Thickness of softened human enamel removed by toothbrush abrasion: an in vitro study

The aim of the study was to assess the thickness of softened enamel removed by toothbrushing. Human enamel specimens were indented with a Knoop diamond. Softening was performed with citric acid or orange juice. The specimens were brushed in a brushing machine with a manual soft toothbrush in toothpaste slurry or in artificial saliva. Enamel loss was calculated from the change in indentation depth of the same indent before and after abrasion. Mean surface losses (95% confidence interval) were recorded in treatment groups (in nanometers): (1) citric acid, abrasion with slurry = 339 (280-398); (2) citric acid, abrasion with artificial saliva = 16 (5-27); (3) orange juice, abrasion with slurry = 268 (233-303); (4) orange juice, abrasion with artificial saliva = 14 (5-23); (5) no softening, abrasion with slurry = 28 (10-46). The calculated thickness of the softened enamel varied between 254 and 323 nm, depending on the acid used.

Controlled toothbrush abrasion of softened human enamel

The aim of this in vitro study was to compare toothbrush abrasion of softened enamel after brushing with two (soft and hard) toothbrushes. One hundred and fifty-six human enamel specimens were indented with a Knoop diamond. Salivary pellicle was formed in vitro over a period of 3 h. Erosive lesions were produced by means of 1% citric acid. A force-measuring device allowed a controlled toothbrushing force of 1.5 N. The specimens were brushed either in toothpaste slurry or with toothpaste in artificial saliva for 15 s. Enamel loss was calculated from the change in indentation depth of the same indent before and after abrasion. Mean surface losses (95% CI) were recorded in ten treatment groups: (1) soft toothbrush only [28 (17-39) nm]; (2) hard toothbrush only [25 (16-34) nm]; (3) soft toothbrush in Sensodyne MultiCare slurry [46 (27-65) nm]; (4) hard toothbrush in Sensodyne MultiCare slurry [45 (24-66) nm]; (5) soft toothbrush in Colgate sensation white slurry [71 (55-87) nm]; (6) hard toothbrush in Colgate sensation white slurry [85 (60-110) nm]; (7) soft toothbrush with Sensodyne MultiCare [48 (39-57) nm]; (8) hard toothbrush with Sensodyne MultiCare [40 (29-51) nm]; (9) soft toothbrush with Colgate sensation white [51 (37-65) nm]; (10) hard toothbrush with Colgate sensation white [52 (36-68) nm]. Neither soft nor hard toothbrushes produced significantly different toothbrush abrasion of softened human enamel in this model (p > 0.05).

Top-Down Parsing with Parsing Contexts

The domain of context-free languages has been extensively explored and there exist numerous techniques for parsing (all or a subset of) context-free languages. Unfortunately, some programming languages are not context-free. Using standard context-free parsing techniques to parse a context-sensitive programming language poses a considerable challenge. Im- plementors of programming language parsers have adopted various techniques, such as hand-written parsers, special lex- ers, or post-processing of an ambiguous parser output to deal with that challenge. In this paper we suggest a simple extension of a top-down parser with contextual information. Contrary to the tradi- tional approach that uses only the input stream as an input to a parsing function, we use a parsing context that provides ac- cess to a stream and possibly to other context-sensitive infor- mation. At a same time we keep the context-free formalism so a grammar definition stays simple without mind-blowing context-sensitive rules. We show that our approach can be used for various purposes such as indent-sensitive parsing, a high-precision island parsing or XML (with arbitrary el- ement names) parsing. We demonstrate our solution with PetitParser, a parsing-expression grammar based, top-down, parser combinator framework written in Smalltalk.