Diagnosis and management of eosinophilic esophagitis in children: An update from the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN).

INTRODUCTION: Eosinophilic esophagitis (EoE) is a chronic inflammatory disease of the esophagus characterized by symptoms of esophageal dysfunction and histologically by predominantly eosinophilic infiltration of the squamous epithelium. European Society for Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) published a guideline in 2014; however, the rapid evolution of knowledge about pathophysiology, diagnostic criteria, and therapeutic options have made an update necessary. METHODS: A consensus group of pediatric gastroenterologists from the ESPGHAN Working Group on Eosinophilic Gastrointestinal Diseases (ESPGHAN EGID WG) reviewed the recent literature and proposed statements and recommendations on 28 relevant questions about EoE. A comprehensive electronic literature search was performed in MEDLINE, EMBASE, and Cochrane databases from 2014 to 2022. The Grading of Recommendations Assessment, Development and Evaluation system was used to assess the quality of evidence and formulate recommendations. RESULTS: A total of 52 statements based on the available evidence and 44 consensus-based recommendations are available. A revision of the diagnostic protocol, options for initial drug treatment, and the new concept of simplified empiric elimination diets are now available. Biologics are becoming a part of the potential armamentarium for refractory EoE, and systemic steroids may be considered as the initial treatment for esophageal strictures before esophageal dilation. The importance and assessment of quality of life and a planned transition to adult medical care are new areas addressed in this guideline. CONCLUSION: Research in recent years has led to a better understanding of childhood EoE. This guideline incorporates the new findings and provides a practical guide for clinicians treating children diagnosed with EoE.

Differences in Management of Eosinophilic Esophagitis in Europe: An Assessment of Current Practice.

OBJECTIVES: The aim of the study was to assess differences in the diagnosis and management of eosinophilic esophagitis (EoE) by European pediatric (PG) and adult gastroenterologists (AG), and their self-reported adherence to guidelines. METHODS: A multiple-choice questionnaire gauged the diagnostic and management strategies of gastroenterologists treating children or adults in 14 European countries and the United Arab Emirates (UAE). RESULTS: Questionnaires were completed by 465 PG and 743 AG. PG were significantly more likely to take biopsies in patients with symptoms of esophageal dysfunction (86.2% PG vs 75.4% AG, P < 0.001) and to perform endoscopic follow-up (86.3% PG vs 80.6% AG, P < 0.001). After failure of proton-pump inhibitors (PPIs), topical steroids were the preferred second-line therapy; however, PG opted more frequently for elimination diets (47.5% PG vs 13.7% AG, P < 0.001). More PG than AG indicated having read recent guidelines (89.4% PG vs 58.2% AG, P < 0.001). Geographic differences in practice were reported, with respondents from the United Kingdom, Portugal, and Spain more often adhering to recommended biopsy protocols. Physicians in the UAE, France, Lithuania, and Poland tended to opt for steroid therapy or elimination diets as first-line therapy, in contrast to most other countries. CONCLUSIONS: Significant differences in general practice between PG and AG were demonstrated with notable divergence from consensus guidelines. International practice variations are also apparent. Among other strategies, educational activities to highlight current recommendations may help harmonize and optimize clinical practice.

Pediatric Eosinophilic Esophagitis: Results of the European Retrospective Pediatric Eosinophilic Esophagitis Registry (RetroPEER).

OBJECTIVES: Recommendations for diagnosing and treating eosinophilic esophagitis (EoE) are evolving; however, information on real world clinical practice is lacking. To assess the practices of pediatric gastroenterologists diagnosing and treating EoE and to identify the triggering allergens in European children. METHODS: Retrospective anonymized data were collected from 26 European pediatric gastroenterology centers in 13 countries. Inclusion criteria were: Patients diagnosis with EoE, completed investigations prescribed by the treating physician, and were on stable medical or dietary interventions. RESULTS: In total, 410 patients diagnosed between December 1999 and June 2016 were analyzed, 76.3% boys. The time from symptoms to diagnosis was 12 ±â€Š33.5 months and age at diagnosis was 8.9 ±â€Š4.75 years. The most frequent indications for endoscopy were: dysphagia (38%), gastroesophageal reflux (31.2%), bolus impaction (24.4%), and failure to thrive (10.5%). Approximately 70.3% had failed proton pump inhibitor treatment. The foods found to be causative of EoE by elimination and rechallenge were milk (42%), egg (21.5%), wheat/gluten (10.9%), and peanut (9.9%). Elimination diets were used exclusively in 154 of 410 (37.5%), topical steroids without elimination diets in 52 of 410 (12.6%), both diet and steroids in 183 of 410 (44.6%), systemic steroids in 22 of 410 (5.3%), and esophageal dilation in 7 of 410 (1.7%). Patient refusal, shortage of endoscopy time, and reluctance to perform multiple endoscopies per patient were noted as factors justifying deviation from guidelines. CONCLUSIONS: In this "real world" pediatric European cohort, milk and egg were the most common allergens triggering EoE. Although high-dose proton pump inhibitor trials have increased, attempted PPI treatment is not universal.

