Iron status and Helicobacter pylori infection in symptomatic children: an international multi-centered study

Objective:Iron deficiency (ID) and iron deficiency anaemia (IDA) are global major public health problems, particularly in developing countries. Whilst an association between H. pylori infection and ID/IDA has been proposed in the literature, currently there is no consensus. We studied the effects of H. pylori infection on ID/IDA in a cohort of children undergoing upper gastrointestinal endoscopy for upper abdominal pain in two developing and one developed country.Methods:In total 311 children (mean age 10.7±3.2 years) from Latin America - Belo Horizonte/Brazil (n = 125), Santiago/Chile (n = 105) - and London/UK (n = 81), were studied. Gastric and duodenal biopsies were obtained for evaluation of histology and H. pylori status and blood samples for parameters of ID/IDA.Results:The prevalence of H. pylori infection was 27.7% being significantly higher (p<0.001) in Latin America (35%) than in UK (7%). Multiple linear regression models revealed H. pylori infection as a significant predictor of low ferritin and haemoglobin concentrations in children from Latin-America. A negative correlation was observed between MCV (r = -0.26; p = 0.01) and MCH (r = -0.27; p = 0.01) values and the degree of antral chronic inflammation, and between MCH and the degree of corpus chronic (r = -0.29, p = 0.008) and active (r = -0.27, p = 0.002) inflammation.Conclusions:This study demonstrates that H. pylori infection in children influences the serum ferritin and haemoglobin concentrations, markers of early depletion of iron stores and anaemia respectively

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