The different impacts of COVID-19 on the mental health of distinct health care worker categories.

The present study sought to explore the factors associated with the odds of having probable depression and posttraumatic stress disorder (PTSD) related to traumatic COVID-19 experiences and their impact on health care workers in distinct categories. In this cross-sectional study, 1843 health care workers (nurses, nurse technicians, physicians, physical therapists, and other healthcare workers) were recruited via convenience sampling. A survey was administered to obtain information regarding sociodemographic, occupational, and mental health status. Descriptive statistics and multivariable logistic regression were used for the analyses. Being a nurse technician was associated with an odds ratio of 1.76 for probable PTSD. No relation was observed between health care worker categories and the odds of probable depression. Additionally, being female and not receiving adequate PPE were related to greater odds of having probable PTSD and depression.

Desempenho econômico-financeiro de empresas do agronegócio da carne, listadas na B3, antes e após o início da Covid-19

Este estudo objetivou analisar o desempenho econômico-financeiro das quatro maiores empresas do Setor do Agronegócio da carne listadas na B3 S.A., antes e após o início da Pandemia do Coronavírus (período de 2018 a 2020). A análise foi realizada por meio de indicadores financeiros de estrutura de capital e rentabilidade, com foco em especial na Identidade DuPont, calculados com base em valores de contas contábeis das demonstrações financeiras das empresas. A coleta foi realizada por meio das demonstrações contábeis estruturadas e apresentadas no site da B3 S.A. e com o uso da base de dados Economática. A pesquisa se caracteriza como descritiva, de caráter documental e de abordagem quantitativa. Em comparação aos percentuais correspondentes aos indicadores de cada ano e ao cenário econômico do período, a pesquisa utilizou a Correlação de Pearson para a análise das relações entre as variações dos indicadores com relação à variação do Produto Interno Bruto (PIB) do Ramo Pecuário, no período estudado. Entre os principais achados, evidencia-se uma melhora significativa do Retorno sobre o Ativo e do Retorno sobre o Patrimônio Líquido, em correlações positivas médias e fortes com o crescimento do PIB do setor. Além disso, encontrou-se correlação positiva extremamente forte entre a Receita de Bens e Serviços das empresas e o PIB do Ramo Pecuário. Conclui-se que, no geral, além do crescimento do PIB do setor, apesar da crise econômica resultante da pandemia, as empresas elevaram seu faturamento e melhoraram a lucratividade e a rentabilidade.Alternate :This study aimed to analyze the economic and financial performance of the four largest companies in the Meat Agribusiness Sector listed on B3 S.A., before and after the onset of the Coronavirus Pandemic (period from 2018 to 2020). The analysis was carried out by means of financial indicators of capital structure and profitability, focusing in particular on the DuPont Identity, calculated based on values of accounting accounts in the companies' financial statements. The collection was performed through the structured financial statements presented on the B3 S.A. website and with the use of the Economática database. The research is characterized as descriptive, documentary, and quantitative. Comparing the percentages corresponding to the indicators for each year and the economic scenario of the period, the research used Pearson's Correlation to analyze the relationship between the variations of the indicators with respect to the variation of the Gross Domestic Product (GDP) of the Livestock Branch, in the period studied. Among the main findings, a significant improvement of the Return on Assets and Return on Equity, in medium and strong positive correlations with the sector's GDP growth, is evident. Furthermore, an extremely strong positive correlation was found between the companies' Revenue from Goods and Services and the Livestock Industry GDP. It is concluded that, overall, in addition to the sector's GDP growth, despite the economic crisis resulting from the pandemic, companies raised their revenues and improved profitability and profitability.

Tonic immobility is associated with posttraumatic stress symptoms in healthcare professionals exposed to COVID-19-related trauma.

