Virtual patient design : exploring what works and why : a grounded theory study

Objectives: Virtual patients (VPs) are online representations of clinical cases used in medical education. Widely adopted, they are well placed to teach clinical reasoning skills. International technology standards mean VPs can be created, shared and repurposed between institutions. A systematic review has highlighted the lack of evidence to support which of the numerous VP designs may be effective, and why. We set out to research the influence of VP design on medical undergraduates. Methods: This is a grounded theory study into the influence of VP design on undergraduate medical students. Following a review of the literature and publicly available VP cases, we identified important design properties. We integrated them into two substantial VPs produced for this research. Using purposeful iterative sampling, 46 medical undergraduates were recruited to participate in six focus groups. Participants completed both VPs, an evaluation and a 1-hour focus group discussion. These were digitally recorded, transcribed and analysed using grounded theory, supported by computer-assisted analysis. Following open, axial and selective coding, we produced a theoretical model describing how students learn from VPs. Results: We identified a central core phenomenon designated ‘learning from the VP’. This had four categories: VP Construction; External Preconditions; Student–VP Interaction, and Consequences. From these, we constructed a three-layer model describing the interactions of students with VPs. The inner layer consists of the student's cognitive and behavioural preconditions prior to sitting a case. The middle layer considers the VP as an ‘encoded object’, an e-learning artefact and as a ‘constructed activity’, with associated pedagogic and organisational elements. The outer layer describes cognitive and behavioural change. Conclusions: This is the first grounded theory study to explore VP design. This original research has produced a model which enhances understanding of how and why the delivery and design of VPs influence learning. The model may be of practical use to authors, institutions and researchers

Adult attachment style and cortisol responses across the day in older adults.

The association between cortisol and adult attachment style, an important indicator of social relationships, has been relatively unexplored. Previous research has examined adult attachment and acute cortisol responses to stress in the laboratory, but less is known about cortisol levels in everyday life. The present study examined adult romantic attachment style and cortisol responses across the day. Salivary cortisol was collected at six time points during the course of the day in 1,807 healthy men and women from a subsample of the Whitehall II cohort. Significant associations were found between attachment on cortisol across the day and slope of cortisol decline. The lowest cortisol output was associated with fearful attachment, with preoccupied attachment having the highest levels and a flatter cortisol profile. The results tentatively support the proposition that attachment style may contribute to HPA dysregulation

Automating biomedical data science through tree-based pipeline optimization

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding

High-energy kink in high-temperature superconductors

In conventional metals, electron-phonon coupling, or the phonon-mediated interaction between electrons, has long been known to be the pairing interaction responsible for the superconductivity. The strength of this interaction essentially determines the superconducting transition temperature TC. One manifestation of electron-phonon coupling is a mass renormalization of the electronic dispersion at the energy scale associated with the phonons. This renormalization is directly observable in photoemission experiments. In contrast, there remains little consensus on the pairing mechanism in cuprate high temperature superconductors. The recent observation of similar renormalization effects in cuprates has raised the hope that the mechanism of high temperature superconductivity may finally be resolved. The focus has been on the low energy renormalization and associated "kink" in the dispersion at around 50 meV. However at that energy scale, there are multiple candidates including phonon branches, structure in the spin-fluctuation spectrum, and the superconducting gap itself, making the unique identification of the excitation responsible for the kink difficult. Here we show that the low-energy renormalization at ~50 meV is only a small component of the total renormalization, the majority of which occurs at an order of magnitude higher energy (~350 meV). This high energy kink poses a new challenge for the physics of the cuprates. Its role in superconductivity and relation to the low-energy kink remains to be determined.Comment: 13 pages, 4 figure

A hot cocoon in the ultralong GRB 130925A: hints of a PopIII-like progenitor in a low density wind environment

GRB 130925A is a peculiar event characterized by an extremely long gamma-ray duration ($\approx$7 ks), as well as dramatic flaring in the X-rays for $\approx$20 ks. After this period, its X-ray afterglow shows an atypical soft spectrum with photon index $\Gamma$$\sim$4, as observed by Swift and Chandra, until $\approx 10^7$ s, when XMM-Newton observations uncover a harder spectral shape with $\Gamma$$\sim$2.5, commonly observed in GRB afterglows. We find that two distinct emission components are needed to explain the X-ray observations: a thermal component, which dominates the X-ray emission for several weeks, and a non-thermal component, consistent with a typical afterglow. A forward shock model well describes the broadband (from radio to X-rays) afterglow spectrum at various epochs. It requires an ambient medium with a very low density wind profile, consistent with that expected from a low-metallicity blue supergiant (BSG). The thermal component has a remarkably constant size and a total energy consistent with those expected by a hot cocoon surrounding the relativistic jet. We argue that the features observed in this GRB (its ultralong duration, the thermal cocoon, and the low density wind environment) are associated with a low metallicity BSG progenitor and, thus, should characterize the class of ultralong GRBs.Comment: 6 pgs, 3 figs, fig1 revised, ApJL in pres

The differential contribution of tumour necrosis factor to thermal and mechanical hyperalgesia during chronic inflammation

Therapies directed against tumour necrosis factor (TNF) are effective for the treatment of rheumatoid arthritis and reduce pain scores in this condition. In this study, we sought to explore mechanisms by which TNF contributes to inflammatory pain in an experimental model of arthritis. The effects of an anti-TNF agent, etanercept, on behavioural pain responses arising from rat monoarthritis induced by complete Freund's adjuvant were assessed and compared with expression of TNF receptors (TNFRs) by dorsal root ganglion (DRG) cells at corresponding time points. Etanercept had no effect on evoked pain responses in normal animals but exerted a differential effect on the thermal and mechanical hyperalgesia associated with rat arthritis induced by complete Freund's adjuvant (CFA). Joint inflammation was associated with increased TNFR1 and TNFR2 expression on DRG cells, which was maintained throughout the time course of the model. TNFR1 expression was increased in neuronal cells of the DRG bilaterally after arthritis induction. In contrast, TNFR2 expression occurred exclusively on nonneuronal cells of the macrophage-monocyte lineage, with cell numbers increasing in a TNF-dependent fashion during CFA-induced arthritis. A strong correlation was observed between numbers of macrophages and the development of mechanical hyperalgesia in CFA-induced arthritis. These results highlight the potential for TNF to play a vital role in inflammatory hyperalgesia, both by a direct action on neurons via TNFR1 and by facilitating the accumulation of macrophages in the DRG via a TNFR2-mediated pathway