Knowledge of Brazilian dentists and students in treating dentine hypersensitivity

Objective: To evaluate knowledge of undergraduates and qualified dentists from a Brazilian Dental School in treating Dentine Hypersensitivity (DH). Methodology: Data obtained from a 22-item questionnaire were analysed and arranged in distribution figures. Results: Of 100 respondents, 66.3% indicated that up to 25% of their patients had DH; 41.7%, that the duration of discomfort was up to eight weeks; 78.4%, that they examined a patient with DH within the last two-four weeks; and 70.4%, that this was done after the patient initiated the conversation on DH. Most of participants responded DH affects patients’ quality of life, and its aetiology was attrition, exposed dentine, occlusal interference, gingival recession or abrasion. The most common ways to diagnose DH were sensitivity history analysis, clinical examination, clinical testing and probing; and conflicting conditions were fractured restoration, bleaching sensitivity, marginal leakage, chipped tooth and periodontal disease. Furthermore, 82.5% and 78.7% of respondents indicated they were confident in diagnosing DH and providing advice to patients, but only 38.8% identified hydrodynamic theory as its underlying mechanism. To evaluate pain from DH they considered self-assessment, dental examination, dietary analysis and thermal assessment; and as recommendations, the use of desensitizing dentifrices, education on toothbrushing, in-office application of desensitizing products, and restorations. Conclusion: There is still confusion concerning the aetiology, the diagnosis and the subsequent management of DH, and both students and qualified dentists need better education

A Nonlocal Elasto-Plastic Model for Structured Soils at Large Strains for the Particle Finite Element Method

This work presents a robust and mesh-independent implementation of an elasto-plastic constitutive model at large strains, appropriate for structured soils, into a Particle Finite Element code specially developed for geotechnical simulations. The constitutive response of structured soils is characterized by softening and, thus, leading to strain localization. Strain localization poses two numerical challenges: mesh dependence of the solution and computability of the solution. The former is mitigated by employing a non-local integral type regularization whereas an Implicit-Explicit integration scheme is used to enhance the computability. The good performance of these techniques is highlighted in the simulation of the cone penetration test in undrained conditions.Peer ReviewedPostprint (published version

Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1

Peer reviewedPublisher PD

Web-based monitoring tools for Resistive Plate Chambers in the CMS experiment at CERN

The Resistive Plate Chambers (RPC) are used in the CMS experiment at the trigger level and also in the standard offline muon reconstruction. In order to guarantee the quality of the data collected and to monitor online the detector performance, a set of tools has been developed in CMS which is heavily used in the RPC system. The Web-based monitoring (WBM) is a set of java servlets that allows users to check the performance of the hardware during data taking, providing distributions and history plots of all the parameters. The functionalities of the RPC WBM monitoring tools are presented along with studies of the detector performance as a function of growing luminosity and environmental conditions that are tracked over time

Radiation background with the CMS RPCs at the LHC

The Resistive Plate Chambers (RPCs) are employed in the CMS Experiment at the LHC as dedicated trigger system both in the barrel and in the endcap. This article presents results of the radiation background measurements performed with the 2011 and 2012 proton-proton collision data collected by CMS. Emphasis is given to the measurements of the background distribution inside the RPCs. The expected background rates during the future running of the LHC are estimated both from extrapolated measurements and from simulation

The multiple roles of myelin protein genes during the development of the oligodendrocyte

It has become clear that the products of several of the earliest identified myelin protein genes perform functions that extend beyond the myelin sheath. Interestingly, these myelin proteins, which comprise proteolipid protein, 2′,3′-cyclic nucleotide 3′-phosphodiesterase and the classic and golli MBPs (myelin basic proteins), play important roles during different stages of oligodendroglial development. These non-myelin-related functions are varied and include roles in the regulation of process outgrowth, migration, RNA transport, oligodendrocyte survival and ion channel modulation. However, despite the wide variety of cellular functions performed by the different myelin genes, the route by which they achieve these many functions seems to converge upon a common mechanism involving Ca2+ regulation, cytoskeletal rearrangements and signal transduction. In the present review, the newly emerging functions of these myelin proteins will be described, and these will then be discussed in the context of their contribution to oligodendroglial development

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c

Statistical Guidance for Experimental Design and Data Analysis of Mutation Detection in Rare Monogenic Mendelian Diseases by Exome Sequencing

Recently, whole-genome sequencing, especially exome sequencing, has successfully led to the identification of causal mutations for rare monogenic Mendelian diseases. However, it is unclear whether this approach can be generalized and effectively applied to other Mendelian diseases with high locus heterogeneity. Moreover, the current exome sequencing approach has limitations such as false positive and false negative rates of mutation detection due to sequencing errors and other artifacts, but the impact of these limitations on experimental design has not been systematically analyzed. To address these questions, we present a statistical modeling framework to calculate the power, the probability of identifying truly disease-causing genes, under various inheritance models and experimental conditions, providing guidance for both proper experimental design and data analysis. Based on our model, we found that the exome sequencing approach is well-powered for mutation detection in recessive, but not dominant, Mendelian diseases with high locus heterogeneity. A disease gene responsible for as low as 5% of the disease population can be readily identified by sequencing just 200 unrelated patients. Based on these results, for identifying rare Mendelian disease genes, we propose that a viable approach is to combine, sequence, and analyze patients with the same disease together, leveraging the statistical framework presented in this work