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

Intrapulmonary vascular dilatation evaluated by 99mTc-MAA scintigraphy and its association with portal hypertension in schistosomiasis.

BACKGROUND: Portal hypertension is responsible for various complications in patients with schistosomiasis, among them intrapulmonary vascular dilations (IPVD). In cirrhotic patients the presence of IPVD is a sign of poor prognosis, but in patients with hepatosplenic schistosomiasis (HSS) there are no studies assessing the significance of this change. The aim of this study was to evaluate the occurrence of IPVD through 99mTc-MAA scintigraphy in patients with HSS and its relationship with clinical, laboratory, endoscopic and ultrasound parameters. METHODS: Cross-sectional study evaluating 51 patients with HSS. Patients were diagnosed with IPVD when the brain uptake of 99mTc-MAA was higher than 6%. Subsequently, they were divided according to presence (G1) or absence (G2) of IPVD and variables were compared between groups. RESULTS: Overall, 51 patients with mean age of 56±12 years were assessed. IPVD was observed in 31 patients (60%). There was no statistically significant differences between groups when clinical, laboratory and endoscopic parameters were compared. Regarding ultrasound parameters, the splenic vein diameter was smaller in G1 (0.9 ± 0.3 cm) compared to G2 (1.2 ± 0.4 cm), p=0.029. CONCLUSION: In patients with HSS, the occurrence of IPVD by 99mTc-MAA scintigraphy was high and was associated with lower splenic vein diameter, which can be a mechanism of vascular protection against portal hypertension. However, more studies are needed to determine the clinical significance of the early diagnosis and natural evolution of IPVD in this population

Anormal scintigraphy.

<p>Brain uptake = 15.7%.</p

Endoscopics and ultrasounds parameters.

<p>Endoscopics and ultrasounds parameters.</p

Clinical and laboratory parameters.

<p>RNI – Ratio Normalized International; AST – alanine aminotransferase; ALT – aspartate aminotrasnferase; γGT – γglutamil transferase.</p