Search

Background: Achieving endoscopic and histological remission is a critical treatment objective in ulcerative colitis (UC). Nevertheless, interobserver variability can significantly impact overall assessment performance. Objectives: We aimed to develop a deep learning algorithm for the real-time and objective evaluation of endoscopic disease activity and prediction of histological remission in UC. Design: This is a retrospective diagnostic study. Methods: Two convolutional neural network (CNN) models were constructed and trained using 12,257 endoscopic images and biopsy results sourced from 1124 UC patients who underwent colonoscopy at a single center from January 2018 to December 2022. Mayo Endoscopy Subscore (MES) and UC Endoscopic Index of Severity Score (UCEIS) assessments were conducted by two experienced and independent reviewers. Model performance was evaluated in terms of accuracy, sensitivity, and positive predictive value. The output of the CNN models was also compared with the corresponding histological results to assess histological remission prediction performance. Results: The MES-CNN model achieved 97.04% accuracy in diagnosing endoscopic remission of UC, while the MES-CNN and UCEIS-CNN models achieved 90.15% and 85.29% accuracy, respectively, in evaluating endoscopic severity of UC. For predicting histological remission, the CNN models achieved accuracy and kappa values of 91.28% and 0.826, respectively, attaining higher accuracy than human endoscopists (87.69%). Conclusion: The proposed artificial intelligence model, based on MES and UCEIS evaluations from expert gastroenterologists, offered precise assessment of inflammation in UC endoscopic images and reliably predicted histological remission. Plain language summary: Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity Why was this study done? This study aimed to develop a real-time and objective diagnostic tool to reduce subjectivity when evaluating ulcerative colitis (UC) endoscopic disease activity and to predict histological remission without mucosal biopsy. What did the researchers do? We developed and validated a deep learning algorithm that uses UC endoscopic images to predict the Mayo Endoscopic Score (MES), US Endoscopic Index of Severity Score (UCEIS), and histological remission. What did the researchers find? The constructed MES- and UCEIS-based models both achieved high accuracy and performance in predicting histological remission, outperforming human endoscopists. What do the findings mean? The efficiency and performance of the deep learning algorithm rivaled that of expert assessments, which may assist endoscopists in making more objective evaluations of UC severity and in predicting histological remission. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to examine environmental factors associated with inflammatory bowel disease (IBD) in Yunnan Province, a southwestern highland region of China. Methods: In this nested case-control study, newly diagnosed ulcerative colitis (UC) cases in 2 cities in Yunnan Province and Crohn’s disease (CD) cases in 16 cities in Yunnan Province were recruited between 2008 and 2013. Controls were matched by geography, sex and age at a ratio of 1:4. Data were collected using the designed questionnaire. Conditional logistic regression models were used to estimate adjusted odds ratios (ORs). Results: A total of 678 UC and 102 CD cases were recruited. For UC, various factors were associated with an increased risk of developing UC: dietary habits, including frequent irregular meal times; consumption of fried foods, salty foods and frozen dinners; childhood factors, including intestinal infectious diseases and frequent use of antibiotics; and other factors, such as mental labor, high work stress, use of non-aspirin non-steroidal anti-inflammatory drugs and allergies (OR > 1, p < 0.05). Other factors showed a protective effect: such as consumption of fruits, current smoking, physical activity, and drinking tea (OR < 1, p < 0.05). For CD, appendectomy and irregular meal times increased the disease risk (OR >1, p < 0.05), whereas physical activity may have reduced this risk (OR < 1, p < 0.05). Conclusions: This study is the first nested case-control study to analyze the association between environmental factors and IBD onset in a southwestern highland region of China. Certain dietary habits, lifestyles, allergies and childhood factors may play important roles in IBD, particularly UC. [ABSTRACT FROM AUTHOR]

Background and Aims: The morbidity of ulcerative colitis (UC) is increasing in China every year. In addition, there is a lack of accurate diagnostic indices with which to evaluate the activity of the disease. The aim of this study was to identify UC-associated proteins as biomarkers for the diagnosis, and objective assessment of disease activity. Methods: Differential expression of serum proteins from UC patients compared to normal controls was analyzed by two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS). The expression of heat shock factor 2(HSF2)in colonic mucosa in Crohn's disease, Behcet's disease, ulcerative colitis, intestinal tuberculosis, infective enteritis, intestinal lymphoma, and normal controls was investigated by immunohistochemistry (IHC). The expression of the HSF2 in colonic mucosa of UC subjects with varying severity of disease was measured by real time-PCR and Western Blots. The expression of HSF2 was inhibited by HSF2 small interfering RNA (siRNA) transfection in Caco-2 cells. The concentrations of HSF2, IL-1β, and TNF-α in serum and IL-1β, and TNF-α in the supernatants of transfected Caco-2 cells were determined by ELISA. Results: HSF2 was differentially expressed in UC patients compared to normal controls. HSF2 expression was significantly higher in the intestinal mucosa of UC patients compared to other six groups. The results of immunohistochemistry, real time-PCR, Western Blots, and ELISA showed that the expression of HSF2 increased in parallel with the severity of UC. The serum concentration of HSF2 also positively correlated with levels of IL-1β and TNF-α. After down-regulation expression of HSF2 in Caco-2 cells by RNA interference, the productions of IL-1β and TNF-α stimulated by lipopolysaccharide (LPS) increased dramatically. Conclusions: HSF2 appears to be a potential novel molecular marker for UC activity, and may provide a basis for studies on the pathogenesis and novel therapeutic targets for UC. [ABSTRACT FROM AUTHOR]

Excessive proinflammatory cytokine release induced by pyroptosis plays a vital role in intestinal mucosal inflammation in ulcerative colitis (UC). Several pyroptosis‐related factors are regulated by the centrosome. Pericentriolar material 1 (PCM1) is a primary component of centriolar satellites that is present as cytoplasmic granules around the centrosome. Our previous study revealed that PCM1 was highly expressed in UC patients, but the role of PCM1 in UC remains unknown. This study aimed to elucidate the role of PCM1 in the development of UC, especially the mechanism in pyroptosis process of UC. Clinical mucosal sample and dextran sulfate sodium (DSS)‐induced colitis mouse were used to reveal the association between PCM1 and intestinal inflammation. Intestinal epithelial cell‐specific PCM1‐knockout mice were constructed to determine the role of PCM1 in colitis. Finally, PCM1 RNA interference and overexpression assays in THP1 cells were employed to study the molecular mechanisms of PCM1 in inflammatory responses and pyroptosis. We found that PCM1 expression was upregulated in the colonic mucosa of UC patients and positively correlated with inflammatory indicators. PCM1 expression was elevated in DSS‐induced colitis mice and was reduced after methylprednisolone treatment. In the DSS colitis model, intestinal‐specific PCM1‐knockout mice exhibited milder intestinal inflammation and lower pyroptosis levels than wild‐type mice. In cell level, PCM1 exerted a proinflammatory effect by activating the NLRP3 inflammasome and triggering subsequent gasdermin D‐mediated pyroptosis to release IL‐1β and IL‐18. In conclusion, PCM1 mediates activation of the NLRP3 inflammasome and gasdermin D‐dependent pyroptosis, ultimately accelerating intestinal inflammation in UC. These findings revealed a previously unknown role of PCM1 in initiating intestinal mucosal inflammation and pyroptosis in UC, and this factor is expected to be a regulator in the complex inflammatory network of UC. [ABSTRACT FROM AUTHOR]

• The highlights of this article are mainly divided into the following aspects:. • Firstly, a fine-grained lesion feature learner (FG-LF Learner) is proposed by integrating local features and a Softmax category prediction (SCP) module to improve the feature accuracy in small lesion areas, thus addressing the challenge of detecting small lesions. • Subsequently, a graph convolutional feature combiner (GCFC) is developed to connect features across adjacent convolutional layers and to incorporate short connections between input and output, thereby improving the network's feature transmission capability and mitigating the problem of feature loss during transmission. • Thereafter, a noise suppression gating (NS gating) technique is designed by implementing a grid attention mechanism and a feature gating (FG) module to prioritize significant lesion features and suppress irrelevant and noisy regions in the input feature map. • Finally, we employed gradient-weighted class activation mapping (Grad-CAM) technique generate a localization map for identifying and suspicious lesions, facilitating an intuitive understanding and explanation of the model's effectiveness. • We evaluated the performance of the proposed network on both privately-collected and publicly-available datasets. The evaluation of UC achieves excellent results on privately-collected dataset, with an accuracy (ACC) of 89.57 %, Matthews correlation coefficient (MCC) of 85.52 %, precision of 89.26 %, recall of 89.48 %, and F1-score of 89.78 %. On publicly-available dataset, the results are also impressive, with ACC of 85.47 %, MCC of 80.42 %, precision of 85.62 %, recall of 84.00 %, and F1-score of 84.53 %, surpassing the performance of state-of-the-art techniques. Ulcerative colitis (UC) is a chronic disease characterized by recurrent symptoms and significant morbidity. The exact cause of the disease remains unknown. The selection of current treatment options for ulcerative colitis depends on the severity and location of the disease in each patient. Therefore, developing a fully automated endoscopic images for evaluating UC is crucial for guiding treatment plans and facilitating early prevention efforts. We propose a network called ulcerative colitis evaluation based on fine-grained lesion learner and noise suppression gating (UCFN Net). UCFN Net contains three novel modules. Firstly, a fine-grained lesion feature learner (FG-LF Learner) is proposed by integrating local features and a Softmax category prediction (SCP) module to improve the feature accuracy in small lesion areas. Subsequently, a graph convolutional feature combiner (GCFC) is developed to connect features across adjacent convolutional layers and to incorporate short connections between input and output, thereby mitigating feature loss during transmission. Thereafter, a noise suppression gating (NS gating) technique is designed by implementing a grid attention mechanism and a feature gating (FG) module to prioritize significant lesion features and suppress irrelevant and noisy regions in the input feature map. We evaluate the performance of the proposed network on both privately-collected and publicly-available datasets. The evaluation of UC achieves excellent results on privately-collected dataset, with an accuracy (ACC) of 89.57 %, Matthews correlation coefficient (MCC) of 85.52 %, precision of 89.26 %, recall of 89.48 %, and F1-score of 89.78 %. The results are also impressive on publicly-available dataset, with ACC of 85.47 %, MCC of 80.42 %, precision of 85.62 %, recall of 84.00 %, and F1-score of 84.53 %, surpassing the performance of state-of-the-art techniques. Our proposed model introduces three innovative algorithm modules, which outperform the current state-of-the-art methods and achieve high ACC and F1-score. This indicates that our method has superior performance compared to traditional machine learning and existing deep methods, which means that our method has good application prospects. Meanwhile, it has been verified that the proposed model demonstrates good interpretability. The source code is available at github.com/YinLeRenNB/UCFN Net. [ABSTRACT FROM AUTHOR]

The abnormal differentiation of Th17 cells aggravates ulcerative colitis (UC). Antimicrobial peptides (AMPs) exert pivotal protection functions against UC. KT2 is a cationic AMP that mediates colon cancer development. However, KT2′s function in UC remains unclear. The UC mouse model was induced by administering 2.5% dextran sulfate sodium, and the mice were given an enema of KT2. KT2′s function in UC and Th17 cell differentiation in vivo was evaluated through various molecular experiments. The KT2′s function in Th17 cell differentiation in vitro was evaluated by the proportion of CD4+ IL-17+ T cells, IL-17 levels, and RORγt expression levels. Meanwhile, the mechanism was assessed through quantitative real-time PCR, various loss-of-function assays, and dual-luciferase reporter gene assay. KT2 restrained Th17 cell differentiation in both in vivo and in vitro UC models and slowed the UC process. KT2 elevated miR-302c-5p expression, as well as restrained Th17 cell differentiation by increasing miR-302c-5p. Meanwhile, miR-302c-5p interacted with the signal transducer and activator of transcription 3 (STAT3) and negatively regulated its expression. Furthermore, our data revealed that KT2 restrained the activation of STAT3 by elevating miR-302c-5p, thereby inhibiting Th17 cell differentiation. KT2 alleviates UC by repressing Th17 cell differentiation through the miR-302c-5p/STAT3 axis. • Antimicrobial peptide KT2 can relieve the symptoms of ulcerative colitis in mice. • Antimicrobial peptide KT2 can inhibit the differentiation of Th17 cells in vitro and in vivo. • Antimicrobial peptide KT2 can up-regulate miR-302c-5p. • MiR-302c-5p targets STAT3 and negatively regulates its expression. [ABSTRACT FROM AUTHOR]