Mineral exchange within restorative materials following incomplete carious lesion removal using 3D non-destructive XMT subtraction methodology.

OBJECTIVES: The objective of this study was to quantify the changes in mineral and selected element concentrations within residual carious dentine and restorative materials following incomplete carious lesion removal (ICLR) using different cavity liners, with non-destructive subtraction 3D-X-ray Microtomography (XMT, QMUL, London, UK). MATERIALS AND METHODS: A total of 126 extracted teeth with deep dental caries were assessed using International Caries Risk and Assessment (ICDAS). Eight teeth were subsequently selected after radiographic evaluation. Each lesion was removed, leaving a thin layer of leathery dentine at the deepest part of cavity. Different cavity lining materials were placed; Mineral Trioxide Aggregate (MTA), calcium hydroxide, (Ca(OH)2), resin-based material (RBM). For each, the restorative material was an encapsulated glass ionomer (GIC) and the control group had a GIC restoration alone. Each tooth was immediately placed in Simulated Body Fluid (SBF). All samples were then imaged using XMT at baseline, and three weeks after treament. The XMT images were then subtracted to show the mineral concentration changes three weeks after treatment. RESULTS: There were significant increases in mineral concentrations within the residual demineralised dentine in individual teeth treated with Ca(OH)2, MTA, RBM, and GIC following immersion in SBF for three weeks. GIC group without any liners showed the greatest increase in mineral concentration, followed by MTA and Ca(OH)2. CONCLUSION: Mineral changes in demineralised dentine and within restorative materials are quantifiable using non-destructive 3D-XMT subtraction methodology. This laboratory study suggested that calcium, phosphate and strontium ion-exchange occurs with GIC, MTA and Ca(OH)2 in deep dentinal lesions following ICLR. CLINICAL RELEVANCE: In clinical practice, incomplete carious lesion removal could be performed to avoid the dental pulp exposure. 3D non-destructive XMT subtraction methodology in a laboratory setting is advantageous to provide evidence for different restorative materials on deep carious lesions prior to clinical investigations.

A database for curating the associations between killer cell immunoglobulin-like receptors and diseases in worldwide populations.

The killer cell immunoglobulin-like receptors (KIR) play a fundamental role in the innate immune system, through their interactions with human leucocyte antigen (HLA) molecules, leading to the modulation of activity in natural killer (NK) cells, mainly related to killing pathogen-infected cells. KIR genes are hugely polymorphic both in the number of genes an individual carries and in the number of alleles identified. We have previously developed the Allele Frequency Net Database (AFND, http://www.allelefrequencies.net), which captures worldwide frequencies of alleles, genes and haplotypes for several immune genes, including KIR genes, in healthy populations, covering >4 million individuals. Here, we report the creation of a new database within AFND, named KIR and Diseases Database (KDDB), capturing a large quantity of data derived from publications in which KIR genes, alleles, genotypes and/or haplotypes have been associated with infectious diseases (e.g. hepatitis C, HIV, malaria), autoimmune disorders (e.g. type I diabetes, rheumatoid arthritis), cancer and pregnancy-related complications. KDDB has been created through an extensive manual curation effort, extracting data on more than a thousand KIR-disease records, comprising >50 000 individuals. KDDB thus provides a new community resource for understanding not only how KIR genes are associated with disease, but also, by working in tandem with the large data sets already present in AFND, where particular genes, genotypes or haplotypes are present in worldwide populations or different ethnic groups. We anticipate that KDDB will be an important resource for researchers working in immunogenetics. Database URL: http://www.allelefrequencies.net/diseases/.