Assessment of liposome disruption to quantify drug delivery in vitro

Efficient liposome disruption inside the cells is a key for success with any type of drug delivery system. The efficacy of drug delivery is currently evaluated by direct visualization of labeled liposomes internalized by cells, not addressing objectively the release and distribution of the drug. Here, we propose a novel method to easily assess liposome disruption and drug release into the cytoplasm. We propose the encapsulation of the cationic dye Hoechst 34,580 to detect an increase in blue fluorescence due to its specific binding to negatively charged DNA. For that, the dye needs to be released inside the cell and translocated to the nucleus. The present approach correlates the intensity of detected fluorescent dye with liposome disruption and consequently assesses drug delivery within the cells.

The influence of biofilm formation by Gardnerella vaginalis and other anaerobes on bacterial vaginosis

Bacterial vaginosis (BV) is the worldwide leading vaginal disorder in women of reproductive age. BV is characterized by the replacement of beneficial lactobacilli and the augmentation of anaerobic bacteria. Gardnerella vaginalis is a predominant bacterial species, however, BV is also associated with other numerous anaerobes, such as Atopobium vaginae, Mobiluncus mulieris, Prevotella bivia, Fusobacterium nucleatum and Peptoniphilus sp.. Currently, the role of G. vaginalis in the etiology of BV remains a matter of controversy. It is however known that, in BV patients, a biofilm is usually formed on the vaginal epithelium and G. vaginalis is typically the predominant species. So, the current paradigm is that the establishment of a biofilm plays a key role in the pathogenesis of BV. This review provides background on the influence of biofilm formation by G. vaginalis and other anaerobes in the polymicrobial etiology of BV, through its initial adhesion until biofilm formation and discusses the commensal and synergic interactions established between them to understand the phenotypic shift of G. vaginalis' biofilms into BV establishment.

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.