EDITORIAL

Editorial from volume 02, number 02 year 2013.Editorial do volume 02, número 03, ano 2013

EDITORIAL

Editorial from volume 01, number 02, year 2012.Editorial do volume 01, número 02, año 2012.Editorial do volume 01, número 02, ano 2012

Latin American consensus on the treatment of head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is well known as a serious health problem worldwide, especially in low-income countries or those with limited resources, such as most countries in Latin America. International guidelines cannot always be applied to a population from a large region with specific conditions. This study established a Latin American guideline for care of patients with head and neck cancer and presented evidence of HNSCC management considering availability and oncologic benefit. A panel composed of 41 head and neck cancer experts systematically worked according to a modified Delphi process on (1) document compilation of evidence-based answers to different questions contextualized by resource availability and oncologic benefit regarding Latin America (region of limited resources and/or without access to all necessary health care system infrastructure), (2) revision of the answers and the classification of levels of evidence and degrees of recommendations of all recommendations, (3) validation of the consensus through two rounds of online surveys, and (4) manuscript composition. The consensus consists of 12 sections: Head and neck cancer staging, Histopathologic evaluation of head and neck cancer, Head and neck surgery-oral cavity, Clinical oncology-oral cavity, Head and neck surgery-oropharynx, Clinical oncology-oropharynx, Head and neck surgery-larynx, Head and neck surgery-larynx/hypopharynx, Clinical oncology-larynx/hypopharynx, Clinical oncology-recurrent and metastatic head and neck cancer, Head and neck surgery-reconstruction and rehabilitation, and Radiation therapy. The present consensus established 48 recommendations on HNSCC patient care considering the availability of resources and focusing on oncologic benefit. These recommendations could also be used to formulate strategies in other regions like Latin America countries

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