Sensibilidade à cefoxitina, cefoperazona e ticarcilina-ácido clavulânico de cepas do grupo Bacteroides fragilis isoladas de espécimes clínicos

Um total de 40 cepas do grupo B. fragilis foi isolada de espécimes clínicos em dois centros hospitalares de Fortaleza no período de 1993 a 1997. A espécie mais frequentemente isolada foi Bacteroides fragilis (19 cepas) tendo a maioria dos microrganismos sido isolada de infecção intra-abdominal e ferida cirúrgica infectada. Foi traçado o perfil de sensibilidade à cefoxitina, cefoperazona e associação ticarcilina-ácido clavulânico, utilizando-se o método de referência de diluição em ágar. Todas as espécies testadas apresentaram sensibilidade à ticarcilina-ácido clavulânico (128/2mig/ml). Percentuais de resistência de 15 e 70% foram detectados para cefoxitina (64mig/ml) e cefoperazona (64mig/ml) respectivamente. A espécie B. fragilis apresentou os menores percentuais de resistência quando comparada com as demais espécies do grupo. Estes resultados regionais permitem uma melhor orientação na escolha deste grupo de antibióticos, para profilaxia ou terapêutica, principalmente com relação à cefoxitina que é frequentemente empregada nos centros hospitalares estudados.A total of 40 strains of the B. fragilis group was isolated from clinical specimens in two hospital centers in Fortaleza from 1993 to 1997. The most frequently isolated species was Bacteroides fragilis (19 strains) and most isolates came from intra-abdominal and wound infections. The susceptibility profile was traced for cefoxitin, cefoperazone and ticarcillin-clavulanate by using the agar dilution reference method. All isolates were susceptible to ticarcillin-clavulanate (128/2mug/ml). Resistance rates of 15 and 70% were detected to cefoxitin (64mug/ml) and cefoperazone (64mug/ml), respectively. Such regional results permit a better orientation in choosing this group of antibiotics for prophylaxis and therapy especially in relation to cefoxitin, which is frequently used in the hospital centers studied

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