In the rapidly evolving landscape of gastrointestinal health care, the integration of artificial intelligence (AI) presents unprecedented opportunities for enhancing patient outcomes, improving efficiency, and driving innovation. Effective collaboration among clinicians, academia, and industry is crucial to harness the full potential of AI technologies. Clinicians offer invaluable insights from real-world practice, ensuring that AI solutions address genuine clinical needs and improve patient care. Academia plays a pivotal role in advancing research, developing new methodologies, and training the next generation of professionals who will navigate this transformative field. Industry drives the commercialization of AI tools, providing the resources and infrastructure necessary for widespread adoption. Achieving these synergies is challenging. Issues including data privacy, regulatory hurdles, and interdisciplinary communication must be addressed to foster effective partnerships. By embracing collaborative models, including public-private partnerships, clinical trials, and innovation hubs, stakeholders can work together to overcome barriers and promote responsible AI integration in gastroenterology.

Inflammatory bowel disease (IBD), comprising ulcerative colitis and Crohn's disease, is a chronic inflammatory condition with global prevalence and varying incidence. The IBD pathogenesis involves intricate interactions among genetic, host and environmental factors, leading to dysregulated immune responses and chronic intestinal inflammation. Alongside elevated levels of inflammatory cytokines and altered miRNAs expression, more studies highlight significant dysbiosis in both fecal and ileal microbiota of IBD patients. This dysbiosis is characterized by an increase in pro-inflammatory and mucin-degrading bacteria (e.g., Fusobacterium spp., Escherichia spp.) and a decline in short-chain fatty acids (SCFAs) -producing microbes (e.g., Roseburia spp., Faecalibacterium spp.) which play a protective role in gut health. Diet emerges as a key environmental factor influencing IBD onset and progression and recent advancements in"omics" technologies, such as genomics, transcriptomics, and metabolomics, provide a deeper understanding of the molecular interactions between genes, gut microbiota (GM) and nutrition. Finally, new technologies like artificial intelligence (AI), further enhance findings by enabling data integration and personalized dietary strategies. In this scenario, this review aims to summarize accumulating data on the effects of dietary interventions in IBD patients and introduce the role of artificial intelligence (AI) in facilitating precision dietary approaches to improve IBD management.

Inflammatory bowel disease (IBD) is a complex condition influenced by various intestinal factors. Advances in next-generation sequencing, high-throughput omics, and molecular network technologies have significantly accelerated research in this field. The emergence of artificial intelligence (AI) has further enhanced the efficient utilization and interpretation of datasets, enabling the discovery of clinically actionable insights. AI is now extensively applied in gastroenterology, where it aids in endoscopic analyses, including the diagnosis of colorectal cancer, precancerous polyps, gastrointestinal inflammatory lesions, and bleeding. Additionally, AI supports clinicians in patient stratification, predicting disease progression and treatment responses, and adjusting treatment plans in a timely manner. This approach not only reduces healthcare costs but also improves patient health and safety. This review outlines the principles of AI, the current research landscape, and future directions for its applications in IBD, with the goal of advancing targeted treatment strategies.

Mucosal healing in Crohn's disease (CD) has been established as a crucial target of treatment, leading to long term remission and decrease in complication rates. Endoscopy still serves as the gold standard for assessment, particularly in the small bowel where balloon or capsule enteroscopy is frequently needed. However, these modalities are often unavailable, expensive, and invasive, posing risks to patients. Consequently, the identification of accessible and reliable biomarkers, especially in small intestinal CD, remains a challenge. The study by Ohno et al, published in this issue, further illuminates this field. It confirms the potential role of fecal biomarker leucine-rich α2 glycoprotein (LRG) and validates findings from previous smaller trials. Comparing to other markers LRG showed a much higher predictive value for mucosal healing of the small bowel, making it a useful option for small intestinal CD follow up. In this editorial, we explore the optimal marker of inflammation or mucosal healing in CD, particularly in the small bowel. We provide an overview of available conventional biomarkers and introduce several novel biomarkers, including an update on emerging technologies and innovations.

