Avaliação da atividade genotóxica do composto cloreto de cis-tetr aaminodiclororutênio (iii) sobre cultura de linfócitos do sangue periférico

Vários compostos metálicos, reconhecidos como potentes agentes antitumorais, têm sido desenvolvidos e testados in vivo e in vitro. Entre os primeiros complexos metálicos empregados no tratamento de tumores sólidos pode-se citar a cisplatina. Contudo, é frequente a perda de eficiência do tratamento com esses complexos, em decorrência de seus efeitos tóxicos e da resistência dos tumores, o que acaba levando à formação de tumores secundários. Os complexos de rutênio, um dos metais do grupo da platina, têm atraído muita atenção como possíveis agentes antitumorais. Estudos pré-clínicos com alguns compostos de rutênio [KP1019, RM175, MMI/ONCO4403, RAP, NAMI, NAMI-A, ditionato de cis-tetraaminoxalatorutênio (III), cloreto de cis-tetraaminodiclororutênio (III)] mostraram atividade promissora no tratamento de tumores, uma vez que sua ação sistêmica possibilitou o acesso a metástases e tumores líquidos. Entre os medicamentos testados, os compostos de rutênio mostraram-se promissores por sua propriedade antimetastática, o que representa importante marco no desenvolvimento de novas drogas antitumorais. Considerando o DNA como alvo, vários compostos de rutênio foram desenvolvidos e suas propriedades de ligação estão sendo testadas. As duas propriedades atribuídas aos complexos de rutênio – ativação por redução e transporte seletivo via sistema transferrina – podem explicar suas características antitumorais. Nos estágios iniciais de descobertas farmacêuticas, quando o mínimo é conhecido sobre as propriedades de uma nova molécula,simples triagens preditivas são apropriadas. Assim, no presente trabalho foram investigados os efeitos genotóxicos do cloreto de cis-tetraaminodiclororutênio (III), em 66 diferentes concentrações, em culturas de linfócitos humanos in vitro, utilizando-se índice mitótico (IM), aberrações cromossômicas (ACs) e ensaio cometa. As culturas de linfócitoshumanos foram tratadas com esse composto de rutênio nas concentrações de 1, 10, 100 e 1000 ?g.mL-1; cloridrato de doxorrubicina foi usado como controle positivo e meio completo como controle negativo. As concentrações de 1, 10, 100 e 1000 ?g.mL-1 mostraram IM de 5,9%, 4,6%, 3,9% e 0%, respectivamente. Foram encontradas aberrações cromossômicas consideradas espontâneas (1-2%) nas concentrações de 1, 10 e 100 ?g.mL-1, apresentando resultados estatisticamente diferentes quando comparados com o controle positivo. Para o ensaio cometa, as lâminas foram preparadas em duplicata e 100 núcleos foram analisados (50 núcleos para cada duplicata). Os cometas foram classificados em classes pelo software CometScore 15. O resultado deste estudo demonstrou que o cloreto de cis-tetraaminodiclororutênio (III) não possui potencial genotóxico in vitro

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

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data

Neotropical freshwater fisheries : A dataset of occurrence and abundance of freshwater fishes in the Neotropics

The Neotropical region hosts 4225 freshwater fish species, ranking first among the world's most diverse regions for freshwater fishes. Our NEOTROPICAL FRESHWATER FISHES data set is the first to produce a large-scale Neotropical freshwater fish inventory, covering the entire Neotropical region from Mexico and the Caribbean in the north to the southern limits in Argentina, Paraguay, Chile, and Uruguay. We compiled 185,787 distribution records, with unique georeferenced coordinates, for the 4225 species, represented by occurrence and abundance data. The number of species for the most numerous orders are as follows: Characiformes (1289), Siluriformes (1384), Cichliformes (354), Cyprinodontiformes (245), and Gymnotiformes (135). The most recorded species was the characid Astyanax fasciatus (4696 records). We registered 116,802 distribution records for native species, compared to 1802 distribution records for nonnative species. The main aim of the NEOTROPICAL FRESHWATER FISHES data set was to make these occurrence and abundance data accessible for international researchers to develop ecological and macroecological studies, from local to regional scales, with focal fish species, families, or orders. We anticipate that the NEOTROPICAL FRESHWATER FISHES data set will be valuable for studies on a wide range of ecological processes, such as trophic cascades, fishery pressure, the effects of habitat loss and fragmentation, and the impacts of species invasion and climate change. There are no copyright restrictions on the data, and please cite this data paper when using the data in publications