Cryptosporidium sp. in children suffering from acute diarrhea at Uberlândia City, State of Minas Gerais, Brazil

This study's objective was to search for Cryptosporidium sp. in diarrheic feces from children aged zero to 12 years and cared for at medical units within Universidade Federal de Uberlândia or at a private practice in Uberlândia, State of Minas Gerais, Brazil, from September 1992 to August 1993. Three fecal samples preserved in 10% formalin, were collected from 94 children. Oocyst concentration was performed through Ritchie's (modified) method and staining of fecal smears for each sample (total of 1128 slides) was done by the "Safranin/Methylene Blue" and the "Kinyoun (modified)" techniques. The Hoffmann, Pons & Janer method was also employed to look for other enteroparasites. From 94 children, 4.26% excreted fecal Cryptosporidium oocysts. The infection seemed to vary according to age: 5.08% of patients aged zero to two years old; 33.33% of those aging eight to ten years (P>0.05). Cryptosporidium appeared in November, December and March, during the rainy season. 20.21% of the children harbored at least one enteroparasite different from Cryptosporidium, mainly Giardia intestinalis (12.77%). From Cryptosporidium infected patients, two had only this kind, another harbored Giardia intestinalis; the last one hosted Strongyloides stercoralis

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

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

Cryptosporidium sp. in Children Suffering from Acute Diarrhea at Uberlandia City, State of Minas Gerais, Brazil

This study's objective was to search for Cryptosporidium sp. in diarrheic feces from children aged zero to 12 years and cared for at medical units within Universidade Federal de Uberlandia or at a private practice in Uberlandia, State of Minas Gerais, Brazil, from September 1992 to August 1993. Three fecal samples preserved in 10% formalin, were collected from 94 children. Oocyst concentration was performed through Ritchie's (modified) method and staining of fecal smears for each sample (total of 1128 slides) was done by the "Safranin/Methylene Blue" and the "Kinyoun (modified)" techniques. The Hoffmann, Pons & Janer method was also employed to look for other enteroparasites. From 94 children, 4.26% excreted fecal Cryptosporidium oocysts. The infection seemed to vary according to age: 5.08% of patients aged zero to two years old; 33.33% of those aging eight to ten years (P}0.05). Cryptosporidium appeared in November, December and March, during the rainy season. 20.21% of the children harbored at least one enteroparasite different from Cryptosporidium, mainly Giardia intestinalis (12.77%). From Cryptosporidium infected patients, two had only this kind, another harbored Giardia intestinalis; the last one hosted Strongyloides stercoralis