Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium

Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10-06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.

Searching for therapeutic strategies in a mouse modelo of Machado-Joseph disease: targeting proteostases

Tese de Doutoramento em Ciências da Saúde

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil

Promising blood-derived biomarkers for estimation of the postmortem interval

A precise estimation of the postmortem interval (PMI) is one of the most important topics in forensic pathology. However, the PMI estimation is based mainly on the visual observation of cadaverous pheno- mena (e.g. algor, livor and rigor mortis) and on alternative methods such as thanatochemistry that remain relatively imprecise. The aim of this in vitro study was to evaluate the kinetic alterations of several bio- chemical parameters (i.e. proteins, enzymes, substrates, electrolytes and lipids) during putrefaction of human blood. For this purpose, we performed kinetic biochemical analysis during a 264 hour period. The results showed a significant linear correlation between total and direct bilirubin, urea, uric acid, transferrin, immunoglobulin M (IgM), creatine kinase (CK), aspartate transaminase (AST), calcium and iron with the time of blood putrefaction. These parameters allowed us to develop two mathematical models that may have predictive values and become important complementary tools of traditional methods to achieve a more accurate PMI estimation

A modular traffic sampling architecture for flexible network measurements

Dissertação de Mestrado (Programa Doutoral em Informática)

Estimation of statistical cure from cancer using population-based data

Dissertação de mestrado em Estatística

Development of a water-in-oil-in-water multiple emulsion system integrating biomimetic aqueous-core lipid nanodroplets for protein entity stabilization. Part I: experimental factorial design

Lipid nanoballoons integrating multiple emulsions of the type water-in-oil-in-water enclose, at least in theory, a biomimetic aqueous-core suitable for housing hydrophilic biomolecules such as proteins, peptides and bacteriophage particles. The research effort entertained in this paper reports a full statistical 23x31 factorial design study (three variables at two levels and one variable at three levels) to optimize biomimetic aqueous-core lipid nanoballoons for housing hydrophilic protein entities. The concentrations of protein, lipophilic and hydrophilic emulsifiers, and homogenization speed were set as the four independent variables, whereas the mean particle hydrodynamic size (HS), zeta potential (ZP) and polydispersity index (PI) were set as the dependent variables. The V23x31 factorial design constructed led to optimization of the higher (+1) and lower (-1) levels, with triplicate testing for the central (0) level, thus producing thirty three experiments and leading to selection of the optimized processing parameters as 0.015% (w/w) protein entity, 0.75% (w/w) lipophilic emulsifier (soybean lecithin) and 0.50% (w/w) hydrophilic emulsifier (poloxamer 188). In the present research effort, statistical optimization and production of protein derivatives encompassing full stabilization of their three-dimensional structure, has been attempted via housing said molecular entities within biomimetic aqueous-core lipid nanoballoons integrating a multiple (W/O/W) emulsion.