HLA-DQ8 as an Ir gene in coeliac disease

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.

Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy

In this work we propose a method to extract shape-based features from endoscopic images for an automated classification of colonic polyps. This method is based on the density of pits as used in the pit pattern classification scheme which is commonly used for the classification of colonic polyps. For the detection of pits we employ a noise-robust variant of the LBP operator. To be able to be robust against local texture variations we extend this operator by an adaptive thresholding. Based on the detected pit candidates we compute a Delaunay triangulation and use the edge lengths of the resulting triangles to construct histograms. These are then used in conjunction with the k-NN classifier to classify images. We show that, compared to a previously developed method, we are not only able to almost always get higher classification results in our application scenario, but that the proposed method is also able to significantly outperform the previously developed method in terms of the computational demand.

Future perspective for diagnosis in autoimmune diseases

Human beings have taken successive approaches for the understanding and management of diseases. Initially brewed in supernatural concepts and mystical procedures, a vigorous scientific approach has emerged on the grounds of fundamental disciplines such as anatomy, microbiology, biochemistry, physiology, immunology, pathology, and pharmacology. The resulting integrated knowledge contributed to the current classification of diseases and the way Medicine is carried out today. Despite considerable progress, this approach is rather insufficient when it comes to systemic inflammatory conditions, such as systemic lupus erythematosus, that covers clinical conditions ranging from mild pauci-symptomatic diseases to rapidly fatal conditions. The treatment for such conditions is often insufficient and novel approaches are needed for further progress in these areas of Medicine. A recent breakthrough has been achieved with respect to chronic auto-inflammatory syndromes, in which molecular dissection of underlying gene defects has provided directions for target-oriented therapy. Such approach may be amenable to application in systemic auto-immune diseases with the comprehension that such conditions may be the consequence of interaction of specific environmental stimuli and an array of several and interconnected gene polymorphisms. On the bulk of this transformation, the application of principles of pharmacogenetics may lead the way towards a progressively stronger personalized Medicine.

Sensitive detection of cereal fractions that are toxic to celiac disease patients by using monoclonal antibodies to a main immunogenic wheat peptide

BACKGROUND

Celiac disease is an immune-mediated enteropathy caused by the ingestion of gluten, a protein fraction found in certain cereals. Immunotoxic gluten peptides that are recalcitrant to degradation of digestive enzymes appear to trigger celiac syndromes. A 33-mer peptide from alpha-2 gliadin has been identified as a principal contributor to gluten immunotoxicity. A gluten-free diet is the usual first therapy for celiac disease patients; therefore, the characterization and quantification of the toxic portion of the gluten in foodstuffs is crucial to avoid celiac damage.

OBJECTIVE

We aimed to develop immunologic assays as a novel food analysis tool for measuring cereal fractions that are immunotoxic to celiac disease patients.

DESIGN

The design focused on the production of monoclonal antibodies against the gliadin 33-mer peptide and the development of enzyme-linked immunosorbent assays (ELISAs) and Western blot analysis with the use of novel antibodies.

RESULTS

A sandwich ELISA method showed a detection limit for wheat, barley, and rye of <1 ppm prolamine. However, the method required a sample that was > or =1 order of magnitude greater for the detection of low-toxic oats, and there was no signal with the safe cereals maize and rice. A competitive ELISA method was also developed for detection of the toxic peptide in hydrolyzed food, which had a detection limit of <0.5 ppm gliadin.

CONCLUSIONS

Both ELISAs designed for use with the toxic gliadin 33-mer peptide suggested a high correlation between the presence of the peptide and the amount of cereal that was toxic to celiac disease patients. The sensitivity was significantly higher than that of equivalent methods recognizing other gluten epitopes.