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

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info: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

Evaluation of sampling respondent-driven sampling in the estimation of prevalence of diseases in populations organized in complex networks

Made available in DSpace on 2011-05-04T12:36:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2009Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sergio Arouca. Rio de Janeiro, RJ, Brasil.Diversos fatores podem dificultar a caracterização acurada do perfil de umapopulação por amostragem. Se a característica que define a população é de difícil observação seja porque exige testes caros para detecção ou porque é uma característica de comportamento ilegal ou estigmatizado que dificulta a identificação, torna-se praticamente impossível aplicar os métodos clássicos de amostragem, pois não se pode definir uma base de amostragem (sampling frame). Populações desse tipo são conhecidas como populações ocultas, ou escondidas, e alguns exemplos comumente estudados são homens que fazem sexo com homens, trabalhadores do sexo e usuários de drogas. Essa dissertação discute a técnica de amostragem conhecida como Respondent-Driven Sampling (RDS), originalmente proposta por Heckathorn (1997), e que vem sendo amplamente utilizada na estimação de prevalências de doenças transmissíveis em populações ocultas. Esse método pertence à família de amostragens por bola-de-neve, na qual os elementos seguintes da amostra são recrutados a partir da rede de conhecidos dos elementos já presentes na amostra, formando as cadeias de referência. Com este método, além das informações individuais, é possível estudar também as relações entre os indivíduos. O recrutamento por bola de neve não gera uma amostra aleatória, e está sujeito às propriedades das redes sociais das populações em estudo, que deve mudar de lugar para lugar e potencialmente influenciar as medidas de prevalência geradas. As redes sociais são estruturas complexas, e compreender como que a amostragem RDS é influenciada por estas estruturas é um dos objetivos dessa dissertação. Além disso, se o interesse de um estudo epidemiológico é estimar a prevalência de uma doença transmissível, há de se considerar que muitas vezes a própria rede social pode estar correlacionada com as redes de transmissão, gerando potenciais dependências entre o processo de amostragem e a distribuição da variável desfecho. Essa dissertação teve por objetivo avaliar estimativas de prevalência geradas a partir de amostras obtidas com a utilização da metodologia RDS, considerando estruturas populacionais complexas, ou seja, populações com estruturas distintas de ligação entre os indivíduos e de disseminação de doenças. Para isso, foram realizados experimentos de simulação combinando quatro modelos geradores de redes sociais e quatro modelos de distribuição de casos infectados na população. Para cada uma, foram obtidas amostras utilizando RDS e as respectivas prevalências foram estimadas.Com os resultados encontrados, foi possível realizar uma avaliação tanto do RDS como forma de recrutamento, como o modelo proposto por Heckathorn (2002) para a ponderação e estimação de prevalências. Basicamente, três aspectos foram considerados nessa avaliação: 1. o tempo necessário para concluir a amostragem, 2. a precisão das estimativas obtidas, independente da ponderação, e 3. o método deponderação. De forma geral, o método apresentou bons resultados sob esses três aspectos, refletindo a possibilidade de sua utilização, ainda que exigindo cautela. Os achados apresentam-se limitados, pois são escassos os trabalhos que abordem essa metodologia e que permitam estabelecer comparações. Espera-se, no entanto,despertar o interesse para que outros trabalhos nessa linha sejam desenvolvidos.Several factors may hamper the accurate characterization of a population. If the defining feature of the population is difficult to apply - either because it requires expensive tests for detection or because it is a stigmatized or illegal behavior that hinders the identification, it is virtually impossible to apply traditional methods for sampling, because sampling frame cannot be define. The latter are called “hidden populations”, and some examples are men who have sex with men, sexual workers and drug users. This dissertation focus on Respondent-Driven Sampling (RDS), a sampling method originally proposed by Heckathorn (1997), which has been widely used to estimate the prevalence of infectious diseases in hidden populations. RDS is a snowball sampling method, in which new elements for the sample are recruited from the network of the elements already present in the sample, forming reference chains. With this method, besides individual informations, it is also possible to study the relationships between individuals. Snowball sampling does not generate random samples, and its properties are likely to depend on the properties of the social networks underlying the recruitment process, which may change from place to place and potentially influence the measures of prevalence generated. Social networks are complex structures, and understanding how the different implementations of RDS sampling is influenced by these structures is one of the objectives of this dissertation. Moreover, if the interest of an epidemiological study is to estimate the prevalence of a disease, it is should be considered that very often, social network may be correlated with the transmission networks, generating potential dependencies between the process of sampling and distribution of outcome variable. The aim of this dissertation was to assess the behavior of prevalence estimators using RDS data in scenarios of populations organized in complex structures, i.e. Combinations of social networks structures and spreading patterns. To achieve that, theoretical experiments were performed using simulation models combining four generators of social networks and four models of distribution of infected cases in the population. For each one, samples were obtained using RDS and prevalence, estimated. Findings were used to evaluate RDS as a recruiting process itself, as well as Heckathorn’s (2002) model to estimate prevalences. Three aspects were considered in such analyses: 1. the time elapsed before obtaining the sample; 2. the accuracy of the estimates without taking in consideration the weighting strategies; and 3. the weighting strategy. Overall, RDS performed well in these three areas, showing it is a valid method to assess hidden populations, despite the fact its use should be made with the necessary caution. The interpretation of our findings was constrained by the scarcity of studies using the same methodology, what compromised the comparability of our findings. We hope, however, that our findings may foster the development of additional studies in this field

