Dental enamel is the epithelial-derived hard tissue covering the crowns of teeth. It is the most highly mineralized and hardest tissue in the body. Dental enamel is acellular and has no physiological means of repair outside of the protective and remineralization potential provided by saliva. Enamel is comprised of highly organized hydroxyapatite crystals that form in a defined extracellular space, the contents of which are supplied and regulated by ameloblasts. The entire process is under genetic instruction. The genetic control of amelogenesis is poorly understood, but requires the activities of multiple components that are uniquely important for dental enamel formation. Amelogenesis imperfecta (AI) is a collective designation for the variety of inherited conditions displaying isolated enamel malformations, but the designation is also used to indicate the presence of an enamel phenotype in syndromes. Recently, genetic studies have demonstrated the importance of genes encoding enamel matrix proteins in the etiology of isolated AI. Here we review the essential elements of dental enamel formation and the results of genetic analyses that have identified disease-causing mutations in genes encoding enamel matrix proteins. In addition, we provide a fresh perspective on the roles matrix proteins play in catalyzing the biomineralization of dental enamel.

An emerging concept suggests that a pathogenic burden from different sources might overcome an individual threshold culminating in clinical sequela. P. gingivalis contributes directly and indirectly to atherosclerosis.

ObjectiveThe aim of this study was to evaluate the susceptibility of dentin to brushing abrasion using four different toothbrushes (rotating-oscillating, sonic and two types of manual toothbrushes) with the same brushing forces.MethodsDentin samples (n = 72) were selected from 72 impacted third molars. Half of the surface of dentin samples was covered with an adhesive tape, creating a protected and a freely exposed area in the same specimen. Brushing was performed with either a: sonic (Sonicare PowerUp, Philips GmbH, Hamburg, Germany), b: oscillating-rotating (Oral B Vitality Precisions Clean, Procter & Gamble, Schwalbach am Taunus, Germany) or two different manual toothbrushes c: flat trim brush head toothbrush (Dr. Best: Original, Glaxo-Smith-Kline, Bühl, Germany) and d: rippled-shaped brush head toothbrush (Blend-a-Dent, Complete V-Interdental, Blend-a-med, Schwalbach, Germany) in a custom made automatic brushing machine. The brushing force was set to 2 N and a whitening toothpaste (RDA = 150) was used. The simulation period was performed over a calculated period to mimic a brushing behavior of two times a day brushing for eight years and six months. Dentin loss was quantitatively determined by profilometry and statistically analyzed by Wilcoxon and Mann-Whitney-U Test (p < 0.05).ResultsThe mean (standard deviation) surface loss was 21.03 (±1.26) μm for the sonic toothbrush, 15.71 (±0.85) μm for the oscillating-rotating toothbrush, 6.13 (±1.24) μm for the manual toothbrush with flat trim brush head and 2.50 (±0.43) μm for the manual toothbrush with rippled-shaped brush head. Differences between all groups were statistically significant at p<0.05.ConclusionUsing the same brushing force and a highly abrasive toothpaste, manual toothbrushes are significantly less abrasive compared to power toothbrushes for an 8.5—year simulation.

In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging. Incorporating image mapping convolutional neural networks (CNN) into iterative image recovery has great potential to resolve this issue. This paper 1) incorporates image mapping CNN using identical convolutional kernels in both encoders and decoders into a block coordinate descent (BCD) signal recovery method and 2) applies alternating direction method of multipliers to train the aforementioned image mapping CNN. We refer to the proposed recurrent network as BCD-Net using identical encodingdecoding CNN structures. Numerical experiments show that, for a) denoising low signal-to-noise-ratio images and b) extremely undersampled magnetic resonance imaging, the proposed BCD-Net achieves significantly more accurate image recovery, compared to BCD-Net using distinct encoding-decoding structures and/or the conventional image recovery model using both wavelets and total variation.

Insulin-dependent type 1 diabetes mellitus (DM) and oral diseases are closely interrelated. Poor metabolic control in diabetics is associated with a high risk of gingivitis, periodontitis and tooth loss. Salivary flow declines in diabetics and patients suffer from xerostomia. Reduced saliva predisposes to enamel hypomineralization and caries formation; however, the mechanisms that initiate and lead to progression of tooth decay and periodontitis in type 1 DM have not been explored. To address this issue, we analyzed tooth morphology in Akita −/− mice that harbor a point mutation in the Ins2 insulin gene, which leads to progressive hyperglycemia. Mandibles from Akita −/− and wild-type littermates were analyzed by microCT, scanning EM and histology; teeth were examined for amelogenin (Amel) and ameloblastin (Ambn) expression. Mice were injected with pilocarpine to assess saliva production. As hyperglycemia may alter pulp repair, the effect of high glucose levels on the proliferation/differentiation of cultured MD10-F2 pulp cells was also analyzed. Results showed that Akita −/− mice at 6 weeks of age showed chalky white incisors that correlated with marked hyperglycemia and impaired saliva production. MicroCT of Akita −/− teeth revealed excessive enamel wearing and hypomineralization; immunostaining for Amel and Ambn was decreased. A striking feature was invasion of dentinal tubules with Streptococcus mitis and microabcesses that originated in the coronal pulp and progressed to pulp necrosis and periapical periodontitis. High levels of glucose also inhibited MD10-F2 cell proliferation and differentiation. Our findings provide the first evidence that hyperglycemia in combination with reduced saliva in a model of type1 DM leads to decreased enamel mineralization/matrix proteins and predisposes to excessive wearing and decay. Importantly, hyperglycemia adversely affects enamel matrix proteins and pulp repair. Early detection and treatment of hyperglycemia and hyposalivation may provide a useful strategy for preventing the dental complications of diabetes and promoting oral health in this population.