Inflammatory bowel diseases (IBDs) are characterized by chronic gastrointestinal inflammation due to abnormal immune responses to gut microflora. The gut–brain axis is disrupted in IBDs, leading to neurobiological imbalances and affective symptoms. Systemic inflammation in IBDs affects the brain’s inflammatory response system, hormonal axis, and blood–brain barrier integrity, influencing the gut microbiota. This review aims to explore the association between dysregulations in the gut–brain axis, serum biomarkers, and the development of cognitive disorders. Studies suggest a potential association between IBDs and the development of neurodegeneration. The mechanisms include systemic inflammation, nutritional deficiency, GBA dysfunction, and the effect of genetics and comorbidities. The objective is to identify potential correlations and propose future research directions to understand the impact of altered microbiomes and intestinal barrier functions on neurodegeneration. Serum levels of vitamins, inflammatory and neuronal damage biomarkers, and neuronal growth factors have been investigated for their potential to predict the development of neurodegenerative diseases, but current results are inconclusive and require more studies.

Background and Aims: Identifying the risk factors for extraintestinal manifestations (EIMs) in inflammatory bowel diseases (IBD) may optimize the therapeutic decision. We aimed to assess the prevalence of EIMs in IBD patients in Romania and to determine the risk factors. Methods: We analyzed 2,626 patients registered in the Romanian IBD Prospect National Registry. We performed a descriptive cross-sectional study to assess the point prevalence of EIMs, calculating global prevalence and analyzing the different types of EIMs and their respective frequencies were carried out. Demographic and clinical risk factors were researched as possible predictors for EIMs development, based on the results of the univariate and multivariate logistic regression analysis. Results: The overall point prevalence of EIMs was 16.3%. A significantly higher frequency of EIMs in Crohn’s disease (CD) was noted in comparison to ulcerative colitis (UC) and IBD unclassified (IBDU) (23.2% vs 11.3% and 16.3%, respectively, p<0.001). The most frequent type of EIM was peripheral arthropathy (8.3%), significantly associated with CD (p<0.001). Univariate analysis highlighted the significant independent common predictive risk factors for EIMs, in both CD and UC patients: female gender, patient’s urban area of origin, anemia, hypoalbuminemia, and high level of C-reactive protein (CRP), while significant independent IBD phenotype-related risk factors were ileocolonic location and concomitant involvement of upper gastrointestinal tract for CD, non-smoker status and both moderate and severe disease activity for UC (p<0.05). Multivariate analysis determined that female CD patients with moderate or severe disease activity, with other than isolated ileal disease, and female UC patients with moderate or severe extensive colitis are the most likely to develop EIMs. Conclusions: IBD patients are experiencing EIMs in a large proportion, with higher rates for CD. As EIMs negatively affect patient outcomes, foreseeing the risk by identifying independent and associated predictive factors could be a first step to optimal work-up and treatment.

Background and Objectives: Celiac disease (CD) is an immune-mediated enteropathy with characteristic intestinal alterations. CD occurs as a chronic inflammation secondary to gluten sensitivity in genetically susceptible individuals. Until now, the exact cause of the disease has not been established, which is why new studies have appeared that address the involvement of various genes and microRNAs (miRNAs) in the pathogenesis. The aim of the study is to describe the expression of selected genes (Wnt family member 3, WNT3; Wnt family member 11, WNT11; tumor necrosis factor alpha, TNFα; mitogen-activated protein kinase 1, MAPK1; AKT serine/threonine kinase 3, AKT3; phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha, PIK3CA; and cyclin D1, CCND1) and miRNAs (miR-192-5p, miR-194-5p, miR-449a and miR-638) in adult patients with CD. Materials and Methods: In total, 15 patients with CD at diagnosis (newly diagnosed), 33 patients on a gluten-free diet (GFD) for at least 1 year and 10 controls (control) were prospectively included. Blood samples were evaluated by quantitative real-time polymerase chain reaction (qRT-PCR). Results: The results show that TNFα, MAPK1 and CCND1 were significantly overexpressed (p = 0.0249, p = 0.0019 and p = 0.0275, respectively) when comparing the newly diagnosed group to the controls. The other genes studied in CD patients were mostly with high values compared to controls, without reaching statistical significance. Among the miRNAs, the closest to a statistically significant value was miR-194-5p when the newly diagnosed group versus control (p = 0.0510) and GFD group versus control (p = 0.0671) were compared. The DIANA and miRNet databases identified significant functional activity for miR-449a and miR-192-5p and an interconnection of miR-194-5p and miR-449a with CCND1. Conclusions: In conclusion, genes and circulating miRNAs require further studies as they could represent important biomarkers in clinical practice.

Artificial intelligence (AI) is rapidly transforming imaging modalities in inflammatory bowel disease (IBD), particularly in endoscopy and ultrasound. Despite their critical roles, both modalities are challenged by interobserver variability, subjectivity, and accessibility issues. AI offers significant potential to address these limitations by enhancing lesion detection, standardizing disease activity scoring, and supporting clinical decision-making. In endoscopy, deep convolutional neural networks have achieved high accuracy in detecting mucosal abnormalities and grading disease severity, reducing observer dependency and improving diagnostic consistency. AI-assisted colonoscopy systems have also demonstrated improvements in procedural quality metrics, including adenoma detection rates and withdrawal times. Similarly, AI applications in intestinal ultrasound show promise in automating measurements of bowel wall thickness, assessing vascularity, and distinguishing between inflammatory and fibrotic strictures, which are critical for tailored therapy decisions. Video capsule endoscopy has likewise benefited from AI, reducing interpretation times and enhancing the detection of subtle lesions. Despite these advancements, implementation challenges, including dataset quality, standardization, AI interpretability, clinician acceptance, and regulatory and ethical considerations, must be carefully addressed. The current review focuses on the most recent developments in the integration of AI into experimental designs, medical devices, and clinical workflows for optimizing diagnostic accuracy, treatment strategies, and patient outcomes in IBD management.