Universal Screening of SARS-CoV-2 of Oncology Healthcare Workers — a Brazilian experience

The first confirmed case of coronavirus disease 2019 (COVID-19) in Brazil and Latin America was reported on February 26, 2020, in São Paulo. The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has placed unprecedented strain on health-care services worldwide. Asymptomatic health-care workers (HCW) are a potential source of SARS-CoV-2 transmission, especially to immunocompromised Oncology patients. Screening of these HCWs may help contain transmission and isolate only those who require it. At Centro de Terapia Oncológica (CTO), an Oncology clinic in Petrópolis, RJ —Brazil, all HCWs were screened for SARS-CoV-2 in order to isolate those who were asymptomatic/symptomatic and positive for the virus. They were all tested through IgM/IgG rapid testing and those who had symptoms were also tested with nasopharyngeal swabs for reverse transcription polymerase chain reaction (RT-PCR) tests besides IgM/IgG reactivity. Amongst the 60 tested employees, 4 were positive for SARS-CoV-2 and were isolated. This method may be useful for health-care services to detect asymptomatic HCW and maintain workers’ and patients’ health, as transmission probability could be reduced while avoiding becoming short-staffed during this time of crisis

Microbial community structure and dynamics in thermophilic composting viewed through metagenomics and metatranscriptomics

Composting is a promising source of new organisms and thermostable enzymes that may be helpful in environmental management and industrial processes. Here we present results of metagenomicand metatranscriptomic-based analyses of a large composting operation in the Sao Paulo Zoo Park. This composting exhibits a sustained thermophilic profile (50 degrees C to 75 degrees C), which seems to preclude fungal activity. The main novelty of our study is the combination of time-series sampling with shotgun DNA, 16S rRNA gene amplicon, and metatranscriptome high-throughput sequencing, enabling an unprecedented detailed view of microbial community structure, dynamics, and function in this ecosystem. The time-series data showed that the turning procedure has a strong impact on the compost microbiota, restoring to a certain extent the population profile seen at the beginning of the processand that lignocellulosic biomass deconstruction occurs synergistically and sequentially, with hemicellulose being degraded preferentially to cellulose and lignin. Moreover, our sequencing data allowed near-complete genome reconstruction of five bacterial species previously found in biomass-degrading environments and of a novel biodegrading bacterial species, likely a new genus in the order Bacillales. The data and analyses provided are a rich source for additional investigations of thermophilic composting microbiology.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Provost's Office for Research of the University of Sao PauloCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Sao Paulo, Inst Quim, Dept Bioquim, Sao Paulo, BrazilUniv Sao Paulo, Programa Pos Graduacao Interunidades Bioinformat, Sao Paulo, BrazilUniv Sao Paulo, Escola Artes Ciencias & Humanidades, Sao Paulo, Brazil|Fundacao Parque Zool Sao Paulo, Sao Paulo, BrazilUniv Fed Sao Paulo, Dept Ciencias Biol, Sao Paulo, BrazilBiocomplex Inst Virginia, Blacksburg, VA USADepartamento de Ciências Biológicas, Universidade Federal de São Paulo, São Paulo, BrazilFAPESP: 2011/50870-6Web of Scienc

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Endividamento: um estudo do pré e pós da crise empresarial brasileira na pandemia do COVID-19

A economia brasileira, desde antes da pandemia, vinha passando por um processo de desaceleração, enfrentando dificuldades para o seu crescimento. Com o cenário pandêmico da Covid-19, as empresas sofreram ainda mais com esse momento de crise, devido a paralisação das atividades presenciais. Dessa forma, para a obtenção dos resultados da pesquisa, teve-se como objetivo geral do presente estudo, analisar e comparar o estado econômico das empresas brasileiras, listadas na B3. Neste estudo, foram aplicados sete indicadores financeiros, os quais são ferramentas utilizadas pelos gestores na compreensão dos números das suas empresas. Quanto à metodologia, decorreu de uma pesquisa descritiva e quantitativa utilizado o teste T de Student e o teste F de Levene. A partir das informações extraídas dos testes estatísticos há indícios de que houve um aumento significativo, do ponto de vista estatístico, do endividamento geral da empresa contribuindo para o aumento do risco dos negócios

