Purpose: The goal of this in vitro study was to identify the topographical features of the enamel surface deproteinized and etched with phosphoric acid (H3PO4) compared to phosphoric acid alone. Materials and method: Ten extracted lower first and second permanent molars were polished with pumice and water, and then divided into 4 equal buccal sections having similar physical and chemical properties. The enamel surfaces of each group were subjected to the following treatments: Group A: Acid Etching with H3PO4 37% for 15 seconds. Group AH1: Sodium Hypochlorite (NaOCl) 5.25% for 30 seconds followed by Acid Etching with H3PO4 37% for 15 seconds. Group AH2 ; Sodium Hypochlorite (NaOCl) 5.25% for 60 seconds followed by Acid Etching with H3PO4 37% for 15 seconds. Results showed that group AH2 etching technique reached an area of 76.6 mm2 of the total surface, with a 71.8 mm2 (94.47%), type 1 and 2 etching pattern, followed by group AH1 with 55.9 mm2 out of 75.12 mm2 (74.1%), and finally group A with only 36.8 mm2 (48.83%) out of an area of 72.7 mm2. A significant statistical difference (P <0 .05) existed between all groups, leading to the conclusion that enamel deproteinization with 5.25% NaOCl for 1 minute before H3PO4, etching increases the enamel conditioning surface as well as the quality of the etching pattern.

Purpose: The goal of this in vitro study was to identify the topographical features of deproteinized (NaOCl)and etched with phosphoric acid (H3PO4) enamel surface, compared to phosphoric acid surface alone with a Resin Replica model. Materials: Ten extracted lower first and second permanent molars were polished with pumice and water, and then divided into 3 equal buccal sections having similar physical and chemical properties. The enamel surfaces of each group were subjected to the following treatments: Group A: Acid Etching with H3PO4 37% for 15 seconds. Group B: Sodium Hypochlorite (NaOCl) 5.25% for 60 seconds followed by Acid Etching with H3PO4 37% for 15 seconds. Group C; No treatment (control). All the samples were treated as follow: Adhesive and resin were applied to all groups after A, B and C treatment were performed; Then enamel/dentin decalcification and deproteinization and topographic SEM Resin Replica assessment were used to identify resin tags enamel surface quality penetration. Results showed that group B reached an area of 7.52mm2 of the total surface, with a 5.68 mm2 (73%)resin tag penetration equivalent type I and II etching pattern, 1.71 mm2 (26%) equivalent to type III etching pattern and 0.07 mm2 (1%)unaffected surface. Followed by group A with 7.48 mm2 of the total surface, with a 3.47 mm2 (46 %)resin tag penetration equivalent to type I and II etching pattern, 3.30 mm2 (45 %)equivalent to type III etching pattern and 0.71 mm2, and (9 %) unaffected surface. Group C did not show any resin tag penetration. A significant statistical difference (P <0,001) existed between groups A and B in resin quality penetration, leading to the conclusion that when the enamel is deproteinizated with 5.25% NaOCl for 1 minute prior H3PO4,the surface and topographical features of the replica resin penetration surface increases significantly with type I-II etching pattern.

Modifying the mini-implant surface with sandblasting and acid treatment offers good bone anchoring for orthodontic purposes.

The enamel deproteinization technique is an effective way to remove organic material on the occlusal surfaces of teeth, obtaining after phosphoric acid application, up to 72.38% of Types I and II etch patterns. Etching Types I or II can also be determined by the removal of organic material in between enamel crystals.

Non-expert users find complex to gain richer insights into the increasingly amount of available heterogeneous data, the so called big data. Advanced data analysis techniques, such as data mining, are difficult to apply due to the fact that (i) a great number of data mining algorithms can be applied to solve the same problem, and (ii) correctly applying data mining techniques always requires dealing with the inherent features of the data source. Therefore, we are attending a novel scenario in which non-experts are unable to take advantage of big data, while data mining experts do: the big data divide. In order to bridge this gap, we propose an approach to offer non-expert miners a tool that just by uploading their data sets, return them the more accurate mining pattern without dealing with algorithms or settings, thanks to the use of a data mining algorithm recommender. We also incorporate a previous task to help non-expert users to specify data mining requirements and a later task in which users are guided in interpreting data mining results. Furthermore, we experimentally test the feasibility of our approach, in particular, the method to build recommenders in an educational context, where instructors of e-learning courses are non-expert data miners who need to discover how their courses are used in order to make informed decisions to improve them.