Do Live Writing/ Study Sessions for At-Risk Students, in an Online, Distance Learning Environment, Demonstrate the Same Benefits Documented for Face-to-Face Shared Writing Events?

A recent project focussing on students studying an Arts and Humanities Access module at the Open University revealed that students in IMD Q1 are disproportionately more likely to ‘passively’ withdraw than their peers (i.e. not submit an assignment, not register with the university their intention to defer, ultimately leading to a ‘fail’ grade for that module: Lavery & Padley, 2022). Recent data shows a significant award gap for students studying Arts and Humanities modules who have a mental health need registered with the university. Colleagues were therefore motivated to assess whether the inclusion of synchronous, online, study and support opportunities, training students in the use of the Pomodoro technique, might support success for participants. This paper outlines the approach of two projects that applied techniques associated with group writing sessions, reflecting on whether their findings demonstrate similar benefits for participants in an online, distance learning environment as for those traditionally run in the physical space (i.e. writing retreats)

Guidance impact on primary care prescribing rates of simple analgesia: an interrupted time series analysis in England

Background: In March 2018, NHS England published guidance for Clinical Commissioning Groups (CCGs; NHS bodies that commission health services for local areas) to encourage implementation of policy to reduce primary care prescriptions of over-the-counter medications, including simple analgesia. Aims: To investigate: the impact of guidance publication on prescribing rates of simple analgesia (oral paracetamol, oral ibuprofen and topical non-steroidal anti-inflammatory drugs [NSAIDS]) in primary care; CCG implementation intentions; and whether it has created a health inequality based on socioeconomic status. Design and Setting: Interrupted time series analysis of primary care prescribing data in England. Methods: Practice-level prescribing data from January 2015 to March 2019 were obtained from NHS Digital. Interrupted time series analyses assessed the association of guidance publication with prescribing rates. The association between practice-level prescribing rates and Index of Multiple Deprivation score (a marker of socioeconomic deprivation) before and after publication was quantified using multivariable Poisson regression. Freedom of information requests were submitted to all CCGs. Results: There was a 4% reduction in prescribing of simple analgesia following guidance publication (adjusted incidence rate ratio [aIRR] 0.96, 95% CI 0.92-0.99, p=0.027), adjusting for underlying time trend and seasonality. Practice-level prescribing rates were greater in more deprived areas. There was considerable diversity across CCGs in whether or how they chose to implement the guidance. Conclusion: Guidance publication was associated with a small reduction in the prescribing rates of simple analgesia across England, without evidence of creating an additional health inequality. Careful implementation by CCGs would be required to optimise cost-saving to the NHS

A stepped-wedge randomised-controlled trial assessing the implementation, impact and costs of a prospective feedback loop to promote appropriate care and treatment for older patients in acute hospitals at the end of life: study protocol

BACKGROUND: Hospitalisation rates for the older population have been increasing with end-of-life care becoming a more medicalised and costly experience. There is evidence that some of these patients received non-beneficial treatment during their final hospitalisation with a third of the non-beneficial treatment duration spent in intensive care units. This study aims to increase appropriate care and treatment decisions and pathways for older patients at the end of life in Australia. This study will implement and evaluate a prospective feedback loop and tailored clinical response intervention at three hospitals in Queensland, Australia.METHODS: A stepped-wedge cluster randomised trial will be conducted with up to 21 clinical teams in three acute hospitals over 70 weeks. The study involves clinical teams providing care to patients aged 75 years or older, who are prospectively identified to be at risk of non-beneficial treatment using two validated tools for detecting death and deterioration risks. The intervention's feedback loop will provide the teams with a summary of these patients' risk profiles as a stimulus for a tailored clinical response in the intervention phase. The Consolidated Framework for Implementation Research will be used to inform the intervention's implementation and process evaluation. The study will determine the impact of the intervention on patient outcomes related to appropriate care and treatment at the end of life in hospitals, as well as the associated healthcare resource use and costs. The primary outcome is the proportion of patients who are admitted to intensive care units. A process evaluation will be carried out to assess the implementation, mechanisms of impact, and contextual barriers and enablers of the intervention.DISCUSSION: This intervention is expected to have a positive impact on the care of older patients near the end of life, specifically to improve clinical decision-making about treatment pathways and what constitutes appropriate care for these patients. These will reduce the incidence of non-beneficial treatment, and improve the efficiency of hospital resources and quality of care. The process evaluation results will be useful to inform subsequent intervention implementation at other hospitals.TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry (ANZCTR), ACTRN12619000675123p (approved 6 May 2019).</p

