Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

Investigation into the reaction of reactive dyes with carboxylate salts and the application of carboxylate-modified reactive dyes to cotton

Ink-jet printing of cellulosic fabrics with reactive dyes typically requires that the fabric is pretreated with alkali, prior to printing, to facilitate efficient fixation of the dye. In this paper we evaluate the use of sodium formate and other carboxylate salts as a neutral (pH 6.5) pretreatment process. The thickened, prepared-for-print pad liquor contained at least 50 gdm⁻³ of the selected carboxylate salt and was applied to the cotton fabrics by a pad-dry procedure. The fabric was then ink-jet printed with reactive dye inks, followed by standard steaming and washing-off processes. The pH of the carboxylate salt pretreatment was 6.5 and the aqueous extracts from the print fabrics at the end of the steaming process remained at pH 6.5. It was observed that even at pH 6.5, in the presence of selected carboxylates, significant reactive dye fixation could be achieved on a cotton substrate, whereas in the absence of the carboxylate, very little or even zero fixation was achieved. Infrared and capillary electrophoresis analyses of model reactions of reactive dyes with the carboxylate salts indicated that reactive ester residues were formed, and which subsequently promoted reaction with the cellulosic substrates. In addition to improving reactive dye fixation in ink-jet printing, the carboxylate-modified dyes were also demonstrated to improve long-liquor dyeing properties on cotton substrates. As an extension of this carboxylate-based printing process, the incorporation of lithium acetate (100 gdm⁻³) into the ink formulation was further studied and it was demonstrated that the necessity for a preparative pretreatment process could be eliminated

Development of a novel three‐dimensional printing technology for the application of “raised” surface features

A simple procedure to ink-jet print raised images using two water-soluble inorganic inks is reported and it has the potential to be utilised in domestic and commercial environments. The advantages of such a procedure lies in the ability to print moulded objects, Braille type and to engineer special gonio-specific effects that may have value in the security printing area. The study focuses on printing gypsum through the ready precipitation of calcium sulphate dihydrate by co-jetting calcium chloride and ammonium sulphate solutions. The results in this preliminary study are encouraging and offer a potential method for durable surface structuring of material surfaces with haptic and visual effects for both the blind and the sighted

Feature visualisation of classification of diabetic retinopathy using a convolutional neural network

Convolutional Neural Networks (CNNs) have been demonstrated to achieve state-of-the-art results on complex computer vision tasks, including medical image diagnosis of Diabetic Retinopathy (DR). CNNs are powerful because they determine relevant image features automatically. However, the current inability to demonstrate what these features are has led to CNNs being considered to be 'black box' methods whose results should not be trusted. This paper presents a method for identifying the learned features of a CNN and applies it in the context of the diagnosis of DR in fundus images using the well-known DenseNet. We train the CNN to diagnose and determine the severity of DR and then successfully extract feature maps from the CNN which identify the regions and features of the images which have led most strongly to the CNN prediction. This feature extraction process has great potential, particularly for encouraging confidence in CNN approaches from users and clinicians, and can aid in the further development of CNN methods. There is also potential for determining previously unidentified features which may contribute to a classification

Supercritical carbon dioxide (SC-CO₂) dyeing of cellulose acetate: An opportunity for a “greener” circular textile economy

This article compares the dyeing of cellulose diacetate (cellulose-based) and polyester fabrics using supercritical carbon dioxide (SC-CO₂) and aqueous media. The benefits of dyeing in SC-CO₂ were clearly demonstrated in laboratory-based and pilot-scale studies in terms of increased colour strength, uniformity, fastness and the absence of auxiliaries such as dispersing agents or surfactants. In addition, the “super-levelling” nature of the SC-CO₂ medium was demonstrated in the reprocessing of polyester “waste textile” and the re-use of the “locked-in waste” colourant. The SC-CO₂ processing medium can be utilised to accurately colour “multiple life” polyester and cellulose acetate uniformly and to creatively tie-dye polyester and cellulose acetate fabrics. Through SC-CO₂ fluid technology, we can envisage a viable waterless circular manufacturing and recycling/remanufacturing framework for the predominantly polyester global fibre market coupled to the sustainably sourced, biodegradable cellulose diacetate as a replacement for cotton. The key technical and commercial advantages being the use of a single solvent dye class for both polyester and the cellulose diacetate, saving on energy costs, integrated simpler processing, reduced water usage and associated efficient recycling. Further, repositioning the cellulosic fibre industry towards using sustainable forests is attractive in terms of improved land, water and environmental management

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Boundaries of Semantic Distraction: Dominance and Lexicality Act at Retrieval

Three experiments investigated memory for semantic information with the goal of determining boundary conditions for the manifestation of semantic auditory distraction. Irrelevant speech disrupted the free recall of semantic category-exemplars to an equal degree regardless of whether the speech coincided with presentation or test phases of the task (Experiment 1) and occurred regardless of whether it comprised random words or coherent sentences (Experiment 2). The effects of background speech were greater when the irrelevant speech was semantically related to the to-be-remembered material, but only when the irrelevant words were high in output dominance (Experiment 3). The implications of these findings in relation to the processing of task material and the processing of background speech is discussed

Implications of sperm banking for health-related quality of life up to 1 year after cancer diagnosis.

Sperm banking is recommended for all men diagnosed with cancer where treatment is associated with risk of long-term gonadatoxicity, to offer the opportunity of fatherhood and improved quality of life. However, uptake of sperm banking is lower than expected and little is known about why men refuse. Our aims were to determine: (i) demographic and medical variables associated with decisions about banking and (ii) differences in quality of life between bankers and non-bankers at diagnosis (Time 1 (T1)) and 1 year later (Time 2 (T2))

Self-management skills in adolescents with chronic rheumatic disease: A cross-sectional survey

<p>Abstract</p> <p>Background</p> <p>For adolescents with a diagnosis of lifelong chronic illness, mastery of self-management skills is a critical component of the transition to adult care. This study aims to examine self-reported medication adherence and self-care skills among adolescents with chronic rheumatic disease.</p> <p>Methods</p> <p>Cross-sectional survey of 52 adolescent patients in the Pediatric Rheumatology Clinic at UCSF. Outcome measures were self-reported medication adherence, medication regimen knowledge and independence in health care tasks. Predictors of self-management included age, disease perception, self-care agency, demographics and self-reported health status. Bivariate associations were assessed using the Student's t-test, Wilcoxon rank sum test and Fisher exact test as appropriate. Independence in self-management tasks were compared between subjects age 13-16 and 17-20 using the chi-squared test.</p> <p>Results</p> <p>Subjects were age 13-20 years (mean 15.9); 79% were female. Diagnoses included juvenile idiopathic arthritis (44%), lupus (35%), and other rheumatic conditions (21%). Mean disease duration was 5.3 years (SD 4.0). Fifty four percent reported perfect adherence to medications, 40% reported 1-2 missed doses per week, and 6% reported missing 3 or more doses. The most common reason for missing medications was forgetfulness. Among health care tasks, there was an age-related increase in ability to fill prescriptions, schedule appointments, arrange transportation, ask questions of doctors, manage insurance, and recognize symptoms of illness. Ability to take medications as directed, keep a calendar of appointments, and maintain a personal medical file did not improve with age.</p> <p>Conclusions</p> <p>This study suggests that adolescents with chronic rheumatic disease may need additional support to achieve independence in self-management.</p