Leucemia Linfoblástica Aguda (LLA) na população pediátrica: marcadores moleculares e implicações terapêuticas

A Leucemia Linfoblástica Aguda (LLA) é uma forma comum de câncer pediátrico, representando cerca de 80% dos casos de leucemia em crianças. A patologia é caracterizada pela proliferação descontrolada de células-tronco hematopoéticas na medula óssea, e avanços recentes na pesquisa genômica têm proporcionado uma compreensão mais profunda da complexidade molecular subjacente à doença. O presente estudo tem como objetivo oferecer uma visão abrangente dos principais marcadores moleculares e implicações terapêuticas associadas à LLA na população pediátrica. Este estudo, baseado em uma revisão sistemática da literatura científica, abrange o período de 2013 a 2023, utilizando as bases de dados PubMed (Medline), Cochrane Library e Scientific Electronic Library Online (SciELO). Marcadores moleculares preponderantes, como rearranjos cromossômicos específicos (t(12;21), t(1;19), t(9;22)), mutações genéticas distintivas (ETV6-RUNX1, E2A-PBX1, TP53) e amplificação do gene BCR-ABL1, têm sido objeto de estudo aprofundado. Esses marcadores desempenham um papel crucial na estratificação de risco e prognóstico, permitindo uma abordagem mais personalizada no tratamento da LLA em crianças. As implicações terapêuticas derivadas desses marcadores são vastas, destacando a promissora era das terapias direcionadas. Terapias específicas para mutações, como aquelas direcionadas à mutação BCR-ABL1, e inovações em imunoterapia estão moldando o cenário do tratamento da LLA, proporcionando resultados mais eficazes e menos tóxicos. Os resultados destacam a eficácia das terapias direcionadas e a necessidade contínua de pesquisa para otimizar a intervenção terapêutica, melhorar a qualidade de vida dos pacientes pediátricos afetados pela LLA e explorar novas facetas do tratamento. Em conclusão, este artigo fornece uma análise aprofundada dos marcadores moleculares e terapias associadas à LLA na população pediátrica, destacando avanços significativos e delineando áreas para investigação futura

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

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved