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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Identification of priority groups for COVID-19 vaccination in Brazil

In a context of community transmission and shortage of vaccines, COVID-19 vaccination should focus on directly reducing the morbidity and mortality caused by the disease. It was thus essential to define priority groups for vaccination by the Brazilian National Immunization Program (PNI in Portuguese), based on the risk of hospitalization and death from the disease. We calculated overrisk according to sex, age group, and comorbidities using hospitalization and death records from severe acute respiratory illness with confirmation of COVID-19 (SARI-COVID) in all of Brazil in the first 6 months of the epidemic. Higher overrisk was associated with male sex (hospitalization = 1.1 and death = 1.2), age over 45 years for hospitalization (OvRag ranging from 1.1 to 8.5), and age over 55 year for death (OvRag ranging from 1.5 to 18.3). In the groups with comorbidities, chronic kidney disease, diabetes mellitus, cardiovascular disease, and chronic lung disease were associated with overrisk, while there was no such evidence for asthma. Chronic kidney disease or diabetes and age over 60 showed an even stronger association, reaching overrisk of death 14 and 10 times greater than in the general population, respectively. For all the comorbidities, there was higher overrisk at older ages, with a downward gradient in the oldest age groups. This pattern was reversed when examining overrisk in the general population, for both hospitalization and death. The current study provided evidence of overrisk of hospitalization and death from SARI-COVID, assisting the definition of priority groups for COVID-19 vaccination.Em um contexto de transmissão comunitária e escassez de vacinas, a vacinação contra a COVID-19 deve focar na redução direta da morbidade e da mortalidade causadas pela doença. Portanto, é fundamental a definição de grupos prioritários para a vacinação pelo Programa Nacional de Imunizações (PNI), baseada no risco de hospitalização e óbito pela doença. Para tal, calculamos o sobrerrisco por sexo, faixa etária e comorbidades por meio dos registros de hospitalização e óbito por síndrome respiratória aguda grave com confirmação de COVID-19 (SRAG-COVID) em todo o Brasil nos primeiros seis meses de epidemia. Apresentaram maior sobrerrisco pessoas do sexo masculino (hospitalização = 1,1 e óbito = 1,2), pessoas acima de 45 anos para hospitalização (SRfe variando de 1,1 a 8,5) e pessoas acima de 55 anos para óbitos (SRfe variando de 1,5 a 18,3). Nos grupos de comorbidades, doença renal crônica, diabetes mellitus, doença cardiovascular e pneumopatia crônica conferiram sobrerrisco, enquanto para asma não houve evidência. Ter doença renal crônica ou diabetes mellitus e 60 anos ou mais mostrou-se um fator ainda mais forte, alcançando sobrerrisco de óbito 14 e 10 vezes maior do que na população geral, respectivamente. Para todas as comorbidades, houve um sobrerrisco mais alto em idades maiores, com um gradiente de diminuição em faixas mais altas. Esse padrão se inverteu quando consideramos o sobrerrisco em relação à população geral, tanto para hospitalização quanto para óbito. O presente estudo forneceu evidências a respeito do sobrerrisco de hospitalização e óbito por SRAG-COVID, auxiliando na definição de grupos prioritários para a vacinação contra a COVID-19.En un contexto de transmisión comunitaria y escasez de vacunas, la vacunación contra la COVID-19 debe enfocarse en la reducción directa de la morbilidad y de la mortalidad causadas por la enfermedad. Por lo tanto, es fundamental la definición de grupos prioritarios para la vacunación por el Programa Nacional de Inmunizaciones (PNI), basada en el riesgo de hospitalización y óbito por la enfermedad. Para tal fin, calculamos el sobrerriesgo por sexo, franja de edad y comorbilidades mediante los registros de hospitalización y óbito por síndrome respiratorio agudo grave con confirmación de COVID-19 (SRAG-COVID) en todo Brasil, durante los primeros seis meses de epidemia. Presentaron mayor sobrerriesgo personas del sexo masculino (hospitalización = 1,1 y óbito = 1,2), personas por encima de 45 años para hospitalización (SRfe variando de 1,1 a 8,5) y personas por encima de 55 años para óbitos (SRfe variando de 1,5 a 18,3). En los grupos de comorbilidades, enfermedad renal crónica, diabetes mellitus, enfermedad cardiovascular y neumopatía crónica ofrecieron sobrerriesgo, mientras que para el asma no hubo evidencia. Sufrir una enfermedad renal crónica o diabetes mellitus y tener 60 años o más mostró un factor todavía más fuerte, alcanzando sobrerriesgo de enfermedad 14 y 10 veces mayor que en la población general, respectivamente. Para todas las comorbilidades, hubo un sobrerriesgo más alto en edades mayores, con un gradiente de disminución en franjas más altas. Este patrón se invirtió cuando consideramos el sobrerriesgo en relación con la población general, tanto para hospitalización como para óbito. El presente estudio proporcionó evidencias respecto al sobrerriesgo de hospitalización y óbito por SRAG-COVID, ayudando en la definición de grupos prioritarios para la vacunación contra la COVID-19