Integrated Placental Modelling of Histology with Gene Expression to Identify Functional Impact on Fetal Growth

Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Altered placental formation and functional capacity are major contributors to FGR pathogenesis. Relating placental structure to function across the placenta in healthy and FGR pregnancies remains largely unexplored but could improve understanding of placental diseases. We investigated integration of these parameters spatially in the term human placenta using predictive modelling. Systematic sampling was able to overcome heterogeneity in placental morphological and molecular features. Defects in villous development, elevated fibrosis, and reduced expression of growth and functional marker genes (IGF2, VEGA, SLC38A1, and SLC2A3) were seen in age-matched term FGR versus healthy control placentas. Characteristic histopathological changes with specific accompanying molecular signatures could be integrated through computational modelling to predict if the placenta came from a healthy or FGR pregnancy. Our findings yield new insights into the spatial relationship between placental structure and function and the etiology of FGR

Impact of a prospective feedback loop on care review activities in older patients at the end of life. A stepped-wedge randomised trial

Background: Hospitalisation rates for older people are increasing, with end-of-life care becoming a more medicalised experience. Innovative approaches are warranted to support early identification of the end-of-life phase, communicate prognosis, provide care consistent with people’s preferences, and improve the use of healthcare resources. The Intervention for Appropriate Care and Treatment (InterACT) trial aimed to increase appropriate care and treatment decisions for older people at the end of life, through implementation of a prospective feedback loop. This paper reports on the care review outcomes. Methods: A stepped-wedge randomised controlled trial was conducted in three large acute hospitals in Queensland, Australia between May 2020 and June 2021. The trial identified older people nearing the end of life using two validated tools for detecting deterioration and short-term death. Admitting clinical teams were provided with details of patients identified as at-risk with the goal of increasing awareness that end of life was approaching to facilitate appropriate patient centred care and avoid non-beneficial treatment. We examined the time between when the patient was identified as ‘at-risk’ and three outcomes: clinician-led care review discussions, review of care directive measures and palliative care referrals. These were considered useful indicators of appropriate care at the end of life. Results: In two hospitals there was a reduction in the review of care directive measures during the intervention compared with usual care at 21 days (reduced probability of − 0.08; 95% CI: − 0.12 to − 0.04 and − 0.14; 95% CI: − 0.21 to − 0.06). In one hospital there was a large reduction in clinician-led care review discussions at 21 days during the intervention (reduced probability of − 0.20; 95% CI: − 0.28 to − 0.13). There was little change in palliative care referrals in any hospital, with average probability differences at 21 days of − 0.01, 0.02 and 0.04. Discussion: The results are disappointing as an intervention designed to improve care of hospitalised older people appeared to have the opposite effect on care review outcomes. The reasons for this may be a combination of the intervention design and health system challenges due to the pandemic that highlight the complexity of providing more appropriate care at the end of life. Trial registration: Australia New Zealand Clinical Trial Registry, ACTRN12619000675123 (registered 6 May 2019).</p

Comparative Transcriptomic Analysis of Primary and Recurrent Ovarian Granulosa Cell Tumors

View full abstracthttps://openworks.mdanderson.org/leading-edge/1027/thumbnail.jp

The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)

Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Jülich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave–mean flow interaction

Siderophore-Mediated Zinc Acquisition Enhances Enterobacterial Colonization of the Inflamed Gut

Zinc is an essential cofactor for bacterial metabolism, and many Enterobacteriaceae express the zinc transporters ZnuABC and ZupT to acquire this metal in the host. However, the probiotic bacterium Escherichia coli Nissle 1917 (or “Nissle”) exhibits appreciable growth in zinc-limited media even when these transporters are deleted. Here, we show that Nissle utilizes the siderophore yersiniabactin as a zincophore, enabling Nissle to grow in zinc-limited media, to tolerate calprotectin-mediated zinc sequestration, and to thrive in the inflamed gut. We also show that yersiniabactin’s affinity for iron or zinc changes in a pH-dependent manner, with increased relative zinc binding as the pH increases. Thus, our results indicate that siderophore metal affinity can be influenced by the local environment and reveal a mechanism of zinc acquisition available to commensal and pathogenic Enterobacteriaceae

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society