Chronicles of Oklahoma

Notes and Documents, Chronicles of Oklahoma, Volume 33, Number 2, Summer 1955. It includes documents about publications, dedications, correspondence, memorials, some additional history for articles in The Chronicles, and the history of the University of Oklahoma Medical Center

Multiple Imputation to Account for Measurement Error in Marginal Structural Models

Marginal structural models are an important tool for observational studies. These models typically assume that variables are measured without error. We describe a method to account for differential and non-differential measurement error in a marginal structural model

The Toxicity of a Mutant Prion Protein Is Cell-Autonomous, and Can Be Suppressed by Wild-Type Prion Protein on Adjacent Cells

Insight into the normal function of PrPC, and how it can be subverted to produce neurotoxic effects, is provided by PrP molecules carrying deletions encompassing the conserved central region. The most neurotoxic of these mutants, Δ105–125 (called ΔCR), produces a spontaneous neurodegenerative illness when expressed in transgenic mice, and this phenotype can be dose-dependently suppressed by co-expression of wild-type PrP. Whether the toxic activity of ΔCR PrP and the protective activity or wild-type PrP are cell-autonomous, or can be exerted on neighboring cells, is unknown. To investigate this question, we have utilized co-cultures of differentiated neural stem cells derived from mice expressing ΔCR or wild-type PrP. Cells from the two kinds of mice, which are marked by the presence or absence of GFP, are differentiated together to yield neurons, astrocytes, and oligodendrocytes. As a surrogate read-out of ΔCR PrP toxicity, we assayed sensitivity of the cells to the cationic antibiotic, Zeocin. In a previous study, we reported that cells expressing ΔCR PrP are hypersensitive to the toxic effects of several cationic antibiotics, an effect that is suppressed by co-expression of wild type PrP, similar to the rescue of the neurodegenerative phenotype observed in transgenic mice. Using this system, we find that while ΔCR-dependent toxicity is cell-autonomous, the rescuing activity of wild-type PrP can be exerted in trans from nearby cells. These results provide important insights into how ΔCR PrP subverts a normal physiological function of PrPC, and the cellular mechanisms underlying the rescuing process

The N-Terminal, Polybasic Region Is Critical for Prion Protein Neuroprotective Activity

Several lines of evidence suggest that the normal form of the prion protein, PrPC, exerts a neuroprotective activity against cellular stress or toxicity. One of the clearest examples of such activity is the ability of wild-type PrPC to suppress the spontaneous neurodegenerative phenotype of transgenic mice expressing a deleted form of PrP (Δ32–134, called F35). To define domains of PrP involved in its neuroprotective activity, we have analyzed the ability of several deletion mutants of PrP (Δ23–31, Δ23–111, and Δ23–134) to rescue the phenotype of Tg(F35) mice. Surprisingly, all of these mutants displayed greatly diminished rescue activity, although Δ23–31 PrP partially suppressed neuronal loss when expressed at very high levels. Our results pinpoint the N-terminal, polybasic domain as a critical determinant of PrPC neuroprotective activity, and suggest that identification of molecules interacting with this region will provide important clues regarding the normal function of the protein. Small molecule ligands targeting this region may also represent useful therapeutic agents for treatment of prion diseases

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

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery