Vaccine glycoproteins tagged with the human Fc domain as second generation vaccine candidates

Traditional vaccines such as inactivated or live attenuated vaccines, are gradually giving way to more biochemically defined vaccines that are most often based on a recombinant antigen known to possess neutralizing epitopes. Such vaccines can offer improvements in speed, safety and manufacturing process but an inevitable consequence of their high degree of purification is that immunogenicity is reduced through the lack of the innate triggering molecules present in more complex preparations. Targeting recombinant vaccines to antigen presenting cells (APCs) such as dendritic cells however can improve immunogenicity by ensuring that antigen processing is as efficient as possible. Immune complexes, one of a number of routes of APC targeting, are mimicked by a recombinant approach, crystallizable fragment (Fc) fusion proteins, in which the target immunogen is linked directly to an antibody effector domain capable of interaction with receptors, FcR, on the APC cell surface. A number of virus Fc fusion proteins have been expressed in insect cells using the baculovirus expression system and shown to be efficiently produced and purified. Their use for immunization next to non-Fc tagged equivalents shows that they are powerfully immunogenic in the absence of added adjuvant and that immune stimulation is the result of the Fc-FcR interaction.

Expression of epitope-tagged LMW glutenin subunits in the starchy endosperm of transgenic wheat and their incorporation into glutenin polymers

The low-molecular-weight (LMW) glutenin subunits are components of the highly cross-linked glutenin polymers that confer viscoelastic properties to gluten and dough. They have both quantitative and qualitative effects on dough quality that may relate to differences in their ability to form the inter-chain disulphide bonds that stabilise the polymers. In order to determine the relationship between dough quality and the amounts and properties of the LMW subunits, we have transformed the pasta wheat cultivars Svevo and Ofanto with three genes encoding proteins, which differ in their numbers or positions of cysteine residues. The transgenes were delivered under control of the high-molecular-weight (HMW) subunit 1Dx5 gene promoter and terminator regions, and the encoded proteins were C-terminally tagged by the introduction of the c-myc epitope. Stable transformants were obtained with both cultivars, and the use of a specific antibody to the c-myc epitope tag allowed the transgene products to be readily detected in the complex mixture of LMW subunits. A range of transgene expression levels was observed. The addition of the epitope tag did not compromise the correct folding of the trangenic subunits and their incorporation into the glutenin polymers. Our results demonstrate that the ability to specifically epitope-tag LMW glutenin transgenes can greatly assist in the elucidation of their individual contributions to the functionality of the complex gluten system.

Tagged phosphine reagents to assist reaction work-up by phase-switched scavenging using a modular flow reactor

The use of three orthogonally tagged phosphine reagents to assist chemical work-up via phase-switch scavenging in conjunction with a modular flow reactor is described. These techniques (acidic, basic and Click chemistry) are used to prepare various amides and tri-substituted guanidines from in situ generated iminophosphoranes.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

MRI study of radiation effect on Fricke gel solutions

The quality control optimization of medical processes that use ionizing radiation in the treatment of diseases like cancer is a key element for patient safety and success of treatment. The major medical application of radiation is radiotherapy, i.e. the delivery of dose levels to well-defined target tissues of a patient with the purpose of eliminating a disease. The need of an accurate tumour-edge definition with the purpose of preserving healthy surrounding tissue demands rigorous radiation treatment planning. Dosimetric methods are used for dose distribution mapping region of interest to assure that the prescribed dose and the irradiated region are correct. The Fricke gel (FXG) is the main dosimeter that supplies visualization of the three-dimensional (3D) dose distribution. In this work the dosimetric characteristics of the modified Fricke dosimeter produced at the Radiation Metrology Centre of the Institute of Energetic and Nuclear Research (IPEN) such as gel concentration dose response dependence, xylenol orange addition influence, dose response between 5 and 50Gy and signal stability were evaluated by magnetic resonance imaging (MRI). Using the same gel solution, breast simulators (phantoms) were shaped and absorbed dose distributions were imaged by MRI at the Nuclear Resonance Laboratory of the Physics Institute of Sao Paulo University. (C) 2007 Elsevier Ltd. All rights reserved.

