Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Mesenchymal stem cells (MSCs) hold enormous potential for cell-based therapy in the treatment of various diseases, particularly those which currently cannot be cured and result in poor outcomes or invasive surgery. Here we present results of the application of autologous, culture-expanded, adipose tissue (AT)-derived MSCs for the osteoarthritis (OA) treatment of 10 canine patients. The stemness of isolated cells has been confirmed by their ability to differentiate into osteo- and chondrocytic lineages. The clinical effect of a single injection of ATMSCs into the symptomatic joints was evaluated by a veterinarian for five disabilities characteristic of OA at 30, 60 and 90 days after treatment, which has been designated as the initial evaluation period. Functional outcomes for all analysed characteristics improved significantly at the end of this evaluation compared with the baseline. In addition, for 5 of these 10 patients, an extended follow-up study was performed from 1 to 4 years after the initial evaluation period. We detected long-lasting positive effects on two out of five analysed characteristics. The results demonstrate that the use of autologous AT-MSCs is a successful approach to canine OA therapy.

Celiac disease is a complex chronic immune-mediated disorder of the small intestine. Today, the pathobiology of the disease is unclear, perplexing differential diagnosis, patient stratification, and decision-making in the clinic. Herein, we adopted a next-generation sequencing approach in a celiac disease trio of Greek descent to identify all genomic variants with the potential of celiac disease predisposition. Analysis revealed six genomic variants of prime interest: SLC9A4 c.1919G>A, KIAA1109 c.2933T>C and c.4268_4269delCCinsTA, HoxB6 c.668C>A, HoxD12 c.418G>A, and NCK2 c.745_746delAAinsG, from which NCK2 c.745_746delAAinsG is novel. Data validation in pediatric celiac disease patients of Greek (n = 109) and Serbian (n = 73) descent and their healthy counterparts (n = 111 and n = 32, respectively) indicated that HoxD12 c.418G>A is more prevalent in celiac disease patients in the Serbian population (P < 0.01), while NCK2 c.745_746delAAinsG is less prevalent in celiac disease patients rather than healthy individuals of Greek descent (P = 0.03). SLC9A4 c.1919G>A and KIAA1109 c.2933T>C and c.4268_4269delCCinsTA were more abundant in patients; nevertheless, they failed to show statistical significance. The next-generation sequencing-based family genomics approach described herein may serve as a paradigm towards the identification of novel functional variants with the aim of understanding complex disease pathobiology.