Nutrient starvation activates ECM remodeling gene enhancers associated with inflammatory bowel disease risk in fibroblasts.

Nutrient deprivation induces a reversible cell cycle arrest state termed quiescence, which often accompanies transcriptional silencing and chromatin compaction. Paradoxically, nutrient deprivation is associated with activated fibroblast states in pathological microenvironments in which fibroblasts drive extracellular matrix (ECM) remodeling to alter tissue environments. The relationship between nutrient deprivation and fibroblast activation remains unclear.

Using Deep Learning to Automate Eosinophil Counting in Pediatric Ulcerative Colitis Histopathological Images.

Background: Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases.

Accessible chromatin maps of inflammatory bowel disease intestine nominate cell-type mediators of genetic disease risk.

Inflammatory Bowel Disease ( ) is a chronic and often debilitating autoinflammatory condition, with an increasing incidence in children. Standard-of-care therapies lead to sustained transmural healing and clinical remission in fewer than one-third of patients. For children, TNFα inhibition remains the only FDA-approved biologic therapy, providing an even greater urgency to understanding mechanisms of response.

Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review.

Background: There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies.

Methods: We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022.

Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches.

Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms.

Outcomes Following Acute Severe Colitis at Initial Presentation: A Multi-centre, Prospective, Paediatric Cohort Study.

Aim: To assess contemporary outcomes in children with acute severe ulcerative colitis [ASUC] at initial presentation.

Methods: Between April 2014 and January 2019, children aged <17 years, with new onset ASUC (Paediatric Ulcerative Colitis Activity Index [PUCAI ≥65) were prospectively followed in a Canadian inception cohort study. 16S rRNA amplicon sequencing captured microbial composition of baseline faecal samples.

Racial Disparities in Pediatric Inflammatory Bowel Disease Care: Differences in Outcomes and Health Service Utilization Between Black and White Children.

Objective: To describe racial inequities in pediatric inflammatory bowel disease care and explore potential drivers.

Methods: We undertook a single-center, comparative cohort study of newly diagnosed Black and non-Hispanic White patients with inflammatory bowel disease, aged <21 years, from January 2013 through 2020. Primary outcome was corticosteroid-free remission (CSFR) at 1 year.

Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications.

The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology.

STR-based feature extraction and selection for genetic feature discovery in neurological disease genes.

Gene expression, often determined by single nucleotide polymorphisms, short repeated sequences known as short tandem repeats (STRs), structural variants, and environmental factors, provides means for an organism to produce gene products necessary to live. Variation in expression levels, sometimes known as enrichment patterns, has been associated with disease progression. Thus, the STR enrichment patterns have recently gained interest as potential genetic markers for disease progression.

Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach.

Background: The malaria risk prediction is currently limited to using advanced statistical methods, such as time series and cluster analysis on epidemiological data. Nevertheless, machine learning models have been explored to study the complexity of malaria through blood smear images and environmental data. However, to the best of our knowledge, no study analyses the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria using a machine learning model.

Leveraging Mann-Whitney U test on large-scale genetic variation data for analysing malaria genetic markers.

Background: The malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia.

Prioritising positively selected variants in whole-genome sequencing data using FineMAV.

Background: In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project.

Incidence of Inflammatory Bowel Disease in South Asian and Chinese People: A Population-Based Cohort Study from Ontario, Canada.

Background: Inflammatory bowel disease (IBD) is now a global disease with incidence increasing throughout Asia.

Aim: To determine the incidence of IBD among South Asians and Chinese people residing in Ontario, Canada's most populous province.

Methods: All incident cases of IBD in children (1994-2015) and adults (1999-2015) were identified from population-based health administrative data.

One-year outcomes with ustekinumab therapy in infliximab-refractory paediatric ulcerative colitis: a multicentre prospective study.

Background: The phase 3 (UNIFI) trial of ustekinumab (anti-interleukin 12/23) demonstrated efficacy even after prior biologic failure in adult ulcerative colitis (UC), but paediatric data are lacking.

Aim: To prospectively monitor efficacy and serum concentrations of ustekinumab given to children with UC refractory to other biologics.