Grading activity of inflammatory bowel disease (IBD) using standardized histopathological scoring systems remains challenging due to limited availability of pathologists with IBD expertise and interobserver variability. In this study, a deep learning model was developed to classify activity grades in hematoxylin and eosin-stained whole slide images (WSIs) from patients with IBD, offering a robust approach for general pathologists. This study utilized 2077 WSIs from 636 patients who visited Dartmouth-Hitchcock Medical Center in 2018 and 2019, scanned at ×40 magnification (0.25 μm/pixel). Board-certified gastrointestinal pathologists categorized the WSIs into four activity classes: inactive, mildly active, moderately active, and severely active. A transformer-based model was developed and validated using five-fold cross-validation to classify IBD activity. Using HoVer-Net, neutrophil distribution across activity grades was examined. Attention maps from the model highlighted areas contributing to its prediction. The model classified IBD activity with weighted averages of 0.871 (95% CI, 0.860-0.883) for the area under the curve, 0.695 (95% CI, 0.674-0.715) for precision, 0.697 (95% CI, 0.678-0.716) for recall, and 0.695 (95% CI, 0.674-0.714) for F1 score. Neutrophil distribution was significantly different across activity classes. Qualitative evaluation of attention maps by a gastrointestinal pathologist suggested their potential for improved interpretability. The model demonstrates robust diagnostic performance and could enhance consistency and efficiency in IBD activity assessment.

Advanced therapies (ADT) that encompass biological agents and small molecules have been approved for the treatment of inflammatory bowel disease (IBD), broadening the spectrum of available treatment options. Nevertheless, a substantial proportion of patients fail to achieve satisfactory responses, necessitating surgical intervention. Treatment objectives have evolved beyond clinical remission, reduction of inflammation, and mucosal healing, shifting focus toward enhancing the quality of life, acknowledging the profound impact of IBD on physical and mental well-being.

This comprehensive review describes the current landscape of ADT for IBD, including dual biologic therapy (DBT), which involves the combination of two biologics or a single biologic with a small-molecule compound, as well as considerations surrounding the discontinuation of biologics.

ADT is the standard treatment for moderate to severe IBD, while DBT appears promising for specific subsets of patients, including those with refractory disease or extraintestinal manifestations. However, these approaches may increase the risk of adverse effects, including malignancy. To optimize individualized treatment strategies in patients with refractory IBD, further trials are needed to refine ADT's predictive value, establish DBT's safety and indications, define biologic discontinuation criteria, and evaluate emerging biomarkers, artificial intelligence, and bowel ultrasound in patient management.

Background/Objectives: The primary objective of our study was to find a potential use for images generated by imagistic investigations by comparing the appearance of a healthy digestive tract to that of a pathological one. Methods: We conducted a cross-sectional observational study involving 60 older adult patients admitted to and followed up at a primary center in Romania. Our focus was on different diagnostic methods and the use of artificial intelligence (AI) tools integrated into the electronic health records system. Results: Currently, imagery, laboratory values and electronic health records (EHR) can also be used to train AI models. Comparative imagery to predict the appearance of inflammatory bowel disease (IBD) can be used as a predictor model. Conclusions: Our findings indicate with certainty that training a tool in the diagnosis and prevention of relapses in older adults with IBD is promising for further integrating these models into patient care.

In this review, we examine the significance of multi-omics technologies in understanding the plethora of intricate processes that activate gastrointestinal (GI) injury repair. Multi-omics, which includes genomics, transcriptomics, proteomics, and metabolomics, allows intricate mapping of cellular responses and molecular pathways involved in GI repair. We highlight the potential of multi-omics to discover previously unknown therapeutic targets or elucidate the molecular basis of the pathogenesis of GI. Furthermore, we explore the possibilities of integrating omics data to improve prediction models, and summarize the state-of-the-art technological developments and persisting obstacles that hinder the translation of multi-omics into clinical practice. Finally, innovative multi-omics approaches that can improve patient outcomes and advance therapeutic strategies in GI medicine are discussed.

