Association of Anti-glycan Antibodies and Inflammatory Bowel Disease Course.

BACKGROUND AND AIMS: The usefulness of anti-glycan antibodies alone or combined with anti-Saccharomyces cerevisiae [ASCA] or perinuclear antineutrophil cytoplasmic [pANCA] antibodies for diagnosis of inflammatory bowel disease [IBD], differentiation between Crohn's disease [CD] and ulcerative colitis [UC], disease stratification including IBD phenotype, and also for determination of the course of the disease, remain unclear. METHODS: A large panel of serological anti-glycan carbohydrate antibodies, including anti-mannobioside IgG antibodies [AMCA], anti-chitobioside IgA [ACCA], anti-laminaribioside IgG antibodies [ALCA], anti-laminarin [anti-L] and anti-chitine [anti-C] were measured in the serum from a cohort of 195 patients with IBD] [107 CD and 88 UC]. The respective accuracy of isolated or combined markers for diagnosis, disease differentiation, stratification disease phenotype, and severity of the disease course, defined by a wide panel of criteria obtained from the past medical history, was assessed. RESULTS: The positivity of at least one anti-glycan antibody was detected in a significant higher proportion of CD and UC compared with healthy controls [p < 0.0001 and p < 0.0007, respectively]. Whereas ASCA and ANCA antibody status had the highest efficacy to be associated with CD in comparison with UC (area under receiver operating characteristic curve [AUROC] = 0.70 for each], the adjunction of anti-laminarin antibody substantially improved the differentiation between CD and UC [AUROC = 0.77]. Titres of ACCA [> 51U/ml] and anti-laminarin [> 31U/ml] were significantly linked with a higher association with steroid dependency (odds ratio [OR] =2.0 [1.0-4.0], p = 0.03 and OR = 2.4 [1.1-5.2], p = 0.02, respectively]. We further defined the respective performance of anti-glycan antibodies to discriminate between patients with severe or not severe CD and UC course and determined the associated optimal cut-off values: severe CD course was significantly more likely in case of AMCA > 77U/ml [OR = 4.3; p = 0.002], ASCA > 63U/ml [OR = 3.5; p < 0.009] and at a lesser degree ACCA > 50U/ml [OR = 2.8; p < 0.02] and severe UC course was significantly associated with AMCA > 52U/ml [OR = 3.4; p = 0.04] and ACCA > 25U/ml [OR = 3.0; p < 0.04]. CONCLUSIONS: Anti-glycan antibodies are valuable serological markers, especially AMCA antibodies that may help clinicians to promptly classify patients into high risk for severe disease.

GALEN: a third generation terminology tool to support a multipurpose national coding system for surgical procedures.

Generalised architecture for languages, encyclopedia and nomenclatures in medicine (GALEN) has developed a new generation of terminology tools based on a language independent model describing the semantics and allowing computer processing and multiple reuses as well as natural language understanding systems applications to facilitate the sharing and maintaining of consistent medical knowledge. During the European Union 4 Th. framework program project GALEN-IN-USE and later on within two contracts with the national health authorities we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures named CCAM in a minority language country, France. On one hand, we contributed to a language independent knowledge repository and multilingual semantic dictionaries for multicultural Europe. On the other hand, we support the traditional process for creating a new coding system in medicine which is very much labour consuming by artificial intelligence tools using a medically oriented recursive ontology and natural language processing. We used an integrated software named CLAW (for classification workbench) to process French professional medical language rubrics produced by the national colleges of surgeons domain experts into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation, on one hand, we generate with the LNAT natural language generator controlled French natural language to support the finalization of the linguistic labels (first generation) in relation with the meanings of the conceptual system structure. On the other hand, the Claw classification manager proves to be very powerful to retrieve the initial domain experts rubrics list with different categories of concepts (second generation) within a semantic structured representation (third generation) bridge to the electronic patient record detailed terminology.

Galen: a third generation terminology tool to support a multipurpose national coding system for surgical procedures.

GALEN has developed a new generation of terminology tools based on a language independent concept reference model using a compositional formalism allowing computer processing and multiple reuses. During the 4th framework program project Galen-In-Use we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures (CCAM) in France. On one hand we contributed to a language independent knowledge repository for multicultural Europe. On the other hand we support the traditional process for creating a new coding system in medicine which is very much labour consuming by artificial intelligence tools using a medically oriented recursive ontology and natural language processing. We used an integrated software named CLAW to process French professional medical language rubrics produced by the national colleges of surgeons into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation on one hand we generate controlled French natural language to support the finalization of the linguistic labels in relation with the meanings of the conceptual system structure. On the other hand the classification manager of third generation proves to be very powerful to retrieve the initial professional rubrics with different categories of concepts within a semantic network.