Methods: Children with anti-TNF refractory UC initiating ustekinumab intravenously at sites of the Canadian Children IBD Network prior to 12/2019 are included.

Predicting Therapeutic Response in Pediatric Ulcerative Colitis-A Journey Towards Precision Medicine.

Ulcerative colitis (UC) is a disabling disease, characterized by chronic inflammation of the colon, with a rising prevalence worldwide in the pediatric age group. Although UC presents in children with varying severity, disease extent, and comorbidities, initial treatment is essentially uniform, consisting of 5-aminosalicylate drugs with corticosteroid induction for those with moderately to severely active disease. With the advent of anti-tumor necrosis factor (TNF) biologic therapy and several new biologics and small-molecule drugs for UC, precision medicine approaches to treatment are needed to more rapidly achieve sustained remission, restore quality of life, normalize development, and limit exposure to toxic corticosteroids in children with UC.

Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier.

Background: The pediatric inflammatory bowel disease (PIBD) classes algorithm was developed to bring consistency to labelling of colonic IBD, but labels are exclusively based on features atypical for ulcerative colitis (UC).

Aim: The aim of the study was to develop an algorithm and identify features that discriminate between pediatric UC and colonic Crohn disease (CD).

Methods: Baseline clinical, endoscopic, radiologic, and histologic data, including the PIBD class features in 74 colonic IBD (56: UC, 18: colonic CD) patients were collected.

Selected neuropeptide genes show genetic differentiation between Africans and non-Africans.

Background: Publicly available genome data provides valuable information on the genetic variation patterns across different modern human populations. Neuropeptide genes are crucial to the nervous, immune, endocrine system, and physiological homeostasis as they play an essential role in communicating information in neuronal functions. It remains unclear how evolutionary forces, such as natural selection and random genetic drift, have affected neuropeptide genes among human populations.

Body Composition Using Air Displacement Plethysmography in Children With Inflammatory Bowel Disease.

Objective: The aim of the study was to assess the body composition of children with inflammatory bowel disease (IBD) and to study the accuracy of clinically available tools in predicting excess body fatness. We aimed at also exploring the influence of adiposity on pharmacokinetics during early Infliximab exposure.

Methods: Prospective cohort study in 5- to 17-year-old children with IBD initiating Infliximab therapy.

Differentiation of Colonic Inflammatory Bowel Disease: Re-examination of Paediatric Inflammatory Bowel Disease Classes Algorithm With Resected Colon As the Criterion Standard.

Objectives: Differentiation of Crohn disease (CD) from ulcerative colitis (UC) is challenging when inflammation is predominantly colonic. The paediatric inflammatory bowel disease (PIBD) classes algorithm was developed to bring consistency to labelling, but used physician-assigned diagnosis as the criterion standard. We aimed to reassess the PIBD classes using pathology of subsequently resected colon as the criterion standard.

Lean non-alcoholic fatty liver disease.

Background & Aims: Non-alcoholic fatty liver disease (NAFLD), with its increasing prevalence and association with various co-morbidities, such as diabetes, cardiovascular disease and metabolic syndrome, is a growing concern. Previously thought to predominantly affect obese individuals, NAFLD has been shown to occur in non-obese subjects. This subset of individuals, known to have 'lean NAFLD' or 'non-obese NAFLD', is also growing increasingly prevalent.

Clinical Outcomes With Therapeutic Drug Monitoring in Inflammatory Bowel Disease: A Systematic Review With Meta-Analysis.

Background And Aims: We undertook a systematic review and meta-analysis examining the effectiveness of therapeutic drug monitoring [TDM] to improve clinical outcomes in inflammatory bowel disease patients treated with anti-tumour necrosis factor alpha [anti-TNF] drugs.

Methods: We searched MEDLINE, Epub Ahead of Print, EMBASE and Cochrane up to October 2017 for randomized trials [RCTs] and cohort studies comparing proactive or reactive TDM to each other or empiric care. Outcomes included clinical remission [primary], clinical relapse, endoscopic remission, anti-TNF response durability, cost and adverse events [secondary].