Expression, purification and kinetic characterization of His-tagged glyceraldehyde-3-phosphate dehydrogenase from Trypanosoma cruzi

Trypanosomes are flagellated protozoa responsible for serious parasitic diseases that have been classified by the World Health Organization as tropical sicknesses of major importance. One important drug target receiving considerable attention is the enzyme glyceraldehyde-3-phosphate dehydrogenase from the protozoan parasite Trypanosoma cruzi, the causative agent of Chagas disease (T. cruzi Glyceraldehyde-3-phosphate dehydrogenase (TcGAPDH); EC 1.2.1.12). TcGAPDH is a key enzyme in the glycolytic pathway of T. cruzi and catalyzes the oxidative phosphorylation of D-glyceraldehyde-3-phosphate (G3P) to 1,3-bisphosphoglycerate (1,3-BPG) coupled to the reduction of oxidized nicotinamide adenine dinucleotide, (NAD(+)) to NADH, the reduced form. Herein, we describe the cloning of the T. cruzi gene for TcGAPDH into the pET-28a(+) vector, its expression as a tagged protein in Escherichia coli, purification and kinetic characterization. The His(6)-tagged TcGAPDH was purified by affinity chromatography. Enzyme activity assays for the recombinant His(6)-TcGAPDH were carried out spectrophotometrically to determine the kinetic parameters. The apparent Michaelis-Menten constant (K(M)(app)) determined for D-glyceraldehyde-3-phosphate and NAD(+) were 352 +/- 21 and 272 +/- 25 mu M, respectively, which were consistent with the values for the untagged enzyme reported in the literature. We have demonstrated by the use of Isothermal Titration Calorimetry (ITC) that this vector modification resulted in activity preserved for a higher period. We also report here the use of response surface methodology (RSM) to determine the region of optimal conditions for enzyme activity. A quadratic model was developed by RSM to describe the enzyme activity in terms of pH and temperature as independent variables. According to the RMS contour plots and variance analysis, the maximum enzyme activity was at 29.1 degrees C and pH 8.6. Above 37 degrees C, the enzyme activity starts to fall, which may be related to previous reports that the quaternary structure begins a process of disassembly. (C) 2010 Elsevier Inc. All rights reserved.

Clinical and MRI investigation of temporomandibular joint in major depressed patients

Objective: The aim of the present study was to describe the clinical and MRI findings of the temporomandibular joint (TMJ) in patients with major depressive disorders (MDDs) of the non-psychotic type.Methods: 40 patients (80 TMJs) who were diagnosed as having MDDs were selected for this study. The clinical examination of the TMJs was conducted according to the research diagnostic criteria and temporomandibular disorders (TMDs). The MRIs were obtained bilaterally in each patient with axial, parasagittal and paracoronal sections within a real-time dynamic sequence. Two trained oral radiologists assessed all images. For statistical analyses, Fisher's exact test and chi(2) test were applied (alpha = 0.05).Results: Migraine was reported in 52.5% of subjects. Considering disc position, statistically significant differences between opening patterns with and without alteration (p = 0.00) and between present and absent joint noises (p = 0.00) were found. Regarding muscular pain, patients with and without abnormalities in disc function and patients with and without abnormalities in disc position were not statistically significant (p = 0.42 and p = 0.40, respectively). Significant differences between mandibular pathway with and without abnormalities (p=0.00) and between present and absent joint noises (p=0.00) were observed.Conclusion: Based on the preliminary results observed by clinical and MRI examination of the TMJ, no direct relationship could be determined between MDDs and TMDs. Dentomaxillofacial Radiology (2012) 41, 316-322. doi: 10.1259/dmfr/27328352

Measurement of B-s(0) Mixing Parameters from the Flavor-Tagged Decay B-s(0)-> J/psi phi

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Measurement of the CP-violating phase phi sJ/psi phi using the flavor-tagged decay Bs(0) -> J/psi phi in 8 fb(-1) of p(p)over-bar collisions

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)