Canadian Association of Gastroenterology Clinical Practice Guideline for the Medical Management of Pediatric Luminal Crohn's Disease

Background & Aims: We aim to provide guidance for medical treatment of luminal Crohn's disease in children. Methods: We performed a systematic search of publication databases to identify studies of medical management of pediatric Crohn's disease. Quality of evidence and strength of recommendations were rated according to the GRADE (Grading of Recommendation Assessment, Development, and Evaluation) approach. We developed statements through an iterative online platform and then finalized and voted on them. Results: The consensus includes 25 statements focused on medical treatment options. Consensus was not reached, and no recommendations were made, for 14 additional statements, largely due to lack of evidence. The group suggested corticosteroid therapies (including budesonide for mild to moderate disease). The group suggested exclusive enteral nutrition for induction therapy and biologic tumor necrosis factor antagonists for induction and maintenance therapy at diagnosis or at early stages of severe disease, and for patients failed by steroid and immunosuppressant induction therapies. The group recommended against the use of oral 5-aminosalicylate for induction or maintenance therapy in patients with moderate disease, and recommended against thiopurines for induction therapy, corticosteroids for maintenance therapy, and cannabis in any role. The group was unable to clearly define the role of concomitant immunosuppressants during initiation therapy with a biologic agent, although thiopurine combinations are not recommended for male patients. No consensus was reached on the role of aminosalicylates in treatment of patients with mild disease, antibiotics or vedolizumab for induction or maintenance therapy, or methotrexate for induction therapy. Patients in clinical remission who are receiving immunomodulators should be assessed for mucosal healing within 1 year of treatment initiation. Conclusions: Evidence-based medical treatment of Crohn's disease in children is recommended, with thorough ongoing assessments to define treatment success

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Sur les spectres de bandes du carbone dans l'arc électrique; Réponse à une note de MM. Kayser et Runge

Pas de Résumé disponibl

Un systeme a base de connaissances pour l'aide a la maitrise de la qualite des produits fabriques

Available from INIST (FR), Document Supply Service, under shelf-number : AR 16105 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEMinistere de l'Enseignement Superieur et de la Recherche, 75 - Paris (France)FRFranc