COVID-associated pernio: Mechanistic insights to an abortive, seronegative SARS-CoV-2 infection

Introduction: SARS-CoV-2 replicates primarily in the airways but generates a systemic immune response mediated by Type I interferons (IFN-I). Pernio is a rare skin manifestation of disorders characterized by excessive IFN-I signalling. Although pernio increased in incidence during the pandemic, the relationship to SARS-CoV-2 remains controversial. Because of the pivotal nature of interferons in COVID-19 outcomes, pernio offers a window to investigate the biology underlying host resiliency to SARS-CoV-2 infection. Method(s): To further assess COVID-associated pernio, we characterized clinical samples from affected patients across 4 waves of the pandemic and investigated mechanistic feasibility in a rodent model. Patients were followed longitudinally with banking of blood and tissue. Golden hamsters were mock-treated or intra-nasally infected with SARS-CoV-2 and harvested at 3-and 30-days post-infection. Result(s): In affected tissue, immunophenotyping utilizing multiplex immunohistochemistry profiled a robust IFN-1 signature characterized by plasmacytoid dendritic cell activation. Viral RNA was detectable in a subset of cases using in situ hybridization for the SARS-CoV-2 S gene transcript. Profiling of the systemic immune response did not reveal a durable type 1 interferon signature. Consistent with previous literature, antibody and T-cell specific responses to SARS-CoV-2 were not detected. Nasopharyngeal SARS-CoV-2 inoculation in hamsters resulted in rapid dissemination of viral RNA and the generation of an IFN-I response that were both detectable in the paws of infected animals. Conclusion(s): Our data support a durable local IFN signature, with direct evidence of viral SARS-CoV-2 RNA in acral skin and suggest that COVID-associated pernio results from an abortive, seronegative SARS-CoV-2 infection.

Examining virtual research recruitment and participant diversity in a multi-center birth cohort, Childhood Allergy and the NeOnatal Environment" (CANOE)

Rationale: Recruitment for a NIH/ECHO-supported multi-center birth cohort, "Childhood Allergy and the NeOnatal Environment” (CANOE) stopped due to the COVID-19 pandemic. Redesign of study procedures emphasized virtual and socially distanced activities. We hypothesized that "virtual” recruitment methods (social media, websites, email) would surpass "traditional” methods (in-clinic, telephone, flyers/print materials) and increase enrollment of families from diverse backgrounds and communities. Methods: Pregnant women (n=439, target 500) were recruited from four academic medical centers in Detroit MI, Madison WI, Nashville TN, and St. Louis MO. We collected demographic and social information by questionnaires and examined race, ethnicity, age, parity, and employment status in relation to recruitment method using chi-square tests. Results: In-clinic and telephone recruitment comprised 55% of enrollment, followed by print materials (17%), and social media and email (15%). The cohort includes families self-identifying as Caucasian/White (63%), African American/Black (27%), Hispanic/Latino (3.3%), Asian (3.5%), and mixed races (1.2%). This reflects site demographics for White and Black patients, while other populations are not as well recruited into this cohort. Recruitment method success did not vary by race, ethnicity, maternal age, or employment status (p=ns for each comparison). Most (63%) multigravida mothers (9.1% of participants) were recruited in clinic, while primigravida participants were recruited more evenly via all methods. Conclusions: "Virtual” recruitment methods comprised a smaller proportion of cohort enrollment than hypothesized and study recruitment method did not vary by race/ethnicity;however, consideration of combined, varied, and novel recruitment methods may add to the development of best practices for more representative research study recruitment.

Identification of Non-Vaccinated People Using Face Recognition Based on CNN

Preventive medical care relies on vaccinations to provide significant health benefits. Vaccination is an important and effective preventive health measure. There is no better way to reduce the risk of pandemic spread of SARS-CoV-2/COVID-19 than vaccination. As a preventive measure, the government has begun vaccinating Indians against Corona infection. It is therefore important, in addition to developing and supplying vaccines, that enough people are willing to obtain vaccines. However, of the populations worldwide, there are concerning proportions that are reluctant to get vaccinated. In order to end the pandemic, it is highly essential to deal with another omnipresent issue: outright rejection of vaccinations. To achieve population immunity first we have to find the non-vaccinated population should be detected and to this end, this project proposed an Aadhaar-based facial recognition system is used to find non-vaccinated citizen and alert them using Artificial Intelligence. Deep learning which is in the form of Convolutional Neural Networks (CNNs) are used to carry out the face recognition process and it is also proven to be an efficient method to carry out face recognition due to its high fidelity. A CNN is a Deep Neural Network (DNN), which is designed to perform challenging tasks like image processing, which is crucial for facial recognition. The CNN structure is composed of numerous layers of neurons that connect the neurons: an input layer, an output layer, and layers between these two layers. In the midst of the epidemic coronavirus outbreak (COVID-19), a person's current inoculation status will be updated based on face recognition to safeguard him/her from COVID-19 and it may also serve as proof of vaccination for other purposes. Facial recognition technology (FRT) along with the Aadhaar helps to authenticate people before entering into any types of service. This project provides COVID-19 immunization status, which is determined by observing at their face, and certify that they have been vaccinated. © 2022 IEEE.