IMMUNODIAGNOSIS OF HUMAN STRONGYLOIDIASIS: USE OF SIX DIFFERENT ANTIGENIC FRACTIONS FROM Strongyloides venezuelensis PARASITIC FEMALES

SUMMARY The aim of this study was to evaluate six different antigenic fractions from Strongyloides venezuelensis parasitic females for the immunodiagnosis of human strongyloidiasis. Soluble and membrane fractions from S. venezuelensis parasitic females were prepared in phosphate-buffered saline (SSF and SMF, respectively), Tris-HCl (TSF and TMF, respectively), and an alkaline buffer (ASF and AMF, respectively). Serum samples obtained from patients with strongyloidiasis or, other parasitic diseases, and healthy individuals were analyzed by enzyme-linked immunosorbent assay (ELISA). Soluble fractions SSF, TSF, and ASF showed 85.0%, 75.0%, and 80.0% sensitivity and 93.1%, 93.1%, and 87.5% specificity, respectively. Membrane fractions SMF, TMF, and AMF showed 80.0%, 75.0%, and 85.0% sensitivity, and 95.8%, 90.3%, and 91.7% specificity, respectively. In conclusion, the present results suggest that the fractions obtained from parasitic females, especially the SSF and SMF, could be used as alternative antigen sources in the serodiagnosis of human strongyloidiasis

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

Laboratory tests in the detection of extended spectrum beta-lactamase production: National Committee for Clinical Laboratory Standards (NCCLS) screening test, the E-test, the double disk confirmatory test, and cefoxitin susceptibility testing

Extended spectrum beta-lactamase (ESBL) production by Klebsiella sp. and E. coli is an emerging problem. In this study, 107 clinical isolates (53 E. coli, 47 K. pneumoniae and 7 K. oxytoca) screened as ESBL producers by the NCCLS disk diffusion procedure were submitted to a double disk confirmatory test (DDT) and to the E-test double strip for confirmation of ESBL production by demonstration of clavulanic acid inhibition effect (CAIE). Only 72/107 (67%) of the isolates were confirmed as ESBL producers by DDT, with diverse results among species. By the E-test, 58/107 (54%) isolates were confirmed as ESBL producers, and 18/107 (17%) were not determinable. Susceptibility to cefoxitin was found in 57/68 (83%) of strains that did not show CAIE. ESBL detection remains a controversial issue and clinical laboratories are in need of a simple and effective way to recognize strains with this kind of resistance