RT-qPCR is the gold standard technique available for SARS-CoV-2 detection. However, the long test run time and costs associated with this type of molecular testing are a challenge in a pandemic scenario. Due to high testing demand, especially for monitoring highly vaccinated populations facing the emergence of new SARS-CoV-2 variants, strategies that allow the increase in testing capacity and cost savings are needed. We evaluated a RT-qPCR pooling strategy either as a simplex and multiplex assay, as well as performed in-silico statistical modeling analysis validated with specimen samples obtained from a mass testing program of Industry Federation of the State of Rio de Janeiro (Brazil). Although the sensitivity reduction in samples pooled with 32 individuals in a simplex assay was observed, the high-test sensitivity was maintained even when 16 and 8 samples were pooled. This data was validated with the results obtained in our mass testing program with a cost saving of 51.5% already considering the expenditures with pool sampling that were analyzed individually. We also demonstrated that the pooling approach using 4 or 8 samples tested with a triplex combination in RT-qPCR is feasible to be applied without sensitivity loss, mainly combining Nucleocapsid (N) and Envelope (E) gene targets. Our data shows that the combination of pooling in a RT-qPCR multiplex assay could strongly contribute to mass testing programs with high-cost savings and low-reagent consumption while maintaining test sensitivity. In addition, the test capacity is predicted to be considerably increased which is fundamental for the control of the virus spread in the actual pandemic scenario.

O gig work é uma forma emergente de trabalho que vem ganhando atenção por induzir mudanças nas dinâmicas de poder entre empregadores e empregados. Uma questão premente é a disponibilidade limitada de informações para um encaixe pessoa-emprego: na maioria das vezes, um retrato de perfil e uma pequena biografia são as únicas informações disponíveis para seleção, tornando essa forma de seleção sujeita a preconceitos sobre raça, sexo ou idade. Este estudo visa explorar as relações entre empregabilidade e atributos faciais usando um pipeline de processamento de imagem proposto para avaliar atributos raciais, sexuais, idade percebida e emoção percebida usando o conjunto de dados de treinamento ChaLearn Looking at People First Impressions V2.

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