Prediction of tolerance in children with IgE mediated cow's milk allergy by microarray profiling and chemometric approach.

The sera of a retrospective cohort (n=41) composed of children with well characterized cow's milk allergy collected from multiple visits were analyzed using a protein microarray system measuring four classes of immunoglobulins. The frequency of the visits, age and gender distribution reflected real situation faced by the clinicians at a pediatric reference center for food allergy in São Paulo, Brazil. The profiling array results have shown that total IgG and IgA share similar specificity whilst IgM and in particular IgE are distantly related. The correlation of specificity of IgE and IgA is variable amongst the patients and this relationship cannot be used to predict atopy or the onset of tolerance to milk. The array profiling technique has corroborated the clinical selection criteria for this cohort albeit it clearly suggested that 4 out of the 41 patients might have allergies other than milk origin. There was also a good correlation between the array data and ImmunoCAP results, casein in particular. By using qualitative and quantitative multivariate analysis routines it was possible to produce validated statistical models to predict with reasonable accuracy the onset of tolerance to milk proteins. If expanded to larger study groups, the array profiling in combination with the multivariate techniques show potential to improve the prognostic of milk allergic patients.

Multiple protein extract microarray for profiling human food-specific immunoglobulins A, M, G and E.

Existing food immunoglobulin (Ig) tests require large volumes of serum, are limited to one immunoglobulin class, are not amenable to high throughput analysis and only give a limited picture of the immunological response to food antigens. Conversely a new generation of Component Resolved Diagnostic systems using pure proteins is highly specific and totally dependent on the availability of the protein in its recombinant or natural origin form. Here we demonstrate a proof-of-concept of a microarray test based on protein extracts of food components. Our approach relies on innovations on three different fronts: the novelty of using arrayed food samples sequentially extracted with detergent and chaotropic agents, the ability to measure four different Ig classes simultaneously and the ability to analyse the generated data via a suitable bioinformatics/statistical analysis interface. This approach combines high numerical power of microarrays with automation, high throughput analysis and enables detailed investigation of the Ig profiles to food antigens. The prototype shown contains extracts of approximately 350 food ingredients that cover most of the food products found in the UK. Here we showed that the use of a sequential extraction technique to solubilise and then denature food samples has its benefits in the assessment of variations in antigenicity when tested with human sera. A patient dependent degree of class specificity was observed with human sera (IgG specificity correlates well with IgA>IgM>>>>>IgE). Besides generating a simultaneous profile for IgA, IgM, IgG and IgE the array system has shown good discrimination between challenge responders in atopic and non-atopic individuals. Poly- and mono-specific IgE responders were easily identified. The mathematical modelling of specific IgE content showed good correlations when compared with established IgE antibody testing assay (UniCAP). Although in its proof-of-principle stages, the immune profiling technique described here has the potential to provide unique insights into exposure/sensitization and establish relationships between specific immunoglobulin classes and subclasses against food protein antigens. In further developments, the immune profiling technique could also be extended to other related areas such as parasite and bacterial gut infection. Full analyses of large longitudinal and retrospective clinical trials are on going to determine the positive and negative predictive values of the technique.

Effect of bolus size on chewing, swallowing, oral soft tissue and tongue movement.

Simultaneous 3D articulograph and ultrasonograph techniques were used to monitor the chewing, swallowing, oral soft tissue and tongue movements of six subjects whilst consuming varying numbers (1, 2 and 4) of a confectionery product. Modifying the number of confectionery units had a variable effect on chewing, swallowing and oral soft tissue movements during the start of the chewing sequence. The distance, range and velocity of chin movement were significantly lower when 1 confectionery unit was consumed compared with 2 and 4 during the middle stage of the chewing sequence. Significant differences in modioli movement were observed during the initial stages of chewing allowing the identification of a working and non-working side, whilst no differences in thyroid cartilage movement were noted over the whole chewing sequence. Increasing the number of confectionery units caused a significant increase in the index of tongue movement during the end of the eating sequence, although the mean index of tongue movement over the total chewing sequence remained constant. Jaw movement correlated with tongue movement, where for all samples, gape decreased linearly as the chewing sequence progressed, reflected in a similar decline in the index of tongue movement. The lack of differences in the index of tongue movement observed over the start and middle of the chewing sequence as a consequence of the number of confectionery units consumed suggested that the change in the physical properties of the bolus had a greater effect on the index of tongue movement than the change in bolus size.

Correction of temperature-induced spectral variation by continuous piecewise direct standardization

In process analytical applications it is not always possible to keep the measurement conditions constant. However, fluctuations in external variables such as temperature can have a strong influence on measurement results. For example, nonlinear temperature effects on near-infrared (NIR) spectra may lead to a strongly biased prediction result from multivariate calibration models such as PLS. A new method, called Continuous Piecewise Direct Standardization (CPDS) has been developed for the correction of such external influences. It represents a generalization of the discrete PDS calibration transfer method and is able to adjust for continuous nonlinear influences such as the temperature effects on spectra. It was applied to shortwave NIR spectra of ethanol/water/2-propanol mixtures measured at different temperatures in the range 30-70 degrees C. The method was able to remove, almost completely, the temperature effects on the spectra, and prediction of the mole fractions of the chemical components was close to the results obtained at constant temperature.

Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models.

Temperature, pressure, viscosity, and other process variables fluctuate during an industrial process. When vibrational spectra are measured on- or in-line for process analytical and control purposes, the fluctuations influence the shape of the spectra in a nonlinear manner. The influence of these temperature-induced spectral variations on the predictive ability of multivariate calibration model is assessed. Short-wave NIR spectra of ethanol/water/2-propanol mixtures are taken at different temperatures, and different local and global partial least-squares calibration strategies are applied. The resulting prediction errors and sensitivity vectors of a test set are compared. For data with no temperature variation, the local models perform best with high sensitivity but the knowledge of the temperature for prediction measurements cannot aid in the improvement of local model predictions when temperature variation is introduced. The prediction errors of global models are considerably lower when temperature variation is present in the data set but at the expense of sensitivity. To be able to build temperature-stable calibration models with high sensitivity, a way of explicitly modeling the temperature should be found.