1,721,339 research outputs found
Emerging space for non-polyethene-glycol bowel preparations in inflammatory bowel disease-related colonoscopy: Veering toward better adherence and palatability
No full textPatients with inflammatory bowel diseases (IBDs) require repeated endoscopic evaluations over time by colonoscopy to weigh disease activity but also for different and additional indications (e.g., evaluation of postoperative recurrence, colorectal cancer surveillance). Colonoscopy, however, requires adequate bowel preparation to be of quality. The latter is achieved as long as the patient takes a certain amount of product to have a number of bowel movements suitable to clean the colon and allow optimal visualization of the mucosa during endoscopy. However, significant guidelines recommend preparations for patients with IBD not excelling in palatability. This recommendation originates from the fact that most of the studies conducted on bowel preparations in patients with IBD have been done with isosmolar preparations based on polyethylene glycol (PEG), for which, therefore, more safety data exist. As a result, the low-volume non-PEG preparations (e.g., magnesium citrate plus picosulphate, oral sulphate solutions) have been set aside for the whole range of warnings to be heeded because of their hyperosmolarity. New studies, however, are emerging, leaning in overall for a paradigm shift in this matter. Indeed, such non-PEG preparations seem to show a particularly encouraging and engaging safety profile when considering their broad potential for tolerability and patient preference. Indeed, such evidence is insufficient to indicate such preparations in all patients with IBD but may pave the way for those with remission or well-controlled disease. This article summarizes the central studies conducted in IBD settings using non-PEG preparations by discussing their results
Potential of traditional Chinese medicine in gastrointestinal disorders: Hericium erinaceus in chronic atrophic gastritis
No full textTraditional Chinese medicine (TCM) has been extensively explored with various naturally derived compounds as a potential therapeutic agent for chronic atrophic gastritis (CAG). In addition to the aspects discussed in the reviewed article, this invited commentary explores the initial available evidence on a fungus from TCM, Hericium erinaceus, in the context of CAG. Initial clinical data suggest the potential of this fungus in inducing clinical and histological improvements in patients with CAG, as well as a marked antimicrobial activity against Helicobacter pylori infection. Preclinical cellular evidence also indicates an antineoplastic role in gastric carcinogenesis, mediated by two components: Erinacine A and S. Further evidence is needed to propose this fungus as a potential complementary therapeutic approach for CAG
Evoluzione olocenica e dinamica insediativa antropica della piana costiera del Fiume Fortore (Italia Meridionale)
No full textElectron Beam Welding on Steel Plates: A Mathematical Predictive Model of Temperature Fields
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Evoluzione olocenica e dinamica insediativa antropica della piana costiera del Fiume Fortore (Italia Meridionale)
No full textEditorial: Challenges in inflammatory bowel disease: current, future and unmet needs, volume II
No full textNon-dissipative Propagation by Randomized Anti-symmetric Deep Graph Networks
No full textDeep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to the efficiency of their adaptive message-passing scheme between nodes. However, DGNs are typically afflicted by a distortion in the information flowing from distant nodes (i.e., over-squashing) that limit their ability to learn long-range dependencies. This reduces their effectiveness, since predictive problems may require to capture interactions at different, and possibly large, radii in order to be effectively solved. We focus on Anti-symmetric Deep Graph Networks (A-DGNs), a recently proposed neural architecture for learning from graphs. A-DGNs are designed based on stable and non-dissipative ordinary differential equations, with a key architectural design based on an anti-symmetric structure of the internal weights. In this paper, we investigate the merits of the resulting architectural bias by incorporating randomized internal connections in node embedding computations and by restricting the training algorithms to operate exclusively at the output layer. To empirically validate our approach, we conduct experiments on various graph benchmarks, demonstrating the effectiveness of the proposed approach in learning from graph data
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