POTENCIAL ANTIBACTERIANO DO COGUMELO COMESTÍVEL Pleurotus ostreatus FRENTE À Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853 E Escherichia coli ATCC 25922

Este trabalho reporta o potencial antibacteriano do cogumelo comestível Pleurotus ostreatus. Extratos orgânicos, bem como β-glucânico e quitosânico, ambos nas concentrações de 100, 300 e 500 ppm, foram submetidos a ensaios de disco-difusão contra quatro bactérias padrões: duas Gram positivas (Staphylococcus aureus ATCC 25923 e Enterococcus faecalis ATCC 29212) e duas Gram negativas (Escherichia coli ATCC 25922 e Pseudomonas aeruginosa ATCC 27853). Os extratos que apresentaram halo de inibição (HI) do crescimento bacteriano iguais a 8 mm foram classificados como ativos. Dos extratos testados, o extrato β-glucânico se mostrou ativo, em ambas concentrações, contra a bactéria Pseudomonas aeruginosa ATCC 27853 e na concentração de 500 ppm contra as bactérias Staphylococcus aureus ATCC 25923 e Escherichia coli ATCC 25922. O extrato quitosânico apresentou atividade somente contra Pseudomonas aeruginosa ATCC 27853 na concentração de 500 ppm. Estes resultados são considerados promissores, pois apontam o extrato β-glucânico com potencial contra bactérias Gram positivas e Gram negativas. Futuros estudos bioguiados sob a ação antibacteriana desse extrato devem ser realizados para produção, purificação e caracterização físico-química do constituinte bioativo de interesse farmacológico

Erratum to: The study of cardiovascular risk in adolescents – ERICA: rationale, design and sample characteristics of a national survey examining cardiovascular risk factor profile in Brazilian adolescents

1585

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