FHIT gene therapy prevents tumor development in Fhit-deficient mice

The tumor suppressor gene FHIT spans a common fragile site and is highly susceptible to environmental carcinogens. FHIT inactivation and loss of expression is found in a large fraction of premaligant and malignant lesions. In this study, we were able to inhibit tumor development by oral gene transfer, using adenoviral or adenoassociated viral vectors expressing the human FHIT gene, in heterozygous Fhit+/- knockout mice, that are prone to tumor development after carcinogen exposure. We therefore suggest that FHIT gene therapy could be a novel clinical approach not only in treatment of early stages of cancer, but also in prevention of human cancer

Efficacy and Safety Comparison of Liraglutide, Glimepiride, and Placebo, All in Combination With Metformin, in Type 2 Diabetes: The LEAD (Liraglutide Effect and Action in Diabetes)-2 study

OBJECTIVE—The efficacy and safety of adding liraglutide (a glucagon-like peptide-1 receptor agonist) to metformin were compared with addition of placebo or glimepiride to metformin in subjects previously treated with oral antidiabetes (OAD) therapy

GLP-1R Agonist Liraglutide Activates Cytoprotective Pathways and Improves Outcomes After Experimental Myocardial Infarction in Mice

OBJECTIVE—Glucagon-like peptide-1 receptor (GLP-1R) ago-nists are used to treat type 2 diabetes, and transient GLP-1 administration improved cardiac function in humans after acute myocardial infarction (MI) and percutaneous revascularization. However, the consequences of GLP-1R activation before isch-emic myocardial injury remain unclear. RESEARCH DESIGN AND METHODS—We assessed the pathophysiology and outcome of coronary artery occlusion in normal and diabetic mice pretreated with the GLP-1R agonist liraglutide. RESULTS—Male C57BL/6 mice were treated twice daily for 7 days with liraglutide or saline followed by induction of MI. Survival was significantly higher in liraglutide-treated mice. Lira-glutide reduced cardiac rupture (12 of 60 versus 46 of 60; P 0.0001) and infarct size (21 2 % versus 29 3%, P 0.02) an

Is analysing the nitrogen use at the plant canopy level a matter of choosing the right optimization criterion?

Optimization theory in combination with canopy modeling is potentially a powerful tool for evaluating the adaptive significance of photosynthesis-related plant traits. Yet its successful application has been hampered by a lack of agreement on the appropriate optimization criterion. Here we review how models based on different types of optimization criteria have been used to analyze traits—particularly N reallocation and leaf area indices—that determine photosynthetic nitrogen-use efficiency at the canopy level. By far the most commonly used approach is static-plant simple optimization (SSO). Static-plant simple optimization makes two assumptions: (1) plant traits are considered to be optimal when they maximize whole-stand daily photosynthesis, ignoring competitive interactions between individuals; (2) it assumes static plants, ignoring canopy dynamics (production and loss of leaves, and the reallocation and uptake of nitrogen) and the respiration of nonphotosynthetic tissue. Recent studies have addressed either the former problem through the application of evolutionary game theory (EGT) or the latter by applying dynamic-plant simple optimization (DSO), and have made considerable progress in our understanding of plant photosynthetic traits. However, we argue that future model studies should focus on combining these two approaches. We also point out that field observations can fit predictions from two models based on very different optimization criteria. In order to enhance our understanding of the adaptive significance of photosynthesis-related plant traits, there is thus an urgent need for experiments that test underlying optimization criteria and competing hypotheses about underlying mechanisms of optimization

A theoretical approach to spot active regions in antimicrobial proteins

Background: Much effort goes into identifying new antimicrobial compounds able to evade the increasing resistance of microorganisms to antibiotics. One strategy relies on antimicrobial peptides, either derived from fragments released by proteolytic cleavage of proteins or designed from known antimicrobial protein regions. Results: To identify these antimicrobial determinants, we developed a theoretical approach that predicts antimicrobial proteins from their amino acid sequence in addition to determining their antimicrobial regions. A bactericidal propensity index has been calculated for each amino acid, using the experimental data reported from a high-throughput screening assay as reference. Scanning profiles were performed for protein sequences and potentially active stretches were identified by the best selected threshold parameters. The method was corroborated against positive and negative datasets. This successful approach means that we can spot active sequences previously reported in the literature from experimental data for most of the antimicrobial proteins examined. Conclusion: The method presented can correctly identify antimicrobial proteins with an accuracy of 85% and a sensitivity of 90%. The method can also predict their key active regions, making this a tool for the design of new antimicrobial drugs