Estudo de alguns compostos bioativos das pitayas de polpas branca e vermelha (Cereus undatus, Sinonímia: Hylocereus guatemalensis, H.undatus) / Study of some bioactive compounds of white and red pulp pitayas (Cereus undatus, Synonymy: Hylocereus guatemalensis, H.undatus)

A pitaya (Cereus undatus, sinonímia: Hylocereus guatemalensis, H.undatus) é uma fruta exótica e de consumo ligeiramente crescente no nosso país. As atribuições funcionais dadas a essa fruta, pelo senso comum, incita ao estudo das suas características físicas, químicas e microbiológicas. Deve-se ressaltar que as frutas são fontes primárias de várias vitaminas e outros compostos bioativos, como por exemplo, os antioxidantes, vitaminas e açúcares. A ingestão desses compostos aumenta a imunidade dos indivíduos, induzindo a melhores níveis de saúde e melhorando o seu rendimento físico e mental. Os valores de referência para a pitaya, ainda, são desconhecidos do grande público, por ser esta uma fruta de consumo de uma classe abastada, por seu preço ser demasiadamente alto para os nossos padrões brasileiros. As matrizes em alimentos são muito complexas, dadas as suas características naturais. Diante disso, várias são as técnicas utilizadas para determinações analíticas de compostos bioativos, dentre elas, têm-se a Cromatografia Líquida de Alta Eficiência (CLAE) e espectrofotometria U.V visível. O objetivo deste trabalho é estudar a presença de vitamina C e açúcares nas pitayas de polpas branca e vermelha por CLAE, bem como, determinar, ainda, o teor de atividade antioxidante pelo método de captura do radical 2,2’- azinobis (3- etilbenzenotiazolina-6-ácido sulfônico – ABTS), teores de sólidos solúveis (ºBrix), além da acidez e pH

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

Aspectos anatomopatológicos das neoplasias malignas renais: Anatomopathological aspects of malignant renal neoplasms

As neoplasias renais correspondem ao crescimento exacerbado de células tumorais no interior dos rins, classificadas como benignas ou malignas. Neste estudo será abordado sobre as neoplasias malignas renais, a qual correspondem a maior prevalência e são representadas pelo carcinoma de células renais e o tumor de Wilms, com a finalidade de descrever a respeito dos aspectos anatomopatológicos, disseminando informações para o diagnóstico e manejo precoce. O carcinoma de células renais é mais prevalente no sexo masculino, indivíduos mais velhos, geralmente assintomático, contribuindo para o diagnóstico tardio junto a existência de metástases e terapêutica irresponsiva. Não se trata de uma doença genética, sendo o caráter esporádico o predominante, neste contexto os fatores de risco, sobretudo o tabagismo em seguida de obesidade hemodiálise e doenças genéticas são potenciais desencadeantes da enfermidade. Os exames complementares associado a clínica, junto ao acompanhamento eleva a possibilidade de identificação antes de avanços metastáticos. O tumor de Wilms é típico de crianças, acometendo um ou ambos os rins, normalmente com alguma anomalia genética, sendo os sinais inespecíficos, mas sempre manifestando massa palpável e dor abdominal, a qual os métodos de imagem confirmam o diagnóstico e estimam o prognóstico deste. Neste contexto, elucida-se a transcendência que os aspectos anatomopatológicos das neoplasias malignas renais oferecem para a diagnose precoce, devido a escassez e inespecificidafe das manifestações clínicas. Logo, a junção do perfil de cada neoplasia abordado conduz ao manejo adequado e reduz a incidência de tratamentos agressivos e irresponsivos

Understanding the relation between Zika virus infection during pregnancy and adverse fetal, infant and child outcomes: a protocol for a systematic review and individual participant data meta-analysis of longitudinal studies of pregnant women and their infants and children

IntroductionZika virus (ZIKV) infection during pregnancy is a known cause of microcephaly and other congenital and developmental anomalies. In the absence of a ZIKV vaccine or prophylactics, principal investigators (PIs) and international leaders in ZIKV research have formed the ZIKV Individual Participant Data (IPD) Consortium to identify, collect and synthesise IPD from longitudinal studies of pregnant women that measure ZIKV infection during pregnancy and fetal, infant or child outcomes.Methods and analysisWe will identify eligible studies through the ZIKV IPD Consortium membership and a systematic review and invite study PIs to participate in the IPD meta-analysis (IPD-MA). We will use the combined dataset to estimate the relative and absolute risk of congenital Zika syndrome (CZS), including microcephaly and late symptomatic congenital infections; identify and explore sources of heterogeneity in those estimates and develop and validate a risk prediction model to identify the pregnancies at the highest risk of CZS or adverse developmental outcomes. The variable accuracy of diagnostic assays and differences in exposure and outcome definitions means that included studies will have a higher level of systematic variability, a component of measurement error, than an IPD-MA of studies of an established pathogen. We will use expert testimony, existing internal and external diagnostic accuracy validation studies and laboratory external quality assessments to inform the distribution of measurement error in our models. We will apply both Bayesian and frequentist methods to directly account for these and other sources of uncertainty.Ethics and disseminationThe IPD-MA was deemed exempt from ethical review. We will convene a group of patient advocates to evaluate the ethical implications and utility of the risk stratification tool. Findings from these analyses will be shared via national and international conferences and through publication in open access, peer-reviewed journals.Trial registration numberPROSPERO International prospective register of systematic reviews (CRD42017068915).</jats:sec

ATLANTIC-PRIMATES: a dataset of communities and occurrences of primates in the Atlantic Forests of South America

Primates play an important role in ecosystem functioning and offer critical insights into human evolution, biology, behavior, and emerging infectious diseases. There are 26 primate species in the Atlantic Forests of South America, 19 of them endemic. We compiled a dataset of 5,472 georeferenced locations of 26 native and 1 introduced primate species, as hybrids in the genera Callithrix and Alouatta. The dataset includes 700 primate communities, 8,121 single species occurrences and 714 estimates of primate population sizes, covering most natural forest types of the tropical and subtropical Atlantic Forest of Brazil, Paraguay and Argentina and some other biomes. On average, primate communities of the Atlantic Forest harbor 2 ± 1 species (range = 1–6). However, about 40% of primate communities contain only one species. Alouatta guariba (N = 2,188 records) and Sapajus nigritus (N = 1,127) were the species with the most records. Callicebus barbarabrownae (N = 35), Leontopithecus caissara (N = 38), and Sapajus libidinosus (N = 41) were the species with the least records. Recorded primate densities varied from 0.004 individuals/km 2 (Alouatta guariba at Fragmento do Bugre, Paraná, Brazil) to 400 individuals/km 2 (Alouatta caraya in Santiago, Rio Grande do Sul, Brazil). Our dataset reflects disparity between the numerous primate census conducted in the Atlantic Forest, in contrast to the scarcity of estimates of population sizes and densities. With these data, researchers can develop different macroecological and regional level studies, focusing on communities, populations, species co-occurrence and distribution patterns. Moreover, the data can also be used to assess the consequences of fragmentation, defaunation, and disease outbreaks on different ecological processes, such as trophic cascades, species invasion or extinction, and community dynamics. There are no copyright restrictions. Please cite this Data Paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. © 2018 by the The Authors. Ecology © 2018 The Ecological Society of Americ

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