Desnutrição associada à giardíase em escolares: Análise de parâmetros antropométricos e socioeconômicos / Malnutrition associated with giardiasis in school children: Analysis of anthropometric and socioeconomic parameters

Giardia lamblia é um dos principais protozoários que causa diarréia em crianças de países em desenvolvimento, assim esse trabalho teve como objetivo avaliar a prevalência de infecção por Giardia sp nas crianças de uma creche pública do Recife, correlacionando os aspectos antropométricos e socioeconômicos, tais como: tabagismo na família, aleitamento materno, renda e escolaridade materna. A frequência foi determinada através de amostras fecais processadas de acordo com os métodos de Hoffman e Faust. Crianças com infecção por outros parasitos foram eliminadas do estudo. A prevalência foi correlacionada com aspectos antropométricos e sociais. A análise desses dados mostrou haver associação estatisticamente significante da infecção por Giardia sp com os baixos índices de IMC (p=0,0293) e com o baixo grau de aleitamento materno (p=0,0447), além de mostrar associação parcialmente significante com a baixa renda familiar (p=0,09). Os resultados sugerem haver associação entre a infecção e desnutrição e aleitamento materno inadequado (por um período menor que 6 meses). A renda familiar sugere uma forte tendência à infecção nas classes sociais mais baixas

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

A Methylene Blue-Enhanced Nanostructured Electrochemical Immunosensor for H-FABP Myocardial Injury Biomarker

A sensitive electrochemical immunosensor for the detection of the heart-type fatty acid binding protein (HFABP), an earlier biomarker for acute myocardial infarction than Troponins, is described. The sensing platform was enhanced with methylene blue (MB) redox coupled to carbon nanotubes (CNT) assembled on a polymer film of polythionine (PTh). For this strategy, monomers of thionine rich in amine groups were electrosynthesized by cyclic voltammetry on the immunosensor’s gold surface, forming an electroactive film with excellent electron transfer capacity. Stepwise sensor surface preparation was electrochemically characterized at each step and scanning electronic microscopy was carried out showing all the preparation steps. The assembled sensor platform combines MB and PTh in a synergism, allowing sensitive detection of the H-FABP in a linear response from 3.0 to 25.0 ng∙mL−1 with a limit of detection of 1.47 ng∙mL−1 HFABP that is similar to the clinical level range for diagnostics. H-FABP is a newer powerful biomarker for distinguishing between unstable angina and acute myocardial infarction