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Supplementary Material for: Design of Erosion/Abrasion Studies – Insights and Rational Concepts

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posted on 31.05.2011, 00:00 by Wiegand A., Attin T.
In vitro and in situ studies modelling the wear of dental hard tissues due to erosion and abrasion are characterised by a high variation in study designs and experimental parameters. Based on a summary of the existing protocols, the present review aimed to describe and discuss the parameters which must be carefully considered in erosion-abrasion research, especially when it is intended to simulate clinical conditions. Experimental characteristics and parameters were retrieved from a total of 42 in vitro and 20 in situ studies. The key experimental characteristics included parameters of erosion (duration and pH) and abrasion (duration, kinds of toothbrush and toothpaste, brushing force, and time point) as well as co-factors (e.g. dental hard tissue). The majority of studies used models with alternating erosion/abrasion treatments intended to simulate clinical conditions, while other studies exaggerated clinical conditions intentionally, often using only a single erosion/abrasion treatment. Both in vitro and in situ models shared a high level of standardisation, but several studies showed a trend to severe erosion (e.g. >5 min/cycle) or extensive brushing (e.g. >100 brushing strokes/cycle) at a high frequency and repetition rate. Thus, studies often tend to produce a higher amount of wear than in the clinical situation, especially as modifying biological factors (e.g. the dilution of the erosive solution by saliva and the protective effect of the pellicle) cannot be simulated adequately. With respect to the existing models, it seems advisable to diminish duration and frequency of erosion and abrasion to more realistic clinical conditions when the everyday situation is to be simulated. Experimental parameters must be chosen with care to ensure that the problem is investigated in an appropriate mode at standardised conditions and with adequate measuring systems to allow prediction of clinical outcomes.


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