Inflammatory bowel diseases (IBD) including Crohn's disease (CD) and ulcerative colitis (UC) are chronic, relapsing conditions characterized by dysregulated immune responses and persistent intestinal inflammation. This review aims to examine new potential therapeutic targets in IBD starting from the STRIDE-II statements. Key targets now include clinical remission, endoscopic remission, and biomarker normalization (such as C-reactive protein and fecal calprotectin). Moreover, histologic remission, transmural remission, and in the future molecular targets are emerging as important indicators of sustained disease control. The treatment goals for inflammatory bowel disease are varied: to relieve symptoms, prevent permanent intestinal damage, promote inflammation remission, and minimize complications. Consequently, the therapeutic targets have evolved to become broader and more ambitious. Integrating these advanced therapeutic targets has the potential to redefine IBD management by promoting deeper disease control and improved patient outcomes. Further research is essential to validate these strategies and optimize their clinical implementation.

Recent advances in the field of endoscopy have found fertile ground for application in inflammatory bowel diseases (IBD). Mucosal healing is a primary goal of IBD therapy, and current evidence shows that histologic remission (HR) is an additional desirable outcome. However, with the use of standard endoscopy, a considerable number of patients with histologically active disease go unrecognized. This narrative article examines the role, current or potential, of each endoscopic technique, from standard white-light endoscopy to molecular imaging, in the assessment of mucosal healing and HR in IBD.

A singular reliable modality for early distinguishing perianal fistulizing Crohn's disease (PFCD) from cryptoglandular fistula (CGF) is currently lacking. We aimed to develop and validate an MRI-based deep learning classifier to effectively discriminate between them.

The present study retrospectively enrolled 1054 patients with PFCD or CGF from three Chinese tertiary referral hospitals between January 1, 2015, and December 31, 2021. The patients were divided into four cohorts: training cohort (n = 800), validation cohort (n = 100), internal test cohort (n = 100) and external test cohort (n = 54). Two deep convolutional neural networks (DCNN), namely MobileNetV2 and ResNet50, were respectively trained using the transfer learning strategy on a dataset consisting of 44871 MR images. The performance of the DCNN models was compared to that of radiologists using various metrics, including receiver operating characteristic curve (ROC) analysis, accuracy, sensitivity, and specificity. Delong testing was employed for comparing the area under curves (AUCs). Univariate and multivariate analyses were conducted to explore potential factors associated with classifier performance.

A total of 532 PFCD and 522 CGF patients were included. Both pre-trained DCNN classifiers achieved encouraging performances in the internal test cohort (MobileNetV2 AUC: 0.962, 95% CI 0.903-0.990; ResNet50 AUC: 0.963, 95% CI 0.905-0.990), as well as external test cohort (MobileNetV2 AUC: 0.885, 95% CI 0.769-0.956; ResNet50 AUC: 0.874, 95% CI 0.756-0.949). They had greater AUCs than the radiologists (all p ≤ 0.001), while had comparable AUCs to each other (p = 0.83 and p = 0.60 in the two test cohorts). None of the potential characteristics had a significant impact on the performance of pre-trained MobileNetV2 classifier in etiologic diagnosis. Previous fistula surgery influenced the performance of the pre-trained ResNet50 classifier in the internal test cohort (OR 0.157, 95% CI 0.025-0.997, p = 0.05).

The developed DCNN classifiers exhibited superior robustness in distinguishing PFCD from CGF compared to artificial visual assessment, showing their potential for assisting in early detection of PFCD. Our findings highlight the promising generalized performance of MobileNetV2 over ResNet50, rendering it suitable for deployment on mobile terminals.

National Natural Science Foundation of China.

This editorial offers an updated synthesis of the major advancements in the management and treatment of inflammatory bowel disease (IBD), as documented in the World Journal of Gastroenterology between 2023 and early 2024. This editorial explores substantial developments across key research areas, such as intestinal microecology, computational drug discovery, dual biologic therapy, telemedicine, and the integration of lifestyle changes into patient care. Furthermore, the discussion of emerging topics, including bowel preparation in colonoscopy, the impact of the coronavirus disease 2019 pandemic, and the intersection between IBD and mental health, reflects a shift toward a more holistic approach to IBD research. By integrating these diverse areas of research, this editorial seeks to promote a holistic and multidisciplinary approach to IBD treatment, combining emerging technologies, personalized medicine, and conventional therapies to improve patient outcomes.

