Clinical Pathway for Coronary Atherosclerosis in Patients Without Conventional Modifiable Risk Factors JACC State-of-the-Art Review

Reducing the incidence and prevalence of standard modifiable cardiovascular risk factors (SMuRFs) is critical to tackling the global burden of coronary artery disease (CAD). However, a substantial number of individuals develop coronary atherosclerosis despite no SMuRFs. SMuRFless patients presenting with myocardial infarction have been observed to have an unexpected higher early mortality compared to their counterparts with at least 1 SMuRF. Evidence for optimal management of these patients is lacking. We assembled an international, multidisciplinary team to develop an evidence-based clinical pathway for SMuRFless CAD patients. A modified Delphi method was applied. The resulting pathway confirms underlying atherosclerosis and true SMuRFless status, ensures evidence-based secondary prevention, and considers additional tests and interventions for less typical contributors. This dedicated pathway for a previously overlooked CAD population, with an accompanying registry, aims to improve outcomes through enhanced adherence to evidence-based secondary prevention and additional diagnosis of modifiable risk factors observed

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

The impact of the COVID-19 pandemic on infection and utilization of fecal microbiota transplantation

Previous research has demonstrated that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gains cell entry through the angiotensin-converting enzyme 2 receptor, which is abundantly found throughout the gastrointestinal (GI) tract, resulting in a wide array of GI manifestations of coronavirus disease 2019 (COVID-19). By gaining entry into the intestinal epithelial and stromal cells, SARS-CoV-2 has been observed to cause intestinal inflammation and gut dysbiosis. Alterations in gut microbiota are known to be involved in the pathophysiology of Clostridioides difficile infection (CDI). During the initial stages of the COVID-19 pandemic, rates of CDI were similar to historical data despite the increased use of antibiotics. This may be due to increased emphasis on hygiene and protective equipment and reduced C. difficile testing as diarrhea was presumed to be COVID-19 related. Studies also demonstrated additional risk factors for CDI in COVID-19 patients, including length of hospitalization and new abdominal pain during admission. Although not associated with increased mortality, CDI was associated with increased length of hospital stay among patients admitted with COVID-19. Due to fecal viral shedding and concern of oral–fecal transmission of SARS-CoV-2, increased safety regulations were introduced to fecal microbiota transplantation (FMT) leading to reduced rates of this procedure during the COVID-19 pandemic. FMT for recurrent CDI during the COVID-19 pandemic remained highly effective without any reports of SARS-CoV-2 transmission. In addition, limited data show that FMT may be effective in treating COVID-19 and restoring healthy gut microbiota. The goal of this article is to review the impact that the COVID-19 pandemic has had on hospital-acquired CDI and the utilization of FMT

Recommending energy tariffs and load shifting based on smart household usage profiling

We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management

Discussion of Assessment of Reference Evapotranspiration by the Hargreaves Method in the Bekaa Valley, Lebanon by Roula Bachour, Wynn R. Walker, Alfonso F. Torres-Rua, and Mac McKee

Martí Pérez, PC.; Royuela, A.; González Altozano, P. (2015). Discussion of Assessment of Reference Evapotranspiration by the Hargreaves Method in the Bekaa Valley, Lebanon by Roula Bachour, Wynn R. Walker, Alfonso F. Torres-Rua, and Mac McKee. Journal of Irrigation and Drainage Engineering. 141(6). doi:10.1061/(ASCE)IR.1943-4774.0000646S141

Estimation of Spatially Distributed Evapotranspiration using Remote Sensing and a Relevance Vector Machine

With the development of surface energy balance analyses, remote sensing has become a spatially explicit and quantitative methodology for understanding evapotranspiration (ET), a critical requirement for water resources planning and management. Limited temporal resolution of satellite images and cloudy skies present major limitations that impede continuous estimates of ET. This study introduces a practical approach that overcomes (in part) the previous limitations by implementing machine learning techniques that are accurate and robust. The analysis was applied to the Canal B service area of the Delta Canal Company in central Utah using data from the 2009–2011 growing seasons. Actual ET was calculated by an algorithm using data from satellite images. A relevance vector machine (RVM), which is a sparse Bayesian regression, was used to build a spatial model for ET. The RVM was trained with a set of inputs consisting of vegetation indexes, crops, and weather data. ET estimated via the algorithm was used as an output. The developed RVM model provided an accurate estimation of spatial ET based on a Nash-Sutcliffe coefficient (EE) of 0.84 and a root-mean-squared error (RMSE) of 0.5mmday−10.5  mm day−1. This methodology lays the groundwork for estimating ET at a spatial scale for the days when a satellite image is not available. It could also be used to forecast daily spatial ET if the vegetation indexes model inputs are extrapolated in time and the reference ET is forecasted accurately. Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)IR.1943-4774.000075