Acompanhamento Farmacoterpêutico e a Detecção de Reações Adversas a Inibidores de Tirosinoquinase utilizados no Tratamento da Leucemia Mielóide Crônica / Pharmacotherapeutic Follow-up and Detection of Adverse Reactions to Tyrosinokinase Inhibitors used in the Treatment of Chronic Myeloid Leukemia

INTRODUÇÃO: Leucemia Mielóide Crônica (LMC) é caracterizada pela alta atividade da enzima tirosinoquinase. O tratamento é realizado com os inibidores de tirosinoquinase (ITQs) Imatinibe, Dasatinibe e Nilotinibe. Reações adversas acarretam não adesão ao tratamento e interferem na qualidade de vida dos pacientes. Identificar e minimizar reações adversas otimiza a farmacoterapia melhorando respostas clínicas. OBJETIVO: Identificar o papel do acompanhamento farmacoterapêutico na detecção de reações adversa em pacientes utilizando ITQs. MÉTODO: Foi um estudo prospectivo longitudinal realizado entre maio e dezembro de 2018. 23 pacