Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures

In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a “null model” of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets

Type 2 Diabetes, Metabolic traits and Risk of Heart Failure:a Mendelian Randomization study

OBJECTIVE: The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes (T2D), glycemic traits, and risk of heart failure (HF). RESEARCH DESIGN AND METHODS: Summary-level data were obtained from genome-wide association studies of T2D, insulin resistance (IR), glycated hemoglobin, fasting insulin and glucose, and HF. MR was conducted using the inverse-variance weighted method. Sensitivity analyses included the MR-Egger method, weighted median and mode methods, and multivariable MR conditioning on potential mediators. RESULTS: Genetic liability to T2D was causally related to higher risk of HF (odds ratio [OR] 1.13 per 1-log unit higher risk of T2D; 95% CI 1.11-1.14; P < 0.001); however, sensitivity analysis revealed evidence of directional pleiotropy. The relationship between T2D and HF was attenuated when adjusted for coronary disease, BMI, LDL cholesterol, and blood pressure in multivariable MR. Genetically instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1-log unit higher risk of IR; 95% CI 1.00-1.41; P = 0.041). There were no notable associations identified between fasting insulin, glucose, or glycated hemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of T2D (OR 1.49; 95% CI 1.01-2.19; P = 0.042), although again with evidence of pleiotropy. CONCLUSIONS: These findings suggest a possible causal role of T2D and IR in HF etiology, although the presence of both bidirectional effects and directional pleiotropy highlights potential sources of bias that must be considered

Type 2 Diabetes, Metabolic Traits, and Risk of Heart Failure: A Mendelian Randomization Study

OBJECTIVE: The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes (T2D), glycemic traits, and risk of heart failure (HF). RESEARCH DESIGN AND METHODS: Summary-level data were obtained from genome-wide association studies of T2D, insulin resistance (IR), glycated hemoglobin, fasting insulin and glucose, and HF. MR was conducted using the inverse-variance weighted method. Sensitivity analyses included the MR-Egger method, weighted median and mode methods, and multivariable MR conditioning on potential mediators. RESULTS: Genetic liability to T2D was causally related to higher risk of HF (odds ratio [OR] 1.13 per 1-log unit higher risk of T2D; 95% CI 1.11-1.14; P < 0.001); however, sensitivity analysis revealed evidence of directional pleiotropy. The relationship between T2D and HF was attenuated when adjusted for coronary disease, BMI, LDL cholesterol, and blood pressure in multivariable MR. Genetically instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1-log unit higher risk of IR; 95% CI 1.00-1.41; P = 0.041). There were no notable associations identified between fasting insulin, glucose, or glycated hemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of T2D (OR 1.49; 95% CI 1.01-2.19; P = 0.042), although again with evidence of pleiotropy. CONCLUSIONS: These findings suggest a possible causal role of T2D and IR in HF etiology, although the presence of both bidirectional effects and directional pleiotropy highlights potential sources of bias that must be considered

C–H olefination of tryptophan residues in peptides: control of residue selectivity and peptide–amino acid cross-linking

There is high demand for new methods to modify peptides, for application in drug discovery and biomedicine. A C–H functionalization protocol for the olefination of tryptophan residues in peptides is described. The modification is successful for Trp residues at any position in the peptide, has broad scope in the styrene coupling partner, and offers opportunities for conjugating peptides with other biomolecules. For peptides containing both Trp and Phe, directing group manipulation enables full control of residue selectivity

Software Sustainability: The Modern Tower of Babel

The development of sustainable software has been identified as one of the key challenges in the field of computational science and engineering. However, there is currently no agreed definition of the concept. Current definitions range from a composite, non-functional requirement to simply an emergent property. This lack of clarity leads to confusion, and potentially to ineffective and inefficient efforts to develop sustainable software systems. The aim of this paper is to explore the emerging definitions of software sustainability from the field of software engineering in order to contribute to the question, what is software sustainability? The preliminary analysis suggests that the concept of software sustainability is complex and multifaceted with any consensus towards a shared definition within the field of software engineering yet to be achieved

Development of an intervention to support the implementation of evidence-based strategies for optimising antibiotic prescribing in general practice.

BACKGROUND: Trials show that antimicrobial stewardship (AMS) strategies, including communication skills training, point-of-care C-reactive protein testing (POC-CRPT) and delayed prescriptions, help optimise antibiotic prescribing and use in primary care. However, the use of these strategies in general practice is limited and inconsistent. We aimed to develop an intervention to enhance uptake and implementation of these strategies in primary care. METHODS: We drew on the Person-Based Approach to develop an implementation intervention in two stages. (1) Planning and design: We defined the problem in behavioural terms drawing on existing literature and conducting primary qualitative research (nine focus groups) in high-prescribing general practices. We identified 'guiding principles' with intervention objectives and key features and developed logic models representing intended mechanisms of action. (2) Developing the intervention: We created prototype intervention materials and discussed and refined these with input from 13 health professionals and 14 citizens in two sets of design workshops. We further refined the intervention materials following think-aloud interviews with 22 health professionals. RESULTS: Focus groups highlighted uncertainties about how strategies could be used. Health professionals in the workshops suggested having practice champions, brief summaries of each AMS strategy and evidence supporting the AMS strategies, and they and citizens gave examples of helpful communication strategies/phrases. Think-aloud interviews helped clarify and shorten the text and user journey of the intervention materials. The intervention comprised components to support practice-level implementation: antibiotic champions, practice meetings with slides provided, and an 'implementation support' website section, and components to support individual-level uptake: website sections on each AMS strategy (with evidence, instructions, links to electronic resources) and material resources (patient leaflets, POC-CRPT equipment, clinician handouts). CONCLUSIONS: We used a systematic, user-focussed process of developing a behavioural intervention, illustrating how it can be used in an implementation context. This resulted in a multicomponent intervention to facilitate practice-wide implementation of evidence-based strategies which now requires implementing and evaluating. Focusing on supporting the uptake and implementation of evidence-based strategies to optimise antibiotic use in general practice is critical to further support appropriate antibiotic use and mitigate antimicrobial resistance

The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19

In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th-11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics-IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view

Principles of Experimental Design for Big Data Analysis

Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis.CCD was supported by an Australian Research Council’s Discovery Early Career Researcher Award funding scheme (DE160100741). CH would like to gratefully acknowledge support from the Medical Research Council (UK), the OxfordMAN Institute, and the EPSRC UK through the i-like Statistics programme grant. CCD, JMM and KM would like to acknowledge support from the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). Funding from the Australian Research Council for author KM is gratefully acknowledged

Anti-nausea effects and pharmacokinetics of ondansetron, maropitant and metoclopramide in a low-dose cisplatin model of nausea and vomiting in the dog: a blinded crossover study

Nausea is a subjective sensation which is difficult to measure in non-verbal species. The aims of this study were to determine the efficacy of three classes of antiemetic drugs in a novel low dose cisplatin model of nausea and vomiting and measure change in potential nausea biomarkers arginine vasopressin (AVP) and cortisol. A four period cross-over blinded study was conducted in eight healthy beagle dogs of both genders. Dogs were administered 18 mg/m2 cisplatin intravenously, followed 45 min later by a 15 min infusion of either placebo (saline) or antiemetic treatment with ondansetron (0.5 mg/kg; 5-HT3 antagonist), maropitant (1 mg/kg; NK1 antagonist) or metoclopramide (0.5 mg/kg; D2 antagonist). The number of vomits and nausea associated behaviours, scored on a visual analogue scale, were recorded every 15 min for 8 h following cisplatin administration. Plasma samples were collected to measure AVP, cortisol and antiemetic drug concentrations