Stroke penumbra defined by an MRI-based oxygen challenge technique: 2. Validation based on the consequences of reperfusion

Magnetic resonance imaging (MRI) with oxygen challenge (T2* OC) uses oxygen as a metabolic biotracer to define penumbral tissue based on CMRO2 and oxygen extraction fraction. Penumbra displays a greater T2* signal change during OC than surrounding tissue. Since timely restoration of cerebral blood flow (CBF) should salvage penumbra, T2* OC was tested by examining the consequences of reperfusion on T2* OC-defined penumbra. Transient ischemia (109±20 minutes) was induced in male Sprague-Dawley rats (n=8). Penumbra was identified on T2*-weighted MRI during OC. Ischemia and ischemic injury were identified on CBF and apparent diffusion coefficient maps, respectively. Reperfusion was induced and scans repeated. T2 for final infarct and T2* OC were run on day 7. T2* signal increase to OC was 3.4% in contralateral cortex and caudate nucleus and was unaffected by reperfusion. In OC-defined penumbra, T2* signal increased by 8.4%±4.1% during ischemia and returned to 3.25%±0.8% following reperfusion. Ischemic core T2* signal increase was 0.39%±0.47% during ischemia and 0.84%±1.8% on reperfusion. Penumbral CBF increased from 41.94±13 to 116.5±25 mL per 100 g per minute on reperfusion. On day 7, OC-defined penumbra gave a normal OC response and was located outside the infarct. T2* OC-defined penumbra recovered when CBF was restored, providing further validation of the utility of T2* OC for acute stroke management

Stroke penumbra defined by an MRI-based oxygen challenge technique: 1. validation using [14C]2-deoxyglucose autoradiography

Accurate identification of ischemic penumbra will improve stroke patient selection for reperfusion therapies and clinical trials. Current magnetic resonance imaging (MRI) techniques have limitations and lack validation. Oxygen challenge T2* MRI (T2* OC) uses oxygen as a biotracer to detect tissue metabolism, with penumbra displaying the greatest T2* signal change during OC. [14C]2-deoxyglucose (2-DG) autoradiography was combined with T2* OC to determine metabolic status of T2*-defined penumbra. Permanent middle cerebral artery occlusion was induced in anesthetized male Sprague-Dawley rats (n=6). Ischemic injury and perfusion deficit were determined by diffusion- and perfusion-weighted imaging, respectively. At 147±32 minutes after stroke, T2* signal change was measured during a 5-minute 100% OC, immediately followed by 125 μCi/kg 2-DG, intravenously. Magnetic resonance images were coregistered with the corresponding autoradiograms. Regions of interest were located within ischemic core, T2*-defined penumbra, equivalent contralateral structures, and a region of hyperglycolysis. A T2* signal increase of 9.22%±3.9% (mean±s.d.) was recorded in presumed penumbra, which displayed local cerebral glucose utilization values equivalent to contralateral cortex. T2* signal change was negligible in ischemic core, 3.2%±0.78% in contralateral regions, and 1.41%±0.62% in hyperglycolytic tissue, located outside OC-defined penumbra and within the diffusion abnormality. The results support the utility of OC-MRI to detect viable penumbral tissue follow

Potential use of oxygen as a metabolic biosensor in combination with T2*-weighted MRI to define the ischemic penumbra

We describe a novel magnetic resonance imaging technique for detecting metabolism indirectly through changes in oxyhemoglobin:deoxyhemoglobin ratios and T2* signal change during ‘oxygen challenge’ (OC, 5 mins 100% O2). During OC, T2* increase reflects O2 binding to deoxyhemoglobin, which is formed when metabolizing tissues take up oxygen. Here OC has been applied to identify tissue metabolism within the ischemic brain. Permanent middle cerebral artery occlusion was induced in rats. In series 1 scanning (n=5), diffusion-weighted imaging (DWI) was performed, followed by echo-planar T2* acquired during OC and perfusion-weighted imaging (PWI, arterial spin labeling). Oxygen challenge induced a T2* signal increase of 1.8%, 3.7%, and 0.24% in the contralateral cortex, ipsilateral cortex within the PWI/DWI mismatch zone, and ischemic core, respectively. T2* and apparent diffusion coefficient (ADC) map coregistration revealed that the T2* signal increase extended into the ADC lesion (3.4%). In series 2 (n=5), FLASH T2* and ADC maps coregistered with histology revealed a T2* signal increase of 4.9% in the histologically defined border zone (55% normal neuronal morphology, located within the ADC lesion boundary) compared with a 0.7% increase in the cortical ischemic core (92% neuronal ischemic cell change, core ADC lesion). Oxygen challenge has potential clinical utility and, by distinguishing metabolically active and inactive tissues within hypoperfused regions, could provide a more precise assessment of penumbra

Access to COVID-19 testing by individuals with housing insecurity during the early days of the COVID-19 pandemic in the United States: a scoping review

Introduction: The COVID-19 pandemic focused attention on healthcare disparities and inequities faced by individuals within marginalized and structurally disadvantaged groups in the United States. These individuals bore the heaviest burden across this pandemic as they faced increased risk of infection and difficulty in accessing testing and medical care. Individuals experiencing housing insecurity are a particularly vulnerable population given the additional barriers they face. In this scoping review, we identify some of the barriers this high-risk group experienced during the early days of the pandemic and assess novel solutions to overcome these barriers. Methods: A scoping review was performed following PRISMA-Sc guidelines looking for studies focusing on COVID-19 testing among individuals experiencing housing insecurity. Barriers as well as solutions to barriers were identified as applicable and summarized using qualitative methods, highlighting particular ways that proved effective in facilitating access to testing access and delivery. Results: Ultimately, 42 studies were included in the scoping review, with 143 barriers grouped into four categories: lack of cultural understanding, systemic racism, and stigma; medical care cost, insurance, and logistics; immigration policies, language, and fear of deportation; and other. Out of these 42 studies, 30 of these studies also suggested solutions to address them. Conclusion: A paucity of studies have analyzed COVID-19 testing barriers among those experiencing housing insecurity, and this is even more pronounced in terms of solutions to address those barriers. Expanding resources and supporting investigators within this space is necessary to ensure equitable healthcare delivery

RADx-UP Testing Core: Access to COVID-19 Diagnostics in Community-Engaged Research with Underserved Populations

Research on the COVID-19 pandemic revealed a disproportionate burden of COVID-19 infection and death among underserved populations and exposed low rates of SARS-CoV-2 testing in these communities. A landmark National Institutes of Health (NIH) funding initiative, the Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program, was developed to address the research gap in understanding the adoption of COVID-19 testing in underserved populations. This program is the single largest investment in health disparities and community-engaged research in the history of the NIH. The RADx-UP Testing Core (TC) provides community-based investigators with essential scientific expertise and guidance on COVID-19 diagnostics. This commentary describes the first 2 years of the TC's experience, highlighting the challenges faced and insights gained to safely and effectively deploy large-scale diagnostics for community-initiated research in underserved populations during a pandemic. The success of RADx-UP shows that community-based research to increase access and uptake of testing among underserved populations can be accomplished during a pandemic with tools, resources, and multidisciplinary expertise provided by a centralized testing-specific coordinating center. We developed adaptive tools to support individual testing strategies and frameworks for these diverse studies and ensured continuous monitoring of testing strategies and use of study data. In a rapidly evolving setting of tremendous uncertainty, the TC provided essential and real-time technical expertise to support safe, effective, and adaptive testing. The lessons learned go beyond this pandemic and can serve as a framework for rapid deployment of testing in response to future crises, especially when populations are affected inequitably

Career guidance and the changing world of work: Contesting responsibilising notions of the future.

Career guidance is an educational activity which helps individuals to manage their participation in learning and work and plan for their futures. Unsurprisingly career guidance practitioners are interested in how the world of work is changing and concerned about threats of technological unemployment. This chapter argues that the career guidance field is strongly influenced by a “changing world of work” narrative which is drawn from a wide body of grey literature produced by think tanks, supra-national bodies and other policy influencers. This body of literature is political in nature and describes the future of work narrowly and within the frame of neoliberalism. The ‘changing world of work’ narrative is explored through a thematic analysis of grey literature and promotional materials for career guidance conferences. The chapter concludes by arguing that career guidance needs to adopt a more critical stance on the ‘changing world of work’ and to offer more emancipatory alternatives.N/

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care