Usage Information
Citation Information: JCI Insight. 2019;4(20):e131774. https://doi.org/10.1172/jci.insight.131774.
Abstract
Middle East respiratory syndrome coronavirus (MERS-CoV) emerged in 2012 in Saudi Arabia and has caused over 2400 cases and more than 800 deaths. Epidemiological studies identified diabetes as the primary comorbidity associated with severe or lethal MERS-CoV infection. Understanding how diabetes affects MERS is important because of the global burden of diabetes and pandemic potential of MERS-CoV. We used a model in which mice were made susceptible to MERS-CoV by expressing human DPP4, and type 2 diabetes was induced by administering a high-fat diet. Upon infection with MERS-CoV, diabetic mice had a prolonged phase of severe disease and delayed recovery that was independent of virus titers. Histological analysis revealed that diabetic mice had delayed inflammation, which was then prolonged through 21 days after infection. Diabetic mice had fewer inflammatory monocyte/macrophages and CD4+ T cells, which correlated with lower levels of Ccl2 and Cxcl10 expression. Diabetic mice also had lower levels of Tnfa, Il6, Il12b, and Arg1 expression and higher levels of Il17a expression. These data suggest that the increased disease severity observed in individuals with MERS and comorbid type 2 diabetes is likely due to a dysregulated immune response, which results in more severe and prolonged lung pathology.
Authors
Kirsten A. Kulcsar, Christopher M. Coleman, Sarah E. Beck, Matthew B. Frieman
Usage data is cumulative from September 2019 through September 2020.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 6,543 | 0 |
| 858 | 0 | |
| Figure | 332 | 0 |
| Table | 29 | 0 |
| Supplemental data | 85 | 0 |
| Citation downloads | 122 | 0 |
| Totals | 7,969 | 0 |
| Total Views | 7,969 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
