties, including the complexity of conventional computer languages, methodologies and tools. Thus a conceptual simple decision making model with the possibility of automating learning should be used. In this paper we introduce a cardiological knowledge-based system based on the decision tree approach supporting the mitral valve prolapse determination. Prolapse is defined as the displacement of a bodily part from its normal position. The term mitral valve prolaps (PMV), therefore, implies that the mitral leaflets are displaced relative to some structure, generally taken to be the mitral annulus. The implications of the PMV are the following: disturbed normal laminar blood flow, turbulence of the blood flow, injury of the chordae tendinae, the possibility of thrombus's composition, bacterial endocarditis, and finally hemodynamic changes defined as mitral insufficiency and mitral regurgitation. Uncertainty persists about how it should be diagnosed and about its clinical importance. It is our deep belief that the echocardiography enables properly trained experts armed with proper criteria to evaluate PMV almost 100%. But unfortunately, there are some problems concerned with the use of echocardiography. In that manner we have decided to start a research project aimed at finding new criteria and enabling the general practitioner to evaluate PMV using conventional methods and to select potential patients from the general population. To empower one to perform needed activities we have developed a computer tool called ROSE (computeRised prOlaps Syndrom dEtermination) based on algorithms of automatic learning. This tool supports the definition of new criteria and the selection of potential PMV-patients.