979 resultados para Eutectic mixture
Resumo:
Background: Allergen-specific immunotherapy with whole pollen extract may induce anaphylaxis, is poorly standardized and of long duration.We thus designed a randomized, placebo-controlled phase I/II clinical trial in volunteers with birch pollen allergic rhinitis and asthma to evaluate the safety and immunogenicity of a novel immunotherapy based on contiguous overlapping peptides (COPs) derived from Bet v 1, the major birch pollen allergen. Methods: A mixture of three COPs (AllerT™, Anergis SA, Switzerland) spanning the whole Bet v 1 molecule was selected for its inability to bind IgE. Prior to the pollen season, AllerT (in Alum) was injected subcutaneously to 15 adult volunteers at D0 (57 g), D7, D14, D21 and D51 (95 g each). Control volunteers (n = 5) only received the adjuvant. Results: Overall AllerT was safe. No serious adverse events and no immediate allergic reactions were reported. AllerT induced a vigorous early Bet v 1 specific immune response marked by vaccine associated INF- and IL- 10 secretion. This contributed to a strong anti-Bet v 1-specific IgG4 enhancement. Moreover, 2 months after the second season post treatment (July 2010), serum Bet v 1 specific IgG4 response was still markedly increased as compared to pre-treatment values and to placebo whereas post seasonal Bet v 1 specific IgE titers were similar to baseline values. Conclusion: Our data indicate that immunotherapy with a mixture of three COPs derived from Bet v 1 (AllerT) was safe and immunogenic, and led to long-term immunological memory.
Resumo:
This paper discusses the analysis of cases in which the inclusion or exclusion of a particular suspect, as a possible contributor to a DNA mixture, depends on the value of a variable (the number of contributors) that cannot be determined with certainty. It offers alternative ways to deal with such cases, including sensitivity analysis and object-oriented Bayesian networks, that separate uncertainty about the inclusion of the suspect from uncertainty about other variables. The paper presents a case study in which the value of DNA evidence varies radically depending on the number of contributors to a DNA mixture: if there are two contributors, the suspect is excluded; if there are three or more, the suspect is included; but the number of contributors cannot be determined with certainty. It shows how an object-oriented Bayesian network can accommodate and integrate varying perspectives on the unknown variable and how it can reduce the potential for bias by directing attention to relevant considerations and distinguishing different sources of uncertainty. It also discusses the challenge of presenting such evidence to lay audiences.
Resumo:
Pseudomonas aeruginosa undergoes spontaneous mutation that impairs secretion of several extracellular enzymes during extended cultivation in vitro in rich media, as well as during long-term colonization of the cystic fibrosis lung. A frequent type of strong secretion deficiency is caused by inactivation of the quorum-sensing regulatory gene lasR. Here we analyzed a spontaneously emerging subline of strain PAO1 that exhibited moderate secretion deficiency and partial loss of quorum-sensing control. Using generalized transduction, we mapped the secretion defect to the vfr gene, which is known to control positively the expression of the lasR gene and type II secretion of several proteases. We confirmed this secretion defect by sequencing and complementation of the vfr mutation. In a reconstruction experiment conducted with a 1:1 mixture of wild-type strain PAO1 and a vfr mutant of PAO1, we observed that the vfr mutant had a selective advantage over the wild type after growth in static culture for 4 days. Under these conditions, spontaneous vfr emerged in a strain PAO1 population after four growth cycles, and these mutants accounted for more than 40% of the population after seven cycles. These results suggest that partial or complete loss of quorum sensing and secretion can be beneficial to P. aeruginosa under certain environmental conditions.
Resumo:
In this work we present a method for the image analysisof Magnetic Resonance Imaging (MRI) of fetuses. Our goalis to segment the brain surface from multiple volumes(axial, coronal and sagittal acquisitions) of a fetus. Tothis end we propose a two-step approach: first, a FiniteGaussian Mixture Model (FGMM) will segment the image into3 classes: brain, non-brain and mixture voxels. Second, aMarkov Random Field scheme will be applied tore-distribute mixture voxels into either brain ornon-brain tissue. Our main contributions are an adaptedenergy computation and an extended neighborhood frommultiple volumes in the MRF step. Preliminary results onfour fetuses of different gestational ages will be shown.