Multilineage Differentiation Potential of Equine Adipose-Derived Stromal/Stem Cells from Different Sources

The investigation of multipotent stem/stromal cells (MSCs) in vitro represents an important basis for translational studies in large animal models. The study’s aim was to examine and compare clinically relevant in vitro properties of equine MSCs, which were isolated from abdominal (abd), retrobulbar (rb) and subcutaneous (sc) adipose tissue by collagenase digestion (ASCs-SVF) and an explant technique (ASCs-EXP). Firstly, we examined proliferation and trilineage differentiation and, secondly, the cardiomyogenic differentiation potential using activin A, bone morphogenetic protein-4 and Dickkopf-1. Fibroblast-like, plastic-adherent ASCs-SVF and ASCs-EXP were obtained from all sources. The proliferation and chondrogenic differentiation potential did not differ significantly between the isolation methods and localizations. However, abd-ASCs-EXP showed the highest adipogenic differentiation potential compared to rb- and sc-ASCs-EXP on day 7 and abd-ASCs-SVF a higher adipogenic potential compared to abd-ASCs-EXP on day 14. Osteogenic differentiation potential was comparable at day 14, but by day 21, abd-ASCs-EXP demonstrated a higher osteogenic potential compared to abd-ASCs-SVF and rb-ASCs-EXP. Cardiomyogenic differentiation could not be achieved. This study provides insight into the proliferation and multilineage differentiation potential of equine ASCs and is expected to provide a basis for future preclinical and clinical studies in horses

Comparison of Sources and Methods for the Isolation of Equine Adipose Tissue-Derived Stromal/Stem Cells and Preliminary Results on Their Reaction to Incubation with 5-Azacytidine

Physiological particularities of the equine heart justify the development of an in vitro model suitable for investigations of the species-specific equine cardiac electrophysiology. Adipose tissue-derived stromal/stem cells (ASCs) could be a promising starting point from which to develop such a cardiomyocyte (CM)-like cell model. Therefore, we compared abdominal, retrobulbar, and subcutaneous adipose tissue as sources for the isolation of ASCs applying two isolation methods: the collagenase digestion and direct explant culture. Abdominal adipose tissue was most suitable for the isolation of ASCs and both isolation methods resulted in comparable yields of CD45-/CD34-negative cells expressing the mesenchymal stem cell markers CD29, CD44, and CD90, as well as pluripotency markers, as determined by flow cytometry and real-time quantitative PCR. However, exposure of equine ASCs to 5-azacytidine (5-AZA), reportedly inducing CM differentiation from rats, rabbits, and human ASCs, was not successful in our study. More precisely, neither the early differentiation markers GATA4 and NKX2-5, nor the late CM differentiation markers TNNI3, MYH6, and MYH7 were upregulated in equine ASCs exposed to 10 µM 5-AZA for 48 h. Hence, further work focusing on the optimal conditions for CM differentiation of equine stem cells derived from adipose tissue, as well as possibly from other origins, are needed

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

Comparison of Sources and Methods for the Isolation of Equine Adipose Tissue-Derived Stromal/Stem Cells and Preliminary Results on Their Reaction to Incubation with 5-Azacytidine

Physiological particularities of the equine heart justify the development of an in vitro model suitable for investigations of the species-specific equine cardiac electrophysiology. Adipose tissue-derived stromal/stem cells (ASCs) could be a promising starting point from which to develop such a cardiomyocyte (CM)-like cell model. Therefore, we compared abdominal, retrobulbar, and subcutaneous adipose tissue as sources for the isolation of ASCs applying two isolation methods: the collagenase digestion and direct explant culture. Abdominal adipose tissue was most suitable for the isolation of ASCs and both isolation methods resulted in comparable yields of CD45-/CD34-negative cells expressing the mesenchymal stem cell markers CD29, CD44, and CD90, as well as pluripotency markers, as determined by flow cytometry and real-time quantitative PCR. However, exposure of equine ASCs to 5-azacytidine (5-AZA), reportedly inducing CM differentiation from rats, rabbits, and human ASCs, was not successful in our study. More precisely, neither the early differentiation markers GATA4 and NKX2-5, nor the late CM differentiation markers TNNI3, MYH6, and MYH7 were upregulated in equine ASCs exposed to 10 µM 5-AZA for 48 h. Hence, further work focusing on the optimal conditions for CM differentiation of equine stem cells derived from adipose tissue, as well as possibly from other origins, are needed

Multilineage Differentiation Potential of Equine Adipose-Derived Stromal/Stem Cells from Different Sources

The investigation of multipotent stem/stromal cells (MSCs) in vitro represents an important basis for translational studies in large animal models. The study’s aim was to examine and compare clinically relevant in vitro properties of equine MSCs, which were isolated from abdominal (abd), retrobulbar (rb) and subcutaneous (sc) adipose tissue by collagenase digestion (ASCs-SVF) and an explant technique (ASCs-EXP). Firstly, we examined proliferation and trilineage differentiation and, secondly, the cardiomyogenic differentiation potential using activin A, bone morphogenetic protein-4 and Dickkopf-1. Fibroblast-like, plastic-adherent ASCs-SVF and ASCs-EXP were obtained from all sources. The proliferation and chondrogenic differentiation potential did not differ significantly between the isolation methods and localizations. However, abd-ASCs-EXP showed the highest adipogenic differentiation potential compared to rb- and sc-ASCs-EXP on day 7 and abd-ASCs-SVF a higher adipogenic potential compared to abd-ASCs-EXP on day 14. Osteogenic differentiation potential was comparable at day 14, but by day 21, abd-ASCs-EXP demonstrated a higher osteogenic potential compared to abd-ASCs-SVF and rb-ASCs-EXP. Cardiomyogenic differentiation could not be achieved. This study provides insight into the proliferation and multilineage differentiation potential of equine ASCs and is expected to provide a basis for future preclinical and clinical studies in horses

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