Inflammatory bowel disease (IBD) includes ulcerative colitis (UC) and Crohn’s disease (CD). These are autoimmune diseases of the gastrointestinal tract with a chronic relapsing and remitting course. Due to complex interactions between multiple factors in the etiology of IBD, the discovery of new predictors of disease course and response to therapy, and the development of effective therapies is a significant challenge. The dysregulation of microRNAs (miRNAs), a class of conserved endogenous, small non-coding RNA molecules with a length of 18–25 nucleotides, that regulate gene expression by an RNA interference process, is implicated in the complex pathogenetic context of IBD. Both tissue-derived, circulating, and fecal microRNAs have been explored as promising biomarkers in the diagnosis and the prognosis of disease severity of IBD. In this review, we summarize the expressed miRNA profile in blood, mucosal tissue, and stool and highlight the role of miRNAs as biomarkers with potential diagnostic and therapeutic applications in ulcerative colitis and Crohn’s disease. Moreover, we discuss the new perspectives in developing a new screening model for the detection of colorectal cancer (CRC) based on fecal miRNAs.

The advent of biologic drugs has revolutionized the treatment of Inflammatory Bowel Disease, increasing rates of response and mucosal healing in comparison to conventional therapies by allowing the treatment of corticosteroid-refractory cases and reducing corticosteroid-related side effects. However, biologic therapies (anti-TNFα inhibitors, anti-α4β7 integrin and anti-IL12/23) are still burdened by rates of response that hover around 40% (in biologic-naïve patients) or lower (for biologic-experienced patients). Moreover, knowledge of the mechanisms underlying drug resistance or loss of response is still scarce. Several cellular and molecular determinants are implied in therapeutic failure; genetic predispositions, in the form of single nucleotide polymorphisms in the sequence of cytokines or Human Leukocyte Antigen, or an altered expression of cytokines and other molecules involved in the inflammation cascade, play the most important role. Accessory mechanisms include gut microbiota dysregulation. In this narrative review of the current and most recent literature, we shed light on the mentioned determinants of therapeutic failure in order to pave the way for a more personalized approach that could help avoid unnecessary treatments and toxicities.

Background and Aims: Achieving histological remission is a desirable emerging treatment target in ulcerative colitis (UC), yet its assessment is challenging due to high inter- and intraobserver variability, reliance on experts, and lack of standardization. Artificial intelligence (AI) holds promise in addressing these issues. This systematic review, meta-analysis, and meta-regression evaluated the AI’s performance in assessing histological remission and compared it with that of pathologists. Methods: We searched Medline/PubMed and Scopus databases from inception to September 2024. We included studies on AI models assessing histological activity in UC, with or without comparison to pathologists. Pooled performance metrics were calculated: sensitivity, specificity, positive and negative predictive value (PPV and NPV), observed agreement, and F1 score. A pairwise meta-analysis compared AI and pathologists, while sub-meta-analysis and meta-regression evaluated heterogeneity and factors influencing AI performance. Results: Twelve studies met the inclusion criteria. AI models exhibited strong performance with a pooled sensitivity of 0.84 (95% CI, 0.80–0.88), specificity 0.87 (0.84–0.91), PPV 0.90 (0.87–0.92), NPV 0.80 (0.71–0.88), observed agreement 0.85 (0.82–0.89), and F1 score 0.85 (0.82–0.89). AI models demonstrated no significant differences with pathologists for specificity, observed agreement, and F1 score, while they were outperformed by pathologists for sensitivity and NPV. AI models for the adult population were linked to reduced heterogeneity and enhanced AI performance at meta-regression. Conclusions: AI shows significant potential for assessing histological remission in UC and performs comparably to pathologists. Future research should focus on standardized, large-scale studies to minimize heterogeneity and support widespread AI implementation in clinical practice.

Background: Barrier healing is an emerging therapeutic target in inflammatory bowel disease (IBD), though its assessment remains challenging. We evaluated automated advanced imaging for real-time barrier assessment, its correlation with epithelial/vascular barrier markers, and ability to predict adverse outcomes. Methods: IBD patients and healthy controls undergoing endoscopic assessment were prospectively enrolled. The intestinal barrier was evaluated using ultra-high-magnification endocytoscopy and probe-based confocal laser endomicroscopy. Targeted biopsies were obtained from inflamed and non-inflamed segments. Epithelial and vascular barriers were assessed through automated multiplex immunofluorescence for Claudin-2, ZO-1, E-cadherin, PV-1, and CD31. Gene expression profiling was performed in epithelial and lamina propria compartments. Artificial intelligence (AI)-based analysis was employed for automated evaluation of barrier features captured by advanced imaging. Results: In total, 103 patients were included (38 ulcerative colitis [UC], 54 Crohn’s disease [CD], 11 healthy controls). Advanced imaging revealed barrier healing in 21% (8/38) of UC and 30% (16/54) of CD patients. In UC, Claudin-2 moderately correlated with abnormal crypt architecture (ρ = 0.49), goblet cell depletion (ρ = 0.5), and overall endocytoscopy activity (ρ = 0.49). In CD, PV-1 moderately correlated with altered blood flow (ρ = 0.41) and vessel architecture (ρ = 0.40). An integrated assessment of advanced imaging, combined with Claudin-2 and PV-1 expression, effectively predicted adverse outcomes in UC and CD, respectively. AI tools accurately classified epithelial and vascular barrier features captured by advanced imaging. Finally, gene expression confirmed upregulation of Claudin-2 and PV-1 in IBD. Conclusion: Automated advanced imaging enables real-time barrier assessment in IBD and correlates with markers of epithelial and vascular barrier impairment. AI integration can enhance standardization toward broader clinical applicability.

Integrating artificial intelligence (AI) into histologic disease assessment is transforming the management of inflammatory bowel disease (IBD). AI-aided histology enables precise, objective evaluations of disease activity by analysing whole-slide images, facilitating accurate predictions of histologic remission (HR) in ulcerative colitis and Crohn’s disease. Additionally, AI shows promise in predicting adverse outcomes and therapeutic responses, making it a promising tool for clinical practice and clinical trials. By leveraging advanced algorithms, AI enhances diagnostic accuracy, reduces assessment variability and streamlines histological workflows in clinical settings. In clinical trials, AI aids in assessing histological endpoints, enabling real-time analysis, standardising evaluations and supporting adaptive trial designs. Recent advancements are further refining AI-aided digital pathology in IBD. New developments in multimodal AI models integrating clinical, endoscopic, histologic and molecular data pave the way for a comprehensive approach to precision medicine in IBD. Automated assessment of intestinal barrier healing – a deeper level of healing beyond endoscopic and HR – shows promise for improved outcome prediction and patient management. Preliminary evidence also suggests that AI applied to colitis-associated neoplasia can aid in the detection, characterisation and molecular profiling of lesions, holding potential for enhanced dysplasia management and organ-sparing approaches. Although challenges remain in standardisation, validation through randomised controlled trials and ethical considerations. AI is poised to revolutionise IBD management by advancing towards a more personalised and efficient care model, while the path to full clinical implementation may be lengthy. However, the transformative impact of AI on IBD care is already shining through.

Limited knowledge is available about the relationship between food allergies or intolerances and inflammatory bowel disease (IBD). Clinicians frequently encounter patients who report food allergies or intolerances, and gastroenterologists struggle distinguishing between patients with organic disorders and those with functional disorders, which the patients themselves may associate with specific dietary components. This task becomes even more arduous when managing patients with significant underlying organic conditions, like IBD. The aim of this review is to summarize and emphasize any actual associations between food allergies and intolerances and inflammatory diseases, such as ulcerative colitis and Crohn’s disease. Through a narrative disceptation of the current literature, we highlight the increased prevalence of various food intolerances, including lactose, fructose, histamine, nickel, and non-celiac gluten sensitivity, in individuals with IBD. Additionally, we explore the association between increased epithelial barrier permeability in IBD and the development of food sensitization. By doing so, we aim to enhance clinicians’ awareness of the nutritional management of patients with IBD when facing complaints or evidence of food allergies or intolerances.