An antibody-based ELISA for quantification of Ani s 1, a major allergen from Anisakis simplex.

Anisakis simplex is a nematode parasite that can infect humans who have eaten raw or undercooked seafood. Larvae invading the gastrointestinal mucosa excrete/secrete proteins that are implicated in the pathogenesis of anisakiasis and can induce IgE-mediated symptoms. Since Ani s 1 is a potent secreted allergen with important clinical relevance, its measurement could assess the quality of allergenic products used in diagnosis/immunotherapy of Anisakis allergy and track the presence of A. simplex parasites in fish foodstuffs. An antibody-based ELISA for quantification of Ani s 1 has been developed based on monoclonal antibody 4F2 as capture antibody and biotin-labelled polyclonal antibodies against Ani s 1 as detection reagent. The dose-response standard curves, obtained with natural and recombinant antigens, ranged from 4 to 2000 ng/ml and were identical and parallel to that of the A. simplex extract. The linear portion of the dose-response curve with nAni s 1 was between 15 and 250 ng/ml with inter-assay and intra-assays coefficients of variation less than 20% and 10%, respectively. The assay was specific since there was no cross-reaction with other extracts (except Ascaris extracts) and was highly sensitive (detection limit of 1·8 ng/ml), being able to detect Ani s 1 in fish extracts from codfish and monkfish.

DAS-28-based EULAR response and HAQ improvement in rheumatoid arthritis patients switching between TNF antagonists

INTRODUCTION No definitive data are available regarding the value of switching to an alternative TNF antagonist in rheumatoid arthritis patients who fail to respond to the first one. The aim of this study was to evaluate treatment response in a clinical setting based on HAQ improvement and EULAR response criteria in RA patients who were switched to a second or a third TNF antagonist due to failure with the first one. METHODS This was an observational, prospective study of a cohort of 417 RA patients treated with TNF antagonists in three university hospitals in Spain between January 1999 and December 2005. A database was created at the participating centres, with well-defined operational instructions. The main outcome variables were analyzed using parametric or non-parametric tests depending on the level of measurement and distribution of each variable. RESULTS Mean (+/- SD) DAS-28 on starting the first, second and third TNF antagonist was 5.9 (+/- 2.0), 5.1 (+/- 1.5) and 6.1 (+/- 1.1). At the end of follow-up, it decreased to 3.3 (+/- 1.6; Delta = -2.6; p > 0.0001), 4.2 (+/- 1.5; Delta = -1.1; p = 0.0001) and 5.4 (+/- 1.7; Delta = -0.7; p = 0.06). For the first TNF antagonist, DAS-28-based EULAR response level was good in 42% and moderate in 33% of patients. The second TNF antagonist yielded a good response in 20% and no response in 53% of patients, while the third one yielded a good response in 28% and no response in 72%. Mean baseline HAQ on starting the first, second and third TNF antagonist was 1.61, 1.52 and 1.87, respectively. At the end of follow-up, it decreased to 1.12 (Delta = -0.49; p < 0.0001), 1.31 (Delta = -0.21, p = 0.004) and 1.75 (Delta = -0.12; p = 0.1), respectively. Sixty four percent of patients had a clinically important improvement in HAQ (defined as > or = -0.22) with the first TNF antagonist and 46% with the second. CONCLUSION A clinically significant effect size was seen in less than half of RA patients cycling to a second TNF antagonist.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).