Patients with inflammatory bowel diseases (IBDs), including both ulcerative colitis (UC) and Crohn's disease (CD), are at a higher risk of developing colorectal cancer (CRC). However, advancements in endoscopic imaging techniques, integrated surveillance programs, and improved medical therapies have led to a decrease in the incidence of CRC among IBD patients. Currently, the management of patients with IBD who have a history of or ongoing active malignancy is an unmet need. This involves balancing the risk of cancer recurrence/progression with the potential exacerbation of IBD if the medications are discontinued. The objective of this review is to provide an updated summary of the epidemiology, causes, risk factors, and surveillance approaches for CRC in individuals with IBD, and to offer practical guidance on managing IBD patients with history of previous or active cancer.

Impaired quality of life (QOL) is common in patients with inflammatory bowel disease (IBD). A tool to more quickly identify IBD patients at high risk of impaired QOL improves opportunities for earlier intervention and improves long-term prognosis. The purpose of this study was to use a machine learning (ML) approach to develop risk stratification models for evaluating IBD-related QOL impairments.

An online questionnaire was used to collect clinical data on 2478 IBD patients from 42 hospitals distributed across 22 provinces in China from September 2021 to May 2022. Eight ML models used to predict the risk of IBD-related QOL impairments were developed and validated. Model performance was evaluated using a set of indexes and the best ML model was explained using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm.

The support vector machine (SVM) classifier algorithm-based model outperformed other ML models with an area under the receiver operating characteristic curve (AUC) and an accuracy of 0.80 and 0.71, respectively. The feature importance calculated by the SVM classifier algorithm revealed that glucocorticoid use, anxiety, abdominal pain, sleep disorders, and more severe disease contributed to a higher risk of impaired QOL, while longer disease course and the use of biological agents and immunosuppressants were associated with a lower risk.

An ML approach for assessing IBD-related QOL impairments is feasible and effective. This mechanism is a promising tool for gastroenterologists to identify IBD patients at high risk of impaired QOL.

Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform "Teachable Machine" to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.

Crohn's disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract. Intestinal fibrosis or stricture is one of the most prevalent complications in CD with a high recurrence rate. Manual examination of intestinal fibrosis or stricture by physicians may be biased or inefficient. A rapid development of artificial intelligence (AI) technique in recent years facilitates the detection of existing or possible intestinal fibrosis and stricture in CD through various modalities, including endoscopy, imaging examination, and serological biomarkers. We reviewed the articles on AI application in diagnosing intestinal fibrosis and stricture in CD during the past decade and categorized them into three aspects based on the detection methods, and found that AI helps accurate and expedient identification and prediction of intestinal fibrosis and stenosis in CD.

Inflammatory bowel diseases (IBD) remain challenging in terms of understanding their causes and in terms of diagnosing, treating, and monitoring patients. Modern diagnosis combines biomarkers, imaging, and endoscopic methods. Common biomarkers like CRP and fecal calprotectin, while invaluable tools, have limitations and are not entirely specific to IBD. The limitations of existing markers and the invasiveness of endoscopic procedures highlight the need to discover and implement new markers. With an ideal biomarker, we could predict the risk of disease development, as well as the possibility of response to a particular therapy, which would be significant in elucidating the pathogenesis of the disease. Recent research in the fields of machine learning, proteomics, epigenetics, and gut microbiota provides further insight into the pathogenesis of the disease and is also revealing new biomarkers. New markers, such as BAFF, PGE-MUM, oncostatin M, microRNA panels, αvβ6 antibody, and S100A12 from stool, are increasingly being identified, with αvβ6 antibody and oncostatin M being potentially close to being presented into clinical practice. However, the specificity of certain markers still remains problematic. Furthermore, the use of expensive and less accessible technology for detecting new markers, such as microRNAs, represents a limitation for widespread use in clinical practice. Nevertheless, the need for non-invasive, comprehensive markers is becoming increasingly important regarding the complexity of treatment and overall management of IBD.

Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.

Diagnosing inflammatory bowel disease (IBD) can often be challenging, and differentiating between Crohn's disease and ulcerative colitis can be particularly difficult. Diagnostic procedures for IBD include laboratory tests, endoscopy, pathological tests, and imaging tests. Serological and stool tests can be easily performed in an outpatient setting and provide critical diagnostic clues. Although endoscopy is an invasive procedure, it offers essential diagnostic information and allows for tissue biopsy and therapeutic procedures. Video capsule endoscopy and device-assisted enteroscopy are endoscopic procedures used to evaluate the small bowel. In addition to endoscopy, magnetic resonance imaging, computed tomography, and ultrasound (US) are valuable tools for small bowel assessment. Among these, US is noninvasive and easily utilized, making its use highly practical in daily clinical practice. Endoscopic biopsy aids in the diagnosis of IBD and is crucial for assessing the histological activity of the disease, facilitating a thorough evaluation of disease remission, and aiding in the development of treatment strategies. Recent advances in artificial intelligence hold promise for enhancing various aspects of IBD management, including diagnosis, monitoring, and precision medicine. This review compiles current procedures and promising future tools for the diagnosis of IBD, providing comprehensive insights.

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition.

A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English.

Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis.

When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

The timely introduction and adjustment of the appropriate drug in accordance with previously well-defined treatment goals is the foundation of the approach in the treatment of inflammatory bowel disease (IBD). The therapeutic approach is still evolving in terms of the mechanism of action but also in terms of the possibility of maintaining remission. In patients with achieved long-term remission, the question of de-escalation or discontinuation of therapy arises, considering the possible side effects and economic burden of long-term therapy. For each of the drugs used in IBD (5-aminosalycaltes, immunomodulators, biological drugs, small molecules) there is a risk of relapse. Furthermore, studies show that more than 50% of patients who discontinue therapy will relapse. Based on the findings of large studies and meta-analysis, relapse of disease can be expected in about half of the patients after therapy withdrawal, in case of monotherapy with aminosalicylates, immunomodulators or biological therapy. However, longer relapse-free periods are recorded with withdrawal of medication in patients who had previously been on combination therapies immunomodulators and anti-tumor necrosis factor. It needs to be stressed that randomised clinical trials regarding withdrawal from medications are still lacking. Before making a decision on discontinuation of therapy, it is important to distinguish potential candidates and predictive factors for the possibility of disease relapse. Fecal calprotectin level has currently been identified as the strongest predictive factor for relapse. Several other predictive factors have also been identified, such as: High Crohn's disease activity index or Harvey Bradshaw index, younger age (< 40 years), longer disease duration (> 40 years), smoking, young age of disease onset, steroid use 6-12 months before cessation. An important factor in the decision to withdraw medication is the success of re-treatment with the same or other drugs. The decision to discontinue therapy must be based on individual approach, taking into account the severity, extension, and duration of the disease, the possibility of side adverse effects, the risk of relapse, and patient's preferences.

Whether dye spray chromoendoscopy (DCE) adds value in surveillance colonoscopy with high-definition (HD) scopes remains controversial. This updated meta-analysis compares dysplasia detection using DCE and high-definition white light endoscopy (HD-WLE) in patients with inflammatory bowel disease (IBD) undergoing surveillance colonoscopy.

A comprehensive search was performed for randomized controlled trials (RCT) comparing HD-WLE and DCE in patients with IBD. The primary outcome was to compare the proportion of patients with at least 1 dysplastic lesion detected by DCE vs HD-WLE. Odds ratios (OR) and 95% confidence intervals (CI) were pooled using the random-effects model, with I2 > 60% indicating substantial heterogeneity. The Grading of Recommendations, Assessment, Development, and Evaluation approach was used to assess the certainty of evidence (CoE).

Six RCT involving 978 patients were analyzed (DCE = 479 vs HD-WLE = 499 patients). DCE detected significantly more patients with dysplasia than HD-WLE (18.8% vs 9.4%), OR 1.94 (95% CI 1.21-3.11, I2 = 28%, P = 0.006, high CoE). This remained significant after excluding 2 RCT published as abstracts. A sensitivity analysis excluding a noninferiority RCT with a single experienced operator eliminated the results' heterogeneity, OR 2.46 (95% CI 1.56-3.90, I2 = 0%). Although high-grade dysplasia detection was numerically higher in the DCE group (2.8% vs 1.1%), the difference was statistically insignificant, OR 2.21 (95% CI 0.64-7.62, I2 = 0%, low CoE).

Our updated meta-analysis supports DCE as a superior strategy in overall dysplasia detection in IBD, even with HD scopes. When expertise is available, DCE should be considered for surveillance colonoscopy in patients with high-risk IBD, with the acknowledgment that virtual chromoendoscopy shows equivalence in recent studies. Further multicenter trials with multiple endoscopists with varying expertise levels and longer-term outcome data showing a reduction in cancer or cancer-related death are needed.

Inflammatory bowel disease (IBD), marked by chronic gastrointestinal tract inflammation, poses a significant global medical challenge. Current treatments for IBD, including corticosteroids, immunomodulators, and biologics, often require frequent systemic administration through parenteral delivery, leading to nonspecific drug distribution, suboptimal therapeutic outcomes, and adverse effects. There is a pressing need for a targeted drug delivery system to enhance drug efficacy and minimize its systemic impact. Nanotechnology emerges as a transformative solution, enabling precise oral drug delivery to inflamed intestinal tissues, reducing off-target effects, and enhancing therapeutic efficiency. The advantages include heightened bioavailability, sustained drug release, and improved cellular uptake. Additionally, the nano-based approach allows for the integration of theranostic elements, enabling simultaneous diagnosis and treatment. Recent preclinical advances in oral IBD treatments, particularly with nanoformulations such as functionalized polymeric and lipid nanoparticles, demonstrate remarkable cell-targeting ability and biosafety, promising to overcome the limitations of conventional therapies. These developments signify a paradigm shift toward personalized and effective oral IBD management. This review explores the potential of oral nanomedicine to enhance IBD treatment significantly, focusing specifically on cell-targeting oral drug delivery system for potential use in IBD management. We also examine emerging technologies such as theranostic nanoparticles and artificial intelligence, identifying avenues for the practical translation of nanomedicines into clinical applications.

Enteric nervous system (ENS) has been closely associated with the neuro-immune response and is currently considered a reliable target for intestinal inflammation. Neuronal nitric oxide synthase (nNOS) nerves are involved in inflammatory diseases by releasing nitric oxide (NO). EphB2 expression and density of innervation of the mucosal layer are positively correlated with the severity of intestinal inflammatory responses. In this study, we hypothesized that a EphB2-mediated mechanism may regulate enteric immunity through modulation of nNOS nerves.

Firstly, the Western blot (WB) method was employed to quantify EphB2 expression in the intestinal mucosal layer of DSS mice and assess alterations in nerve fiber activation and density. Immunofluorescence (IF) double staining with nNOS and neuronal marker PGP9.5 was conducted to measure nNOS nerve fiber density within the intestinal mucosal layer of mice. Subsequently, in vivo experiments were performed to investigate the inhibitory or activatory effect of EphB2Fc or EphrinB2Fc on EphB2 expression and activation. Immunoprecipitation experiments confirmed the interaction between EphB2 and nNOS nerves. WB and IF experiments were carried out to evaluate both inflammatory conditions of mouse colonic mucosa following intervention with EphB2Fc/EphrinB2Fc as well as changes in nNOS nerve fibers expression. Finally, in vitro experiments, neurally-mediated inflammation was assessed in the organ bath system by activating intestinal mucosal innervation through Veratridine (VER) and electrical field stimulation (EFS) techniques for 3 h. The activation of nNOS nerves was inhibited by nitroindazole (7NI). WB was employed to detect changes in the expression of inflammatory factors in the intestinal mucosal layer in EphB2Fc/EphrinB2Fc treated mice and control group.

We found that the expression of EphB2 and density nNOS nerve fibers in the intestinal mucosa were positively correlated with the colitis response. Blocking (EphB2Fc)/activating (EphrinB2Fc) EphB2 in vivo significantly reduced/increased the density of nNOS nerve fibers and expression of inflammatory factors in colonic mucosa of DSS treated mice. In vitro, blocking nNOS nerves activation attenuated the inflammatory reaction induced by either EFS or EphB2.

Our findings provided evidence that EphB2 mediated regulation of innate immunity-ENS crosstalk might represent an attractive target for novel therapeutic strategies in ulcerative colitis.

Background: Type 2 diabetes mellitus (T2DM) and inflammatory bowel disease (IBD) have been associated, according to various epidemiological research. This study uses Mendelian randomization (MR) to investigate the causal link between T2DM and IBD. Methods: To investigate the causal relationship between IBD and T2DM risk using European population data from the genome-wide association study (GWAS) summary datasets, we constructed a two-sample MR study to evaluate the genetically predicted impacts of liability towards IBD outcomes on T2DM risk. As instrumental variables (IVs), we chose 26 single nucleotide polymorphisms (SNPs) associated with IBD exposure data. The European T2DM GWAS data was obtained from the IEU OpenGWAS Project database, which contains 298,957 cases as the outcome data. The causal relationship between T2DM and IBD using a reverse MR analysis was also performed. Results: The two-sample MR analysis, with the Bonferroni adjustment for multiple testing, revealed that T2DM risk in Europeans is unaffected by their IBD liability (odds ratio (OR): 0.950-1.066, 95% confidence interval (CI): 0.885-1.019, p = 0.152-0.926). The effects of liability to T2DM on IBD were not supported by the reverse MR analysis either (OR: 0.739-1.131, 95% confidence interval (CI): 0.651-1.100, p = 0.058-0.832). MR analysis of IBS on T2DM also have no significant causal relationship (OR: 0.003-1.007, 95% confidence interval (CI): 1.013-5.791, p = 0.069-0.790). FUMA precisely mapped 22 protein-coding genes utilizing significant SNPs of T2DM acquired from GWAS. Conclusion: The MR study showed that the existing evidence did not support the significant causal effect of IBD on T2DM, nor did it support the causal impact of T2DM on IBD.

Acupuncture is a traditional medicinal practice in China that has been increasingly recognized in other countries in recent decades. Notably, several reports have demonstrated that acupuncture can effectively aid in pain management. However, the analgesic mechanisms through which acupuncture provides such benefits remain poorly understood. Purinergic signaling, which is mediated by purine nucleotides and purinergic receptors, has been proposed to play a central role in acupuncture analgesia. On the one hand, acupuncture affects the transmission of nociception by increasing adenosine triphosphate dephosphorylation and thereby decreasing downstream P2X3, P2X4, and P2X7 receptors signaling activity, regulating the levels of inflammatory factors, neurotrophic factors, and synapsin I. On the other hand, acupuncture exerts analgesic effects by promoting the production of adenosine, enhancing the expression of downstream adenosine A1 and A2A receptors, and regulating downstream inflammatory factors or synaptic plasticity. Together, this systematic overview of the field provides a sound, evidence-based foundation for future research focused on the application of acupuncture as a means of relieving pain.

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

The introduction of widespread colonoscopy screening programs has helped in decreasing the incidence of Colorectal Cancer (CRC). However, 'back-to-back' colonoscopies revealed relevant percentage of missed adenomas. Quality indicators were created to further homogenize detection performances and decrease the incidence of post-colonoscopy CRC. Among them, the Adenoma Detection Rate (ADR), defined as the percentage obtained by dividing the number of endoscopic procedures in which at least one adenoma was resected, by the total number of procedures, was found to be inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer.

In this paper, we performed a comprehensive review of the literature focusing on promising new devices and technologies, which are meant to positively affect the endoscopist performance in detecting adenomas, therefore increasing ADR.

Considering the current knowledge, although several devices and technologies have been proposed with this intent, the recent implementation of AI ranked over all of the other strategies and it is likely to become the new standard within few years. However, the combination of different device/technologies need to be investigated in the future aiming at even further increasing of endoscopist detection performances.