The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory.

Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Networks (DANN) are very popular in our days. Depending on the learning task, the exact form of DANNs is determined via their multi-layer architecture, activation functions and the so-called loss function. However, for a majority of deep learning approaches based on DANNs, the kernel structure of neural signal processing remains the same, where the node response is encoded as a linear superposition of neural activity, while the non-linearity is triggered by the activation functions. In the current paper, we suggest to analyze the neural signal processing in DANNs from the point of view of homogeneous chaos theory as known from polynomial chaos expansion (PCE). From the PCE perspective, the (linear) response on each node of a DANN could be seen as a 1st degree multi-variate polynomial of single neurons from the previous layer, i.e. linear weighted sum of monomials. From this point of view, the conventional DANN structure relies implicitly (but erroneously) on a Gaussian distribution of neural signals. Additionally, this view revels that by design DANNs do not necessarily fulfill any orthogonality or orthonormality condition for a majority of data-driven applications. Therefore, the prevailing handling of neural signals in DANNs could lead to redundant representation as any neural signal could contain some partial information from other neural signals. To tackle that challenge, we suggest to employ the data-driven generalization of PCE theory known as arbitrary polynomial chaos (aPC) to construct a corresponding multi-variate orthonormal representations on each node of a DANN. Doing so, we generalize the conventional structure of DANNs to Deep arbitrary polynomial chaos neural networks (DaPC NN). They decompose the neural signals that travel through the multi-layer structure by an adaptive construction of data-driven multi-variate orthonormal bases for each layer. Moreover, the introduced DaPC NN provides an opportunity to go beyond the linear weighted superposition of single neurons on each node. Inheriting fundamentals of PCE theory, the DaPC NN offers an additional possibility to account for high-order neural effects reflecting simultaneous interaction in multi-layer networks. Introducing the high-order weighted superposition on each node of the network mitigates the necessity to introduce non-linearity via activation functions and, hence, reduces the room for potential subjectivity in the modeling procedure. Although the current DaPC NN framework has no theoretical restrictions on the use of activation functions. The current paper also summarizes relevant properties of DaPC NNs inherited from aPC as analytical expressions for statistical quantities and sensitivity indexes on each node. We also offer an analytical form of partial derivatives that could be used in various training algorithms. Technically, DaPC NNs require similar training procedures as conventional DANNs, and all trained weights determine automatically the corresponding multi-variate data-driven orthonormal bases for all layers of DaPC NN. The paper makes use of three test cases to illustrate the performance of DaPC NN, comparing it with the performance of the conventional DANN and also with plain aPC expansion. Evidence of convergence over the training data size against validation data sets demonstrates that the DaPC NN outperforms the conventional DANN systematically. Overall, the suggested re-formulation of the kernel network structure in terms of homogeneous chaos theory is not limited to any particular architecture or any particular definition of the loss function. The DaPC NN Matlab Toolbox is available online and users are invited to adopt it for own needs.

Inference of affordances and active motor control in simulated agents.

Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of such behavior from a computational neuroscience perspective. Based on the theory, we introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture, which processes sensorimotor information, infers behavior-relevant aspects of its world, and invokes highly flexible, goal-directed behavior. We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps. That is, the emerging latent states signal which actions lead to which effects dependent on the local context. In combination with active inference, we show that flexible, goal-directed behavior can be invoked, incorporating the emerging affordance maps. As a result, our simulated agent flexibly steers through continuous spaces, avoids collisions with obstacles, and prefers pathways that lead to the goal with high certainty. Additionally, we show that the learned agent is highly suitable for zero-shot generalization across environments: After training the agent in a handful of fixed environments with obstacles and other terrains affecting its behavior, it performs similarly well in procedurally generated environments containing different amounts of obstacles and terrains of various sizes at different locations.

Partial enteral nutrition has no benefit on bone health but improves growth in paediatric patients with quiescent or mild Crohn's disease.

BACKGROUND AND AIMS: Exclusive enteral nutrition induces remission, improves bone health and growth in paediatric Crohn's disease (CD) patients, but is highly demanding for patients. We investigated efficacy of partial enteral nutrition (PEN) on bone health, growth and course in CD patients and assessed microbial and metabolic changes induced by PEN. METHODS: We performed a two centre, non-randomized controlled intervention study in quiescent CD patients aged <19 years. Patients in intervention group received a liquid formula providing ~25% of daily energy for one year. At baseline, after 3, 6, 9 and 12 months, we collected data on bone, muscle (peripheral quantitative computertomography), anthropometry, disease activity (weighted paediatric CD activity index), metabolomic profile (liquid chromatography mass spectrometry), and faecal microbiome (16S rRNA gene sequencing). RESULTS: Of 41 CD patients, 22 received the intervention (PEN) (mean age 15.0 ± 1.9 years, 50% male), 19 served as controls (non-PEN) (12.8 ± 3.1 years, 58% male). At baseline, mean bone quality was comparable to reference population with no improvement during the intervention. Relapse rate was low (8/41, PEN 4/22 and non-PEN 4/19, ns). PEN was not associated with microbiota community changes (beta diversity) but significantly reduced species diversity. Metabolome changes with upregulation of phosphatidylcholines in PEN patients are likely related to lipid and fatty acid composition of the formula. PEN significantly improved growth in a subgroup with Tanner stage 1-3. CONCLUSION: In our cohort of paediatric CD patients, PEN did not affect bone health but improved growth in patients with a potential to grow.

Learning, planning, and control in a monolithic neural event inference architecture.

We introduce REPRISE, a REtrospective and PRospective Inference SchEme, which learns temporal event-predictive models of dynamical systems. REPRISE infers the unobservable contextual event state and accompanying temporal predictive models that best explain the recently encountered sensorimotor experiences retrospectively. Meanwhile, it optimizes upcoming motor activities prospectively in a goal-directed manner. Here, REPRISE is implemented by a recurrent neural network (RNN), which learns temporal forward models of the sensorimotor contingencies generated by different simulated dynamic vehicles. The RNN is augmented with contextual neurons, which enable the encoding of distinct, but related, sensorimotor dynamics as compact event codes. We show that REPRISE concurrently learns to separate and approximate the encountered sensorimotor dynamics: it analyzes sensorimotor error signals adapting both internal contextual neural activities and connection weight values. Moreover, we show that REPRISE can exploit the learned model to induce goal-directed, model-predictive control, that is, approximate active inference: Given a goal state, the system imagines a motor command sequence optimizing it with the prospective objective to minimize the distance to the goal. The RNN activities thus continuously imagine the upcoming future and reflect on the recent past, optimizing the predictive model, the hidden neural state activities, and the upcoming motor activities. As a result, event-predictive neural encodings develop, which allow the invocation of highly effective and adaptive goal-directed sensorimotor control.

Health effects of expressive writing on stressful or traumatic experiences - a meta-analysis.

In his theory of emotional inhibition Pennebaker [44] proclaimed that the disclosure of stressful or traumatic experiences reduces the probability of detrimental health effects. In his experimental paradigm disclosure was induced by asking the participants to write about their deepest thoughts and feelings relating to a stressful event during 3 to 4 writing sessions of 15 to 20 minutes. Based on a meta-analysis of 13 studies Smyth [58] reported an average effect size of d=0.47 for various health related variables. Considering the great number of studies published since then, the aim of our study was to update the state of evidence regarding the effects of expressive writing on health, including only randomized controlled trials in our analysis. From 42 trials fulfilling the inclusion criteria 30 could be used for the meta-analysis. Neither regarding somatic nor psychological health variables significant effect sizes were found. Various exploratory analyses (e.g. restriction to clinical samples) also resulted in non-significant effect sizes, except for one rendering a very small effect size. Results of our meta-analysis lead to the conclusion that expressive writing has minor or no effects on the subject's health contrary to earlier findings.