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| 14. |
- Moayyedi, P., et al.
(författare)
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Evidence-based and mechanistic insights into exclusion diets for IBS
- 2020
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Ingår i: Nature Reviews Gastroenterology & Hepatology. - : Springer Science and Business Media LLC. - 1759-5045 .- 1759-5053. ; 17, s. 406-413
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Tidskriftsartikel (refereegranskat)abstract
- Exclusion diets are popular in the management of irritable bowel syndrome (IBS). This Review outlines the mechanisms by which food might lead to symptoms in IBS and discusses different approaches to how exclusion diets can be evaluated in randomized controlled trials. Exclusion diets are becoming increasingly popular in the management of irritable bowel syndrome (IBS). Several mechanisms exist by which food items might cause gastrointestinal symptoms, such as direct osmotic effects of food in the gut lumen, changes to the gut microbiota and immune activation. These effects have been demonstrated in animal models and in human studies, particularly in the case of gluten-free diets and diets low in fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAPs). Indeed, randomized controlled trials (RCTs) suggest that gluten-free diets and low-FODMAP diets improve IBS symptoms, and guidelines recommend the latter approach for treating symptoms in some patients with IBS. Designing such RCTs is challenging as participants need to eat so an 'inert' placebo is not an option. Blinding is also an issue with these studies; in the future, new exclusion diets should not advertise what the diet consists of until it is proved to reduce symptoms. In this Review, we outline the advantages and disadvantages of each choice of control group and emphasize the importance of collecting mechanistic data (regarding direct effects of food on the gut lumen, changes in gut microbiota and intestinal inflammation) as well as symptom data in RCTs of exclusion diets in IBS.
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- Flook, N. W., et al.
(författare)
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Acid-Suppressive Therapy With Esomeprazole for Relief of Unexplained Chest Pain in Primary Care: A Randomized, Double-Blind, Placebo-Controlled Trial
- 2013
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Ingår i: American Journal of Gastroenterology. - : Ovid Technologies (Wolters Kluwer Health). - 0002-9270. ; 108:1, s. 56-64
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: High-quality data regarding the efficacy of acid-suppressive treatment for unexplained chest pain are lacking. The aim of this study was to evaluate the efficacy of esomeprazole in primary-care treatment of patients with unexplained chest pain stratified for frequency of reflux/regurgitation symptoms. METHODS: Patients with a ≥2-week history of unexplained chest pain (unrelated to gastroesophageal reflux) who had at least moderate pain on ≥2 of the last 7 days were stratified by heartburn/regurgitation frequency (≤1 day/week (stratum 1) vs. ≥2 days/week (stratum 2)) and randomized to 4 weeks of double-blind treatment with twice-daily esomeprazole 40mg or placebo. Chest pain relief during the last 7 days of treatment (≤1 day with minimal symptoms assessed daily using a 7-point scale) was analyzed by stratum in keeping with the predetermined analysis plan. RESULTS: Overall, 599 patients (esomeprazole: 297, placebo: 302) were randomized. In stratum 1, more esomeprazole than placebo recipients achieved chest pain relief (38.7% vs. 25.5%; P=0.018); no between-treatment difference was observed in stratum 2 (27.2% vs. 24.2%; P=0.54). However, esomeprazole was superior to placebo in a post-hoc analysis of the whole study population (combined strata; 33.1% vs. 24.9%; P=0.035). CONCLUSIONS: A 4-week course of high-dose esomeprazole provided statistically significant relief of unexplained chest pain in primary-care patients who experienced infrequent or no heartburn/regurgitation, but there was no such significant reduction in patients with more frequent reflux symptoms.
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| 17. |
- He, Qing, 1979-, et al.
(författare)
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A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction
- 2020
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8716 .- 1558-0008. ; 38:10, s. 2271-2283
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Tidskriftsartikel (refereegranskat)abstract
- Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The prediction of network traffic at the user level is particularly challenging, because the traffic characteristics emerge from a complex interaction of user level and application protocol behavior. In this work we address the problem of predicting the network traffic at the user level over a short horizon, motivated by its applications in cellular scheduling. Motivated by recent works on robust adversarial learning, we treat the prediction problem for non-stationary traffic in an adversarial context, and propose a meta-learning scheme that consists of a set of predictors, each optimized to predict a particular kind of traffic, and of a master policy that is trained for choosing the best fit predictor dynamically based on recent prediction performance, using deep reinforcement learning. We evaluate the proposed meta-learning scheme on a variety of traffic traces consisting of video and non-video traffic. Our results show that it consistently outperforms state-of-the-art predictors, and can adapt to before unseen traffic without the need for retraining the individual predictors.
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