Disorder-specific functional abnormalities during temporal discounting in youth with Attention Deficit Hyperactivity Disorder (ADHD), Autism and comorbid ADHD and Autism.

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are often comorbid and share cognitive abnormalities in temporal foresight. A key question is whether shared cognitive phenotypes are based on common or different underlying pathophysiologies and whether comorbid patients have additive neurofunctional deficits, resemble one of the disorders or have a different pathophysiology. We compared age- and IQ-matched boys with non-comorbid ADHD (18), non-comorbid ASD (15), comorbid ADHD and ASD (13) and healthy controls (18) using functional magnetic resonance imaging (fMRI) during a temporal discounting task. Only the ASD and the comorbid groups discounted delayed rewards more steeply. The fMRI data showed both shared and disorder-specific abnormalities in the three groups relative to controls in their brain-behaviour associations. The comorbid group showed both unique and more severe brain-discounting associations than controls and the non-comorbid patient groups in temporal discounting areas of ventromedial and lateral prefrontal cortex, ventral striatum and anterior cingulate, suggesting that comorbidity is neither an endophenocopy of the two pure disorders nor an additive pathology.

Validation and comparison of a sandwich ELISA, two competitive ELISAs and a real-time PCR method for the detection of lupine in food.

Methods applied in food allergen analysis should be specific, sensitive and applicable to both raw and highly processed foods. The performance of the most commonly used methods, ELISA and real-time PCR, may, however, be influenced by food processing steps, e.g., heat treatment. The present study compares the applicability of four in-house developed methods, one sandwich ELISA, two competitive ELISAs and a real-time PCR method, for the detection of lupine in four different food matrices, comprising bread, biscuits, rice patties and noodles. In order to investigate the influence of food processing on the detectability, not only the heat treated model foods but also the corresponding doughs were analysed. The sandwich ELISA proved to be the most sensitive method. The LOD was found to be 10 ppm lupine, independent from the food matrix and independent if the dough or the heat treated food was analysed. In addition, the methods were applied to the analysis of commercial foodstuffs differing in their labelling.

Automated electronic tongue based on potentiometric sensors for the determination of a trinary anionic surfactant mixture.

An automated electronic tongue consisting of an array of potentiometric sensors and an artificial neural network (ANN) has been developed to resolve mixtures of anionic surfactants. The sensor array was formed by five different flow-through sensors for anionic surfactants, based on poly(vinyl chloride) membranes having cross-sensitivity features. Feedforward multilayer neural networks were used to predict surfactant concentrations. As a great amount of information is required for the correct modelling of the sensors response, a sequential injection analysis (SIA) system was used to automatically provide it. Dodecylsulfate (DS(-)), dodecylbenzenesulfonate (DBS(-)) and alpha-alkene sulfonate (ALF(-)) formed the three-analyte study case resolved in this work. Their concentrations varied from 0.2 to 4mM for ALF(-) and DBS(-) and from 0.2 to 5mM for DS(-). Good prediction ability was obtained with correlation coefficients better than 0.933 when the obtained values were compared with those expected for a set of 16 external test samples not used for training.