Intervenção farmacêutica e prevenção de eventos adversos

In the present study, we describe the pharmaceutical interventions performed by the pharmacy service of a public orthopaedic hospital at Rio de Janeiro, Brazil. In a retrospective study, we analysed the pharmaceutical interventions performed during a year (from june 2004 to june 2005). The pharmacy service assisted 13.6% of the 5476 patients that were submitted to internation. In 30.4 % of the patients that were directly assisted by the pharmacist was identified the necessity of pharmaceutical interventions, reaching 227 interventions during the period of the study. The physicians were the most requested professionals (71.1%), followed by nurses 16.9%. Eighty four percent of the problems that were detected were related to potential medication errors. Forty nine percent of these errors were prevented by the pharmacists. It was possible to predict some of the potential drug related problems that could be related to these patients as well. Seventy percent of the pharmaceutical interventions were accepted by the professionals. For the interventions related to prescription, sixty percent were accepted. The results suggest that pharmaceutical interventions were effective to prevent drug adverse events, reinforcing the important role of pharmacists in hospital assistance.No presente estudo, são relatadas as intervenções realizadas pelo serviço de farmácia junto ao corpo clínico de uma instituição pública federal, referência nacional para cirurgias de alta complexidade em ortopedia no Rio de Janeiro. Realizou-se estudo retrospectivo no qual foram analisadas as intervenções farmacêuticas realizadas entre junho de 2004 e junho de 2005. O serviço de farmácia atendeu 13,6% dos 5476 pacientes internados neste período. Dos pacientes atendidos, 30,4% necessitaram de pelo menos uma intervenção deste profissional junto ao corpo clínico em algum momento da sua internação, perfazendo um total de 227 intervenções. Os médicos foram os profissionais mais contatados (71,1%), seguidos dos enfermeiros (16,9%) e auxiliares de enfermagem (4,6%). Dos problemas detectados, 84,1% correspondiam a erros, dos quais 49,5% foram prevenidos com as intervenções. A análise dos erros encontrados nos permite sugerir alguns dos principais problemas relacionados a medicamentos apresentados pelos pacientes da instituição. Das intervenções realizadas, 70% foram aceitas, sendo este percentual de 60% para as intervenções relacionadas à prescrição. Os resultados sugerem que as intervenções farmacêuticas foram ferramentas efetivas para a prevenção de eventos adversos, reforçando a importância da assistência farmacêutica para a qualidade da assistência hospitalar

Pharmacokinetics comparison of two pegylated interferon alfa formulations in healthy volunteers

Submitted by Repositório Arca ([email protected]) on 2019-03-07T16:36:23Z No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Approved for entry into archive by monique santos ([email protected]) on 2019-03-27T17:33:50Z (GMT) No. of bitstreams: 2 s40360-017-0192-z.pdf: 602092 bytes, checksum: fffa5d9a393984922af123c83a788c9a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Made available in DSpace on 2019-03-27T17:33:50Z (GMT). No. of bitstreams: 2 s40360-017-0192-z.pdf: 602092 bytes, checksum: fffa5d9a393984922af123c83a788c9a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2018Universidade Federal do Rio Grande do Sul. Centro de Pesquisa Clínica do Hospital de Clínicas de Porto Alegre. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Faculdade de Medicina. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Faculdade de Medicina. Porto Alegre, RS, Brasil.Centro de Pesquisa Biológica. Divisão de Ensaios Clínicos. Havana, Cuba.Centro de Pesquisa Biológica. Divisão de Ensaios Clínicos. Havana, Cuba.Centro de Pesquisa Biológica. Divisão de Ensaios Clínicos. Havana, Cuba.Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Rio de Janeiro, RJ, Brasil.Centro de Pesquisa Biológica. Divisão de Ensaios Clínicos. Havana, Cuba.Background: Several countries have used pegylation technology to improve the pharmacokinetic properties of essential drugs. Recently, a novel interferon alfa-2b protein conjugated to four-branched 12 kDa polyethylene glycol molecules was developed jointly between Cuba and Brazil. The aim of this study was to compare the pharmacokinetic properties of BIP48 (pegylated interferon alfa-2b from Bio-Manguinhos/Fiocruz, Brazil) to those of PEGASYS® (commercially available pegylated interferon alfa-2a from Roche Pharmaceutical). Methods: This phase I, single-centre, randomized, double-blind crossover trial enrolled 31 healthy male volunteers aged 19 to 35 who were allocated to two stages, either side of a 5-week wash-out period, with each arm lasting 14 consecutive days after subcutaneous administration of 180 μg of one formulation or the other (study or comparator). The main outcome variable was serum pegylated interferon concentrations in 15 samples collected during the course of the study and tested using an enzyme immunoassay. Results: There were no differences between formulations in terms of magnitude or absorption parameters. Analysis of time parameters revealed that BIP48 remained in the body significantly longer than PEGASYS® (Tmax: 73 vs. 54 h [p = 0.0010]; MRT: 133 vs. 115 h [p = 0.0324]; ke: 0.011 vs. 0.013 h(−1) [p = 0.0153]; t1/2: 192 vs. 108 h [p = 0.0218]). Conclusion: BIP48 showed the expected pharmacokinetic profile for a pegylated product with a branched molecular structure. Compared to PEGASYS®, the magnitude absorption was similar, but time parameters were consistent with slower elimination. Further studies should be conducted to evaluate the clinical implications of these findings. A phase II-III repeated-dose clinical trial is ongoing to study these findings in patients with chronic hepatitis C virus infection