Bacterial vaginosis biofilms: challenges to current therapies and emerging solutions

Bacterial vaginosis (BV) is the most common genital tract infection in women during their reproductive years and it has been associated with serious health complications, such as preterm delivery and acquisition or transmission of several sexually transmitted agents. BV is characterized by a reduction of beneficial lactobacilli and a significant increase in number of anaerobic bacteria, including Gardnerella vaginalis, Atopobium vaginae, Mobiluncus spp., Bacteroides spp. and Prevotella spp.. Being polymicrobial in nature, BV etiology remains unclear. However, it is certain that BV involves the presence of a thick vaginal multi-species biofilm, where G. vaginalis is the predominant species. Similar to what happens in many other biofilm-related infections, standard antibiotics, like metronidazole, are unable to fully eradicate the vaginal biofilm, which can explain the high recurrence rates of BV. Furthermore, antibiotic therapy can also cause a negative impact on the healthy vaginal microflora. These issues sparked the interest in developing alternative therapeutic strategies. This review provides a quick synopsis of the currently approved and available antibiotics for BV treatment while presenting an overview of novel strategies that are being explored for the treatment of this disorder, with special focus on natural compounds that are able to overcome biofilm-associated antibiotic resistance.

Characterization of the oculomotor alterations in inicial phase of Multiple Sclerosis

Dissertação de mestrado em Advanced Optometry

Assessment of innovative solutions for non-load bearing masonry enclosures

This paper presents some of the results of the research project “Masonry Enclosures” developed in the framework of the transnational access (TA) to LNEC’s triaxial shake table within the FP7 project SERIES.

Column-based databases: estudo exploratório no âmbito das bases de dados NoSQL

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Exploração de dados para soluções de retalho em business intelligence

Dissertação de mestrado integrado em Engenharia de Gestão e Sistemas de Informação

Implementação de um sistema de customer relationship management na broaden information solutions

Dissertação de mestrado em Sistemas de Informação

Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias Tecnológicas

Cognitive functioning and efficacy of cognitive intervention in Multiple Sclerosis

Tese de Doutoramento em Psicologia Clínica / Psicologia

Sustainability assessment of envelope wall solutions using MAXergy methodology

Projeto de investigação integrado de International Master in Sustainable Built Environment

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.

Avaliação do desempenho ambiental do aglomerado negro de cortiça

Dissertação de mestrado integrado em Engenharia Civil

Current Challenges in Modeling Cellular Metabolism

Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The recent advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms from microbes to human cells. These models have been successfully used in multiple biotechnological applications. Despite these advancements, modeling cellular metabolism still presents many challenges. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. The articles presented in this e-book address some of the main challenges in the field, including the integration of different modeling formalisms, the integration of heterogeneous data sources into metabolic models, explicit representation of other biological processes during phenotype simulation, and standardization efforts in the representation of metabolic models and simulation results.

A modular traffic sampling architecture for flexible network measurements

Dissertação de Mestrado (Programa Doutoral em Informática)

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.