Facilitating Mouse Studies of Post-acute Sequelae of COVID-19 (PASC)

COVID-19, caused by SARS-CoV-2, and its chronic form, Post-Acute Sequelae of COVID-19 (PASC), remain significant public health concerns. Transgenic mice are an effective model for acute COVID-19 research, but PASC studies are currently lacking due to the prohibitive costs of performing such studies in an Animal Biosafety Level 3 (ABSL-3) containment setting. The goal of this study was to determine the natural timing of clearance of the SARS-CoV-2 virus from mice and establish a protocol for transfer of animals from ABSL- 3 to ABSL-2 for PASC studies. We hypothesized that infected mice would clear viral infection by approximately 3 to 4 wk postinfection (WPI). Six- to 18-wk-old, B6.Cg-Tg(K18-ACE2)2Prlmn/J (hACE2) mice (N = 48/sex) were intranasally inoculated with a pre-alpha strain of SARS-CoV-2 in an ABSL-3 containment setting. Environmental samples, oral swabs, and fecal samples were collected weekly up to 8 wk postinoculation and cohorts of surviving mice were necropsied at 4, 7, and 8 WPI when lung and brain were collected. Viral loads in all samples were quantified via RT-qPCR. Survival was significantly affected by sex, with males more susceptible (P = 0.002), but not age (P = 0.005). SARS-CoV-2 viral RNA copies were present in the lungs of mice at 4, 7, and 8 WPI, indicating that the mice had not yet cleared infection by the culmination of the study and raising the possibility of persistent infection.

Identifying essential nonprofits with a novel NLP Method

In this research note, we propose a classification method for identifying whether a 501c3 nonprofit organization is considered essential for economic recovery. During the first few months of the COVID-19 pandemic, many nonprofit organizations experienced negative financial effects from the economic recession. While these nonprofits saw increased demand for their services, the weakness in the overall economy led to a decline in donations. Fiscal assistance by local, state, and federal government to essential organizations was a critical element to an economic recovery, and governments needed to prioritize aid to the most essential organizations first. By identifying essential nonprofit organizations in advance, these organizations could quickly and efficiently receive financial assistance. Using descriptive text data provided by Ohio nonprofit organizations in their IRS tax filings, we propose a novel natural language processing (NLP) technique to measure the degree of "essentialness" to a nonprofit's work. We show that our model offers an improvement to the classification system known as the National Taxonomy of Exempt Organizations (NTEE). Our machine learning model is also compared to an independent evaluation of a nonprofit's essentialness produced by human researchers.

VICTORS, VICTIMS, AND VECTORS

COVID-19 has plunged us into times of loss-loss of friends and family, loss of purpose, and loss of a sense of certainty about the immediate future. As we traverse this time of anomie and loss between pre- and post-COVID-19 times, through pandemic surges that threaten to exhaust our resources and seemingly endless troughs of calm, we need to care for each other. Care provided to those in need of hospitalization due to COVID-19 should undoubtedly be prioritized, but we should not forget to care for those who are physically well but suffering, by recognizing the fear and sorrow that flavor changed experiences due to COVID-19. Narratives that reveal challenges and triumphs are central to this kind of care. Frank (2007) argues that care is about "helping people find their stories." We find comfort in telling these stories and in hearing them;recognizing ourselves in another's tale allows us to find meaning in our own suffering. This paper tells the narratives of three health professionals on the COVID-19 frontline.

Using cutting-edge technology to end malnutrition

The world is facing a malnutrition crisis. One billion people are hungry, at least 3 billion do not receive sufficient nutrients and more than 2.5 billion consume unhealthy diets (UNICEF, WHO and World Bank, 2018). In addition to its immediate impact on health and society, the COVID-19 pandemic will cause significant short- and long-term disruptions to food systems. This will reduce the availability of nutritious foods, particularly for poor and vulnerable populations, and fuel the prevalence of all forms of malnutrition. Malnutrition results in the intergenerational transmission of inequity, poverty and poor health and poses a significant barrier to equitable and sustainable social and economic development. The data-collection step in the value chain is, perhaps, the most important and requires the establishment of high-quality national and subnational systems to collect reliable information. Yet, many nutrition issues still lack dependable data. This is the case for micronutrient deficiencies, which are still poorly understood in most countries due to vast data gaps.