Genotype and phenotype landscape of MEN2 in 554 medullary thyroid cancer patients: the BrasMEN study

Multiple endocrine neoplasia type 2 (MEN2) is an autosomal dominant genetic disease caused by RET gene germline mutations that is characterized by medullary thyroid carcinoma (MTC) associated with other endocrine tumors. Several reports have demonstrated that the RET mutation profile may vary according to the geographical area. In this study, we collected clinical and molecular data from 554 patients with surgically confirmed MTC from 176 families with MEN2 in 18 different Brazilian centers to compare the type and prevalence of RET mutations with those from other countries. The most frequent mutations, classified by the number of families affected, occur in codon 634, exon 11 (76 families), followed by codon 918, exon 16 (34 families: 26 with M918T and 8 with M918V) and codon 804, exon 14 (22 families: 15 with V804M and 7 with V804L). When compared with other major published series from Europe, there are several similarities and some differences. While the mutations in codons C618, C620, C630, E768 and S891 present a similar prevalence, some mutations have a lower prevalence in Brazil, and others are found mainly in Brazil (G533C and M918V). These results reflect the singular proportion of European, Amerindian and African ancestries in the Brazilian mosaic genome

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