Stevens County Food Assessment

This report is the culmination of a year-long community food assessment conducted by staff, students, and faculty at the University of Minnesota Morris, and informed by an advisory council made up of key local stakeholders. The main goal of the community food assessment is to describe food security in Stevens County at both community and individual scales. This assessment examines what food is grown in the county, what food is available, where food can be obtained in various forms, accessibility and affordability of food, as well as county residents’ experiences with and thoughts and suggestions about food. Findings summarized below rely on several different types of data, including a household food security survey, a survey of prices and availability at area grocery stores, personal communications and observations, and secondary data (e.g., from the US Census Bureau). More details about data collection and the key findings presented below are available in the full version of this report. Based on the (available and newly collected) data for this community food assessment, it is clear that Stevens County does not fit the definition of community food security because many residents are food insecure, food insecure residents tend to share characteristics of marginalized populations, and little of the food consumed in Stevens County is produced and processed in Stevens County. Challenges with community food security are of course not necessarily uniquely to Stevens County, MN as they are at least in part a product of the way our regional, national, and global food supply chains presently function.https://digitalcommons.morris.umn.edu/cst/1083/thumbnail.jp

Machine learning algorithm improves detection of NASH (NAS-based) and at-risk NASH, a development and validation study

Background & Aims: Detecting non-alcoholic steatohepatitis (NASH) remains challenging, while at-risk NASH (steatohepatitis and F>2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning (ML) techniques, with clinical data and biomarkers to stage and grade non-alcoholic fatty liver disease (NAFLD) patients.Approach & Results: Learning data were collected in the LITMUS Metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH-CRN. Conditions of interest were clinical trial definition of NASH (NAS≥4;53%), at-risk NASH (NASH with F≥2;35%), significant (F>2;47%) and advanced fibrosis (F>3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputation. Data were randomly split into training/validation (75/25) sets. Gradient boosting machine (GBM) was applied to develop two models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease