The Pro-Apoptotic Proteins, Bid and Bax, Cause a Limited Permeabilization of the Mitochondrial Outer Membrane That Is Enhanced by Cytosol

During apoptosis, an important pathway leading to caspase activation involves the release of cytochrome c from the intermembrane space of mitochondria. Using a cell-free system based on Xenopus egg extracts, we examined changes in the outer mitochondrial membrane accompanying cytochrome c efflux. The pro-apoptotic proteins, Bid and Bax, as well as factors present in Xenopus egg cytosol, each induced cytochrome c release when incubated with isolated mitochondria. These factors caused a permeabilization of the outer membrane that allowed the corelease of multiple intermembrane space proteins: cytochrome c, adenylate kinase and sulfite oxidase. The efflux process is thus nonspecific. None of the cytochrome c-releasing factors caused detectable mitochondrial swelling, arguing that matrix swelling is not required for outer membrane permeability in this system. Bid and Bax caused complete release of cytochrome c but only a limited permeabilization of the outer membrane, as measured by the accessibility of inner membrane-associated respiratory complexes III and IV to exogenously added cytochrome c. However, outer membrane permeability was strikingly increased by a macromolecular cytosolic factor, termed PEF (permeability enhancing factor). We hypothesize that PEF activity could help determine whether cells can recover from mitochondrial cytochrome c release

Aiming at the global elimination of viral hepatitis:challenges along the care continuum

Abstract A recent international workshop, organized by the authors, analyzed the obstacles facing the ambitious goal of eliminating viral hepatitis globally. We identified several policy areas critical to reaching elimination targets. These include providing hepatitis B birth-dose vaccination to all infants within 24 hours of birth, preventing the transmission of blood-borne viruses through the expansion of national hemovigilance schemes, implementing the lessons learned from the HIV epidemic regarding safe medical practices to eliminate iatrogenic infection, adopting point-of-care testing to improve coverage of diagnosis, and providing free or affordable hepatitis C treatment to all. We introduce Egypt as a case study for rapid testing and treatment scale-up: this country offers valuable insights to policy makers internationally, not only regarding how hepatitis C interventions can be expeditiously scaled-up, but also as a guide for how to tackle the problems encountered with such ambitious testing and treatment programs.</jats:p

Abstracts from the NIHR INVOLVE Conference 2017

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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 progress of woman and Christianity

Thesis (BL)--Illinois Industrial University, 1884MsBound with 12 other theses from UIUC, 1884 IU-

The progress of woman and Christianity

Thesis (BL)--Illinois Industrial University, 1884MsBound with 12 other theses from UIUC, 1884 IU-

Prevalence and incidence curves.

<p>Prevalence and incidence for all three care contexts, for an arbitrarily chosen calibrated epidemiological parameter set. For all parameter sets see Fig C in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158303#pone.0158303.s001" target="_blank">S1 Supporting Information</a>. The colours correspond to (from top to bottom on both panels): grey—current care (CC) context, orange—enhanced counselling and testing (ECT), blue—universal test and treat (UTT). Also shown on the left panel are the confidence intervals of the UNAIDS prevalence estimates (inner circles) and twice the confidence intervals (outer crosses).</p