Detecção da infecção pelo parvovírus B19 em placenta e tecidos fetais fixados em formalina e embebidos em parafina

Parvovirus B19 infection was first discovered in 1975 and it is implicated in fetal death from hydrops fetalis the world over. Diagnosis is usually made through histological identification of the intranuclear inclusion in placenta and fetal organs. However, these cells may be scarce or uncharacteristic, making definitive diagnosis difficult. We analyzed histologically placentas and fetal organs from 34 cases of non-immune hydrops fetalis, stained with Hematoxylin and Eosin (HE) and submitted to immunohistochemistry and polymerase chain reaction (PCR). Of 34 tissue samples, two (5.9%) presented typical intranuclear inclusion in circulating normoblasts seen in Hematoxylin and Eosin stained sections, confirmed by immunohistochemistry and PCR. However, PCR of fetal organs was negative in one case in which the placenta PCR was positive. We concluded that parvovirus B19 infection frequency is similar to the literature and that immunohistochemistry was the best detection method. It is highly specific and sensitive, preserves the morphology and reveals a larger number of positive cells than does HE with the advantage of showing cytoplasmic and nuclear positivity, making it more reliable. Although PCR is more specific and sensitive in fresh or ideally fixed material it is not so in formalin-fixed paraffin-embedded tissues, frequently the only one available in such cases.O parvovírus B19 foi detectado em 1975 e desde sua descoberta tem se mostrado um agente infeccioso importante em seres humanos, cujo diagnóstico pode ser feito pelo exame histológico através do encontro de inclusão nuclear em tecidos fetais ou placentários. No entanto, estas células podem ser escassas ou não apresentarem características típicas, dificultando o diagnóstico. Analisamos placentas e órgãos fetais de 34 casos de hidropisia fetal não-imune corados com Hematoxilina e Eosina (HE) e submetidos à reação em cadeia da polimerase (PCR) e imuno-histoquímica (IH). Em dois casos (5,9%) houve positividade na placenta pelo HE, IH e PCR. No entanto, PCR dos órgãos fetais foi negativa em um destes casos enquanto que a identificação pôde ser feita por IH e histologia. Concluímos que a freqüência do parvovírus B19 é similar à literatura e a reação IH foi o melhor método de detecção, com identificação mais específica e segura, permitindo identificação citoplasmática, o que não é possível pelo exame histopatológico. A PCR pode apresentar falsa negatividade, provavelmente pela fixação, não identifica as células e é mais dispendiosa. Embora mais específica e sensível em material a fresco ou idealmente fixado isto não ocorre com tecidos fixados em formalina e embebidos em parafina, freqüentemente os únicos disponíveis

Uma nova possibilidade de vigilância: identificamos todos os casos de leptospirose?

Leptospirosis is a febrile disease with a typically underestimated global incidence, especially in regions where dengue is endemic. Therefore, it is difficult to accurately determine the number of leptospirosis cases in these areas, which contributes to significant under-reporting this disease. In this study, we estimated the number of possible leptospirosis cases among dengue-like cases that were reported during 2008, 2010, and 2012 in the city of Fortaleza, northeast Brazil. Patients were evaluated for dengue and leptospirosis using immunoenzymatic tests for IgM antibodies that were specific to each pathogen. Among the suspected cases of dengue that resulted as negative in laboratory tests, 10.8% (2008), 19.2% (2010), and 30.8% (2012) were confirmed to be leptospirosis. Considering the cases reported by the surveillance authority as dengue that were subsequently discarded based on the laboratory test results, we estimate that the number of actual leptospirosis cases may be 26 to 49 times higher than those diagnosed and reported by the Health Services. Furthermore, we believe that approximately 20% of dengue-like cases may be leptospirosis cases in areas where the two diseases are endemic.A leptospirose é doença febril tipicamente subestimada em todo o mundo, principalmente em áreas que a dengue se apresenta de forma endêmica. Desta forma, há limitações importantes na compreensão do número de casos de leptospirose nessas áreas, o que proporciona maior subnotificação. Neste estudo, apresentamos estimativa de possíveis casos de leptospirose a partir de casos de dengue-símile na cidade de Fortaleza, nordeste do Brasil, durante os anos de 2008, 2010 e 2012. Os pacientes foram investigados para dengue e leptospirose utilizando testes imunoezimáticos para detecção do anticorpo, da classe IgM, específicos para cada patologia. Entre os casos suspeitos de dengue, mas que não apresentaram resultado laboratorial positivo, 10,8%; 19,2% e 30,8% foram confirmados como leptospirose nos anos de 2008, 2010 e 2012; respectivamente. Considerando os casos notificados pela vigilância de dengue e que foram, posteriormente, descartados, baseados nos resultados dos testes laboratoriais, estimamos que o número atual de casos de leptospirose pode ser de 26 a 49 vezes mais do que o detectado e notificado pelos serviços de saúde. Além disso, acreditamos que aproximadamente 20% dos casos de dengue-símile podem ser de leptospirose, em áreas onde as duas doenças ocorram de forma endêmica

Dengue: 30 years of cases in an endemic area

The present study aimed to review literature on studies of dengue cases conducted over 30 years in the state of Ceara´. Between November 2015 and January 2016, articles published in Portuguese and English in 7 databases were searched using keywords and a Boolean operator. A total of 191 articles were identified in the databases; 133 were excluded according to the exclusion criteria, and 58 were included in the study. Of the 58 articles analyzed, 6 reported data from Brazil; including the Northeast region and the state of Ceara´; 41 reported data for only the city of Fortaleza; 7 reported data for the state of Ceara´; 4 reported data for cities in the interior of the state; and 3 included only children. The studies adopted different approaches and focused on different aspects of the disease. Study outcomes included the identification of serological, epidemiological, clinical, and laboratory characteristics; potential larvicides and biological predators of mosquitoes; potential antiviral agents; vector density characteristics; and educational dengue prevention and control strategies. Additionally, one vaccine trial was included. Although studies on dengue in the state of Ceara´ are scarce, they are encompassing, including several lines of research, and the number of studies and reports on dengue in the state of Ceara´ continues to increase

Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness

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