backgroundIn general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of 'resect and discard'. study design and methods We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps. results The AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%. conclusions An AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned. IntroductIonEndoscopists combine their knowledge of the spectrum of endoscopic appearances of precancerous lesions with meticulous mechanical exploration and cleaning of mucosal surfaces to maximise lesion detection during colonoscopy. An extension of detection is endoscopic prediction of lesion histology, including differentiation of precancerous lesions from non-neoplastic lesions, and prediction of deep submucosal invasion of cancer.1 2 Image analysis can guide whether lesion removal is necessary and direct an endoscopist to the best resection method. 1-3Image analysis during colonoscopy has achieved increasing acceptance as a means to accurately predict the histology of diminutive lesions, 4 5 which have minimal risk of cancer, 6 so that these diminutive lesions could be resected and discarded without pathological assessment or left in place without resection in the case of diminutive distal colon hyperplastic polyps.3 Discarding most diminutive lesions without pathological assessment has the potential for large cost saving with minimal risk.

E. coli DNA may be detected more frequently in Crohn's granulomas than in other non-Crohn's bowel granulomas. This may indicate a tendency for lumenal bacteria to colonize inflamed tissue, or may be due to increased uptake of bacterial DNA by gut antigen presenting cells. In light of previous detection of M. paratuberculosis DNA in Crohn's granulomas, the nonspecific nature of the type of bacterial DNA present in granulomas is evidence against any one bacterium having a significant causative role in Crohn's disease.

Fas is a transmembrane receptor that can induce apoptosis after cross-linking with either agonistic antibodies or with Fas ligand (FasL). Although originally described as an important regulator of peripheral immune homeostasis, accumulating evidence suggests that the Fas/FasL system plays an important role in tumour development. In addition to its proapoptotic functions, accumulating evidence demonstrates that Fas can activate numerous nonapoptotic signalling pathways, and that activation of these pathways can result in increased tumourigenicity and metastasis. This review summarises the current understanding of the Fas/FasL system in tumorigenesis and discusses attempts to utilise the Fas/FasL system in the treatment of cancer.