Effect of flexible family visitation on delirium among patients in the Intensive Care Unit: the ICU visits randomized clinical trial

Fernando Augusto Bozza. Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Documento produzido em parceria ou por autor vinculado à Fiocruz, mas não consta a informação no documento.Intensive Care Unit, Hospital Moinhos de Vento (HMV), Porto Alegre, Rio Grande do Sul, Brazil (Rosa, D. B. da Silva, Eugênio, Haack, Medeiros, Tonietto, Teixeira); Research Projects Office, HMV, Porto Alegre, Rio Grande do Sul, Brazil (Rosa, Falavigna, D. B. da Silva, Sganzerla, Santos, Kochhann, de Moura, Eugênio, Haack, Barbosa, Robinson, Schneider, de Oliveira, Jeffman, Medeiros, Hammes); Brazilian Research in Intensive Care Network (BRICNet), São Paulo, São Paulo (Rosa, Cavalcanti, Machado, Azevedo, Salluh, Nobre, Bozza, Teixeira); HCor Research Institute, São Paulo, São Paulo, Brazil (Cavalcanti); Department of Anesthesiology, Pain and Intensive Care, Universidade Federal de São Paulo (UNIFESP), São Paulo, São Paulo, Brazil (Machado); Intensive Care Unit, Hospital Sírio-Libanês, São Paulo, São Paulo, Brazil (Azevedo); Department of Critical Care, Instituto D’Or de Pesquisa e Ensino, Rio de Janeiro, Rio de Janeiro, Brazil (Salluh, Mesquita, Bozza); Intensive Care Unit, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Rio Grande do Sul, Brazil (Pellegrini, Moraes); Intensive Care Unit, Hospital Santa Cruz, Santa Cruz do Sul, Rio Grande do Sul, Brazil (Foernges); Intensive Care Unit, Hospital Santa Rita, Porto Alegre, Rio Grande do Sul, Brazil (Torelly); Intensive Care Unit, Hospital Universitário do Oeste do Paraná, Cascavel, Paraná, Brazil (Ayres, Duarte); Intensive Care Unit, Hospital do Câncer de Cascavel, Cascavel, Paraná, Brazil (Duarte); Intensive Care Unit, Hospital das Clínicas, Faculdade de Medicina de Ribeirão Preto, Ribeirão Preto, São Paulo, Brazil (Lovato); Intensive Care Unit, Santa Casa de Misericórdia de Feira de Santana, Feira de Santana, Bahia, Brazil (Sampaio); Intensive Care Unit, Hospital Geral Clériston Andrade, Feira de Santana, Bahia, Brazil (de Oliveira Júnior); Intensive Care Unit, Santa Casa de Misericórdia de São João Del Rei, São João Del Rei, Minas Gerais, Brazil (Paranhos); Intensive Care Unit, Hospital Regional Doutor Deoclécio Marques de Lucena, Parnamirim, Rio Grande do Norte, Brazil (Dantas, de Brito); Intensive Care Unit, Fundação Hospital Adriano Jorge, Manaus, Amazonas, Brazil (Paulo); Intensive Care Unit, Hospital Agamenon Magalhães, Recife, Pernambuco, Brazil (Gallindo); Intensive Care Unit, Hospital da Cidade, Passo Fundo, Rio Grande do Sul, Brazil (Pilau); Intensive Care Unit, Hospital Mãe de Deus, Porto Alegre, Rio Grande do Sul, Brazil (Valentim); Intensive Care Unit, Hospital de Urgências de Goiânia, Goiânia, Goiânia, Brazil (Meira Teles); Intensive Care Unit, Hospital das Clínicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil (Nobre); Intensive Care Unit, Pavilhão Pereira Filho, Porto Alegre, Rio Grande do Sul, Brazil (Birriel); Intensive Care Unit, Hospital Regional do Baixo Amazonas, Santarém, Pará, Brazil (Corrêa e Castro); Intensive Care Unit, Hospital Nossa Senhora da Conceição, Porto Alegre, Rio Grande do Sul, Brazil (Specht); School of Medicine, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Rio Grande do Sul, Brazil (N. B. da Silva); Department of Public Health Sciences, Medical University of South Carolina, Charleston (Korte); Unit of Pediatric Anesthesia and Intensive Care, Ospedale dei Bambini—ASST Spedali Civili, Brescia, Italy (Giannini); Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Rio de Janeiro, Brazil (Bozza).Submitted by Janaína Nascimento ([email protected]) on 2019-09-11T14:37:38Z No. of bitstreams: 1 ve_Rosa_Regis_etal_INI_2019.pdf: 616825 bytes, checksum: 2aae5be305137324e272a08cc32e9270 (MD5)Approved for entry into archive by Janaína Nascimento ([email protected]) on 2019-09-11T14:52:11Z (GMT) No. of bitstreams: 1 ve_Rosa_Regis_etal_INI_2019.pdf: 616825 bytes, checksum: 2aae5be305137324e272a08cc32e9270 (MD5)Made available in DSpace on 2019-09-11T14:52:11Z (GMT). No. of bitstreams: 1 ve_Rosa_Regis_etal_INI_2019.pdf: 616825 bytes, checksum: 2aae5be305137324e272a08cc32e9270 (MD5) Previous issue date: 2019Múltipla - Ver em Notas.IMPORTANCE: The effects of intensive care unit (ICU) visiting hours remain uncertain. OBJECTIVE: To determine whether a flexible family visitation policy in the ICU reduces the incidence of delirium. DESIGN, SETTING AND PARTICIPANTS: Cluster-crossover randomized clinical trial involving patients, family members, and clinicians from 36 adult ICUs with restricted visiting hours (<4.5 hours per day) in Brazil. Participants were recruited from April 2017 to June 2018, with follow-up until July 2018. INTERVENTIONS: Flexible visitation (up to 12 hours per day) supported by family education (n = 837 patients, 652 family members, and 435 clinicians) or usual restricted visitation (median, 1.5 hours per day; n = 848 patients, 643 family members, and 391 clinicians). Nineteen ICUs started with flexible visitation, and 17 started with restricted visitation. MAIN OUTCOMES AND MEASURES: Primary outcome was incidence of delirium during ICU stay, assessed using the CAM-ICU. Secondary outcomes included ICU-acquired infections for patients; symptoms of anxiety and depression assessed using the HADS (range, 0 [best] to 21 [worst]) for family members; and burnout for ICU staff (Maslach Burnout Inventory). RESULTS: Among 1685 patients, 1295 family members, and 826 clinicians enrolled, 1685 patients (100%) (mean age, 58.5 years; 47.2% women), 1060 family members (81.8%) (mean age, 45.2 years; 70.3% women), and 737 clinicians (89.2%) (mean age, 35.5 years; 72.9% women) completed the trial. The mean daily duration of visits was significantly higher with flexible visitation (4.8 vs 1.4 hours; adjusted difference, 3.4 hours [95% CI, 2.8 to 3.9]; P < .001). The incidence of delirium during ICU stay was not significantly different between flexible and restricted visitation (18.9% vs 20.1%; adjusted difference, −1.7% [95% CI, −6.1% to 2.7%]; P = .44). Among 9 prespecified secondary outcomes, 6 did not differ significantly between flexible and restricted visitation, including ICU-acquired infections (3.7% vs 4.5%; adjusted difference, −0.8% [95% CI, −2.1% to 1.0%]; P = .38) and staff burnout (22.0% vs 24.8%; adjusted difference, −3.8% [95% CI, −4.8% to 12.5%]; P = .36). For family members, median anxiety (6.0 vs 7.0; adjusted difference, −1.6 [95% CI, −2.3 to −0.9]; P < .001) and depression scores (4.0 vs 5.0; adjusted difference, −1.2 [95% CI, −2.0 to −0.4]; P = .003) were significantly better with flexible visitation. CONCLUSIONS AND RELEVANCE: Among patients in the ICU, a flexible family visitation policy, vs standard restricted visiting hours, did not significantly reduce the incidence of delirium