The Coronavirus Disease 2019 (COVID-19) pandemic has exposed healthcare workers (HCW) to traumatic situations that might lead to the development of posttraumatic stress disorder (PTSD). An important vulnerability factor for PTSD is the peritraumatic tonic immobility (TI) reaction, an involuntary and reflexive defensive response evoked by an intense and inescapable threat. TI is largely understudied in humans and has not been investigated during trauma related to COVID-19. For HCW, the pandemic context might be experienced as an intense and potentially inescapable threat, i.e., an overwhelming situation. Here, we investigated if TI response occurred during traumatic events related to the pandemic and its association with posttraumatic stress symptoms (PTSS). An online survey of 1001 HCW investigated COVID-19-related traumatic experiences, TI and PTSS. TI was reported for all types of traumatic events, and multivariate regression models revealed that TI was significantly associated with PTSS severity. HCW who reported high TI scores exhibited an increase of 9.08 times the probability of having a probable diagnosis of PTSD. Thus, TI was evoked by pandemic-related traumatic situations and associated with PTSS severity and higher odds of a PTSD diagnosis. Tonic immobility occurrence should be screened, and psychoeducation about its reflexive biological nature should be introduced.

Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach.

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19. Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers. Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms. Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.

Facing Racism and Sexism in Science by Fighting Against Social Implicit Bias: A Latina and Black Woman's Perspective.

The editors of several major journals have recently asserted the importance of combating racism and sexism in science. This is especially relevant now, as the COVID-19 pandemic may have led to a widening of the gender and racial/ethnicity gaps. Implicit bias is a crucial component in this fight. Negative stereotypes that are socially constructed in a given culture are frequently associated with implicit bias (which is unconscious or not perceived). In the present article, we point to scientific evidence that shows the presence of implicit bias in the academic community, contributing to strongly damaging unconscious evaluations and judgments of individuals or groups. Additionally, we suggest several actions aimed at (1) editors and reviewers of scientific journals (2) people in positions of power within funding agencies and research institutions, and (3) members of selection committees to mitigate this effect. These recommendations are based on the experience of a group of Latinx American scientists comprising Black and Latina women, teachers, and undergraduate students who participate in women in science working group at universities in the state of Rio de Janeiro, Brazil. With this article, we hope to contribute to reflections, actions, and the development of institutional policies that enable and consolidate diversity in science and reduce disparities based on gender and race/ethnicity.

Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences.

The Covid-19 pandemic, a disease transmitted by the SARS-CoV-2 virus, has already caused the infection of more than 120 million people, of which 70 million have been recovered, while 3 million people have died. The high speed of infection has led to the rapid depletion of public health resources in most countries. RT-PCR is Covid-19's reference diagnostic method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach and represented by co-occurrence matrices. This technique eliminates multiple sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. When comparing SARS-CoV-2 with virus families with similar symptoms, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP classifier and 30% overlap. When SARS-CoV-2 is compared to other coronaviruses and healthy human DNA sequences, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify DNA sequences for SARS-CoV-2 with greater specificity and sensitivity.

Gender, Race and Parenthood Impact Academic Productivity During the COVID-19 Pandemic: From Survey to Action.

The coronavirus disease 2019 (COVID-19) pandemic is altering dynamics in academia, and people juggling remote work and domestic demands - including childcare - have felt impacts on their productivity. Female authors have faced a decrease in paper submission rates since the beginning of the pandemic period. The reasons for this decline in women's productivity need to be further investigated. Here, we analyzed the influence of gender, parenthood and race on academic productivity during the pandemic period based on a survey answered by 3,345 Brazilian academics from various knowledge areas and research institutions. Productivity was assessed by the ability to submit papers as planned and to meet deadlines during the initial period of social isolation in Brazil. The findings revealed that male academics - especially those without children - are the least affected group, whereas Black women and mothers are the most impacted groups. These impacts are likely a consequence of the well-known unequal division of domestic labor between men and women, which has been exacerbated during the pandemic. Additionally, our results highlight that racism strongly persists in academia, especially against Black women. The pandemic will have long-term effects on the career progression of the most affected groups. The results presented here are crucial for the development of actions and policies that aim to avoid further deepening the gender gap in academia.