Central reading, that is, independent, off-site, blinded review or reading of imaging endpoints, has been identified as a crucial component in the conduct and analysis of inflammatory bowel disease clinical trials. Central reading is the final step in a workflow that has many parts, all of which can be improved. Furthermore, the best reading algorithm and the most intensive central reader training cannot make up for deficiencies in the acquisition stage (clinical trial endoscopy) or improve on the limitations of the underlying score (outcome instrument). In this review, academic and industry experts review scoring systems, and propose a theoretical framework for central reading that predicts when improvements in statistical power, affecting trial size and chances of success, can be expected: Multireader models can be conceptualised as statistical or non-statistical (social). Important organisational and operational factors, such as training and retraining of readers, optimal bowel preparation for colonoscopy, video quality, optimal or at least acceptable read duration times and other quality control matters, are addressed as well. The theory and practice of central reading and the conduct of endoscopy in clinical trials are interdisciplinary topics that should be of interest to many, regulators, clinical trial experts, gastroenterology societies and those in the academic community who endeavour to develop new scoring systems using traditional and machine learning approaches.

Alcohol-related liver disease (ALD) is a global healthcare problem with limited treatment options. Alpha-1 antitrypsin (AAT, encoded by SERPINA1) shows potent anti-inflammatory activities in many preclinical and clinical trials. In our study, we aimed to explore the role of AAT in ALD.

Non-alcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is an increasing healthcare burden worldwide. We examined the role of dietary cholesterol in driving NAFLD-HCC through modulating gut microbiota and its metabolites.

Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning.

The COVID-19 pandemic has had a major global impact on endoscopic services. This reduced capacity, along with public reluctance to undergo endoscopy during the pandemic, might result in excess mortality from delayed cancer diagnosis. Using the UK's National Endoscopy Database (NED), we performed the first national analysis of the impact of the pandemic on endoscopy services and endoscopic cancer diagnosis.

Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in faeces of patients with COVID-19, the activity and infectivity of the virus in the GI tract during disease course is largely unknown. We investigated temporal transcriptional activity of SARS-CoV-2 and its association with longitudinal faecal microbiome alterations in patients with COVID-19.

Comorbid conditions are associated with poor prognosis in COVID-19. Registry data show that patients with cirrhosis may be at high risk. However, outcome comparisons among patients with cirrhosis+COVID-19 versus patients with COVID-19 alone and cirrhosis alone are lacking. The aim of this study was to perform these comparisons.