ABSTRACT
PURPOSE: To evaluate the presence of SARS-COV-2 specific IgA and IgG antibodies in tears of unvaccinated and anti-COVID-19 vaccinated subjects with previous history of SARS-COV-2 infection. To compare results in tears with those in saliva and serum and correlate with clinical data and vaccination regimens. METHODS: Cross-sectional study including subjects with a previous history of SARS-CoV-2 infection, both unvaccinated and vaccinated against COVID-19. Three samples were collected: tears, saliva and serum. IgA and IgG antibodies against S-1 protein of SARS-CoV-2 were analyzed with a semi-quantitative ELISA. RESULTS: 30 subjects, mean age 36.4⯱â¯10, males 13/30 (43.3%) with history of mild SARS-CoV-2 infection were included. 13/30 (43.3%) subjects had received a 2-dose regimen and 13/30 (43.3%) a 3-dose regimen of anti-COVID-19 vaccine, 4/30 (13.3%) subjects were unvaccinated. All the participants with full anti-COVID-19 vaccination (2-or 3-doses) presented detectable anti-S1 specific IgA in all three biofluids, tears, saliva and serum. Among unvaccinated subjects, specific IgA was detected in 3/4 subjects in tears and saliva, whereas IgG was not detected. Considering IgA and IgG antibodies titers, no differences were observed between the 2- and 3-dose vaccination regimen. CONCLUSIONS: SARS-CoV-2-specific IgA and IgG antibodies were detected in tears after mild COVID-19, highlighting the role of the ocular surface as a first line of defense against infection. Most naturally infected unvaccinated individuals exhibit long-term specific IgA in tears and saliva. Hybrid immunization (natural infection plus vaccination) appears to enhance mucosal and systemic IgG responses. However, no differences were observed between the 2- and 3-dose vaccination schedule.
Subject(s)
COVID-19 , Male , Humans , Adult , Middle Aged , Cross-Sectional Studies , SARS-CoV-2 , Eye , Antibodies, Viral , Immunoglobulin G , Immunoglobulin AABSTRACT
This paper examines the rapid introduction of AI and automation technologies within essential industries amid the COVID-19 pandemic. Drawing on participant observation and interviews within two sites of waste labor in the United States, we consider the substantial effort performed by frontline workers who smooth the relationship between robotics and their social and material environment. Over the course of the research, we found workers engaged in continuous acts of calibration, troubleshooting, and repair required to support AI technologies over time. In interrogating these sites, we develop the concept of "patchwork": human labor that occurs in the space between what AI purports to do and what it actually accomplishes. We argue that it is necessary to consider the often-undervalued frontline work that makes up for AI's shortcomings during implementation, particularly as CSCW increasingly turns to discussions of Human-AI collaboration. © 2023 Owner/Author.
ABSTRACT
Purpose: To evaluate the presence of SARS-CoV-2 specific IgA and IgG antibodies in tears of unvaccinated and anti-COVID-19 vaccinated subjects with previous history of SARS-CoV-2 infection. To compare results in tears with those in saliva and serum and correlate with clinical data and vaccination regimens. Methods: Cross-sectional study including subjects with a previous history of SARS-CoV-2 infection, both unvaccinated and vaccinated against COVID-19. Three samples were collected: tears, saliva and serum. IgA and IgG antibodies against S-1 protein of SARS-CoV-2 were analyzed with a semi-quantitative ELISA. Results: Thirty subjects, mean age 36.4 ± 10, males 13/30 (43.3%) with history of mild SARS-CoV-2 infection were included. 13/30 (43.3%) subjects had received a 2-dose regimen and 13/30 (43.3%) a 3-dose regimen of anti-COVID-19 vaccine, 4/30 (13.3%) subjects were unvaccinated. All the participants with full anti-COVID-19 vaccination (2-or 3-doses) presented detectable anti-S1 specific IgA in all 3 biofluids, tears, saliva and serum. Among unvaccinated subjects, specific IgA was detected in 3/4 subjects in tears and saliva, whereas IgG was not detected. Considering IgA and IgG antibodies titers, no differences were observed between the 2- and 3-dose vaccination regimen. Conclusions: SARS-CoV-2-specific IgA and IgG antibodies were detected in tears after mild COVID-19, highlighting the role of the ocular surface as a first line of defense against infection. Most naturally infected unvaccinated individuals exhibit long-term specific IgA in tears and saliva. Hybrid immunization (natural infection plus vaccination) appears to enhance mucosal and systemic IgG responses. However, no differences were observed between the 2- and 3-dose vaccination schedule.
ABSTRACT
The Latent Dirichlet Location (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a negative binomial regression is embedded within LDA, enabling straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling units (instead of the analysis being focused on modeling the proportion of each cluster, as in Structural Topic Models). We use slice sampling within a Gibbs sampling algorithm to estimate model parameters. We rely on simulations to show how our algorithm is able to successfully retrieve the true parameter values and the ability to make predictions for the abundance matrix using the information given by the covariates. The model is illustrated using real data sets from three different areas: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species on Barro Colorado Island (Panama). This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters.