Laxatives do not improve symptoms of opioid-induced constipation: results of a patient survey

Introduction: Laxatives are commonly used to treat opioid-induced constipation, the commonest and most bothersome complication of opioids. However, laxatives have a non-specific action and do not target underlying mechanisms of opioid-induced constipation; their use is associated with abdominal symptoms that negatively impact quality of life. Objective: To assess the effects of laxatives in patients taking opioids for chronic pain. Methods: 198 UK patients who had taken opioid analgesics for at least one month completed a cross-sectional online or telephone survey. Questions addressed their pain condition and medication, and laxative use (including efficacy and side-effects). The survey also assessed bowel function using the Bowel Function Index. Results: Since starting their current opioid, 134/184 patients (73%) had used laxatives at some point and 122 (91%) of these were currently taking them. The most common laxatives were osmotics and stimulants. Laxative side-effects were reported in 75%, most commonly gas, bloating/fullness and a sudden urge to defecate. Side-effects were more common in patients <40 years old. Approximately half of patients said laxatives interfered with work and social activities, and one-fifth had needed an overnight hospital stay because of their pain condition and/or constipation. Laxatives did not improve the symptoms of constipation, as assessed by the Bowel Function Index. Constipation was not related to opioid strength or dose of opioid or number of laxatives taken. Conclusions: Use of laxatives to treat opioid-induced constipation is often ineffective and associated with side-effects. Instead of relieving the burden of opioid-induced constipation, laxative use is associated with a negative impact

NetLSD: Hearing the Shape of a Graph

Comparison among graphs is ubiquitous in graph analytics. However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation. Ideally, graph comparison should be invariant to the order of nodes and the sizes of compared graphs, adaptive to the scale of graph patterns, and scalable. Unfortunately, these properties have not been addressed together. Graph comparisons still rely on direct approaches, graph kernels, or representation-based methods, which are all inefficient and impractical for large graph collections. In this paper, we propose the Network Laplacian Spectral Descriptor (NetLSD): the first, to our knowledge, permutation- and size-invariant, scale-adaptive, and efficiently computable graph representation method that allows for straightforward comparisons of large graphs. NetLSD extracts a compact signature that inherits the formal properties of the Laplacian spectrum, specifically its heat or wave kernel; thus, it hears the shape of a graph. Our evaluation on a variety of real-world graphs demonstrates that it outperforms previous works in both expressiveness and efficiency.Comment: KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 19--23, 2018, London, United Kingdo

Weighted norm inequalities for de Branges--Rovnyak spaces and their applications

Let $\mathcal{H}(b)$ denote the de Branges--Rovnyak space associated with a function $b$ in the unit ball of $H^\infty(\mathbb{C}_+)$. We study the boundary behavior of the derivatives of functions in $\mathcal{H}(b)$ and obtain weighted norm estimates of the form $\|f^{(n)}\|_{L^2(\mu)} \le C\|f\|_{\mathcal{H}(b)}$, where $f \in \mathcal{H}(b)$ and $\mu$ is a Carleson-type measure on $\mathbb{C}_+\cup\mathbb{R}$. We provide several applications of these inequalities. We apply them to obtain embedding theorems for $\mathcal{H}(b)$ spaces. These results extend Cohn and Volberg--Treil embedding theorems for the model (star-invariant) subspaces which are special classes of de Branges--Rovnyak spaces. We also exploit the inequalities for the derivatives to study stability of Riesz bases of reproducing kernels $\{k^b_{\lambda_n}\}$ in $\mathcal{H}(b)$ under small perturbations of the points $\lambda_n$

VERSE: Versatile Graph Embeddings from Similarity Measures

Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good results as the non-scalable full variant.Comment: In WWW 2018: The Web Conference. 10 pages, 5 figure

Graph Clustering with Graph Neural Networks

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. In this paper, we study unsupervised training of GNN pooling in terms of their clustering capabilities. We start by drawing a connection between graph clustering and graph pooling: intuitively, a good graph clustering is what one would expect from a GNN pooling layer. Counterintuitively, we show that this is not true for state-of-the-art pooling methods, such as MinCut pooling. To address these deficiencies, we introduce Deep Modularity Networks (DMoN), an unsupervised pooling method inspired by the modularity measure of clustering quality, and show how it tackles recovery of the challenging clustering structure of real-world graphs. In order to clarify the regimes where existing methods fail, we carefully design a set of experiments on synthetic data which show that DMoN is able to jointly leverage the signal from the graph structure and node attributes. Similarly, on real-world data, we show that DMoN produces high quality clusters which correlate strongly with ground truth labels, achieving state-of-the-art results

Oropharyngeal dysphagia management in cervical spinal cord injury patients : an exploratory survey of variations to care across specialised and non-specialised units

Study design: A multi-centre online survey to staff working in specialised and non-specialised acute units. Objectives: To identify clinical decisions and practices made for acute cervical spinal cord injury (CSCI) patients with respiratory impairments and oropharyngeal dysphagia. Settings: All hospital intensive care units in the UK that admit acute cervical spinal cord injury patients. Methods: Online distribution of a 35-question multiple-choice survey on the clinical management of ventilation, swallowing, nutrition, oral hygiene and communication for CSCI patients, to multi-disciplinary staff based in specialised and non-specialised intensive care units across UK. Results: Responses were received from 219 staff members based in 92 hospitals. Of the 77 units that admitted CSCI patients, 152 participants worked in non-specialised and 30 in specialised units. Non-specialised unit staff showed variations in clinical decisions for respiratory management compared to specialised units with limited use of vital capacity measures and graduated weaning programme, reliance on coughing to indicate aspiration, inconsistent manipulation of tracheostomy cuffs for speech and swallowing and limited use of instrumental assessments of swallowing. Those in specialised units employed a multi-discplinary approach to clinical management of nutritional needs. Conclusions: Variation in the clinical management of respiratory impairments and oropharyngeal dysphagia between specialised and non-specialised units have implications for patient outcomes and increase the risk of respiratory complications that impact mortality. The future development of clinical guidance is required to ensure best practice and consistent care across all units

The experiences of individuals with cervical spinal cord injury and their family during post-injury care in non-specialised and specialised units in UK

Background: Individuals with acute cervical spinal cord injury require specialised interventions to ensure optimal clinical outcomes especially for respiratory, swallowing and communication impairments. This study explores the experiences of post-injury care for individuals with cervical spinal cord injury and their family members during admissions in specialised and non-specialised units in the United Kingdom. Methods: Semi-structured interviews were undertaken with individuals with a cervical spinal cord injury and their family member, focussing on the experience of care across units. Eight people with spinal cord injury levels from C2 to C6, were interviewed in their current care settings. Six participants had family members present to support them. Interviews were audio-recorded and transcribed with data inputted into NVivo for thematic analysis. Results: The study identified six themes from the participant interviews that highlighted different experiences of care in non-specialised and specialised settings. A number of these were related to challenges with the system, whilst others were about the personal journey of recovery. The themes were titled as: adjustment, transitions, “the golden opportunity”, “when you can’t eat”, communication, and “in the hands of the nurses and doctors”. Conclusions: Whilst participants reported being well cared for in non-specialised units, they felt that they did not receive specialist care and this delayed their rehabilitation. Participants were dependent on healthcare professionals for information and care and at times lost hope for recovery. Staff in non-specialised units require training and guidance to help provide support for those with dysphagia and communication difficulties, as well as reassurance to patients and families whilst they wait for transfer to specialised units

Spectral Graph Complexity

We introduce a spectral notion of graph complexity derived from the Weyl's law. We experimentally demonstrate its correlation to how well the graph can be embedded in a low-dimensional Euclidean space.Comment: BigNet workshop at the Web conferece'201