Background And Aims: Current artificial intelligence (AI)-based solutions for capsule endoscopy (CE) interpretation are proprietary. We aimed to evaluate an AI solution trained on a specific CE system (Pillcam®, Medtronic) for the detection of angiectasias on images captured by a different proprietary system (MiroCam®, Intromedic).
Material And Methods: An advanced AI solution (Axaro®, Augmented Endoscopy), previously trained on Pillcam® small bowell images, was evaluated on independent datasets with more than 1200 Pillcam® and MiroCam® still frames (equally distributed, with or without angiectasias).
Objectives: Patients who develop upper gastrointestinal bleeding (UGIB) while in hospital appear to have a poor prognosis. Our study aims at analysing the difference in outcome between in-patients (IPs) and out-patients presenting with variceal and non-variceal UGIB.
Methods: We conducted a multicentre prospective study by collecting data about variceal and non-variceal UGIB cases through 46 hospitals in France between November 2017 and October 2018.
We describe a 79-year-old man with spondylodiscitis and unknown pathogen, treated with cefazolin and rifampicin. He developed a massive digestive hemorrhage. Prothrombin time was prolonged with severe vitamin-K-dependent clotting-factor deficiency.
View Article and Find Full Text PDFComplex structures derived from multiple tissue types are challenging to study in vivo, and our knowledge of how cells from different tissues are coordinated is limited. Model organisms have proven invaluable for improving our understanding of how chemical and mechanical cues between cells from two different tissues can govern specific morphogenetic events. Here we used Caenorhabditis elegans as a model system to show how cells from three different tissues are coordinated to give rise to the anterior lumen.
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