Involving Urban Single Low-Income African American Mothers in Genomic Research: Giving Voice to How Place Matters in Health Disparities and Prevention Strategies.

This article describes the process of using principles from community-based participatory action research to involve low-income, single, African American mothers on the south side of Chicago in genomic research, including as citizen scientists. The South Chicago Black Mothers' Resiliency Project used a mixed methods design to investigate how the stress of living in neighborhoods with high levels of violence affects mothers' mental and physical health. This article seeks to serve as a model for physicians and scholars interested in successfully involving low-income African American mothers in genomic research, and other health-related activities in ways that are culturally sensitive and transformative. The lives of Black mothers who struggle under interlocking systems of oppression that are often hidden from view of most Americans are at the center of this article. Therefore, we provide extensive information about the procedures used to collect the various types of data, the rationale for our procedures, the setting, the responses of mothers in our sample and methodological challenges. This study also has implications for the current COVID-19 pandemic and the need to train a corps of citizen scientists in health and wellness to avoid future extreme loss of life such as the 106,195 lives lost in the United States as of June 1, 2020.

Risk Factors for Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Homeless Shelters in Chicago, Illinois-March-May, 2020.

BACKGROUND: People experiencing homelessness are at increased risk of coronavirus disease 2019 (COVID-19), but little is known about specific risk factors for infection within homeless shelters. METHODS: We performed widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction testing and collected risk factor information at all homeless shelters in Chicago with at least 1 reported case of COVID-19 (n = 21). Multivariable, mixed-effects log-binomial models were built to estimate adjusted prevalence ratios (aPRs) for SARS-CoV-2 infection for both individual- and facility-level risk factors. RESULTS: During March 1 to May 1, 2020, 1717 shelter residents and staff were tested for SARS-CoV-2; 472 (27%) persons tested positive. Prevalence of infection was higher for residents (431 of 1435, 30%) than for staff (41 of 282, 15%) (prevalence ratio = 2.52; 95% confidence interval [CI], 1.78-3.58). The majority of residents with SARS-CoV-2 infection (293 of 406 with available information about symptoms, 72%) reported no symptoms at the time of specimen collection or within the following 2 weeks. Among residents, sharing a room with a large number of people was associated with increased likelihood of infection (aPR for sharing with >20 people compared with single rooms = 1.76; 95% CI, 1.11-2.80), and current smoking was associated with reduced likelihood of infection (aPR = 0.71; 95% CI, 0.60-0.85). At the facility level, a higher proportion of residents leaving and returning each day was associated with increased prevalence (aPR = 1.08; 95% CI, 1.01-1.16), whereas an increase in the number of private bathrooms was associated with reduced prevalence (aPR for 1 additional private bathroom per 100 people = 0.92; 95% CI, 0.87-0.98). CONCLUSIONS: We identified a high prevalence of SARS-CoV-2 infections in homeless shelters. Reducing the number of residents sharing dormitories might reduce the likelihood of SARS-CoV-2 infection. When community transmission is high, limiting movement of persons experiencing homelessness into and out of shelters might also be beneficial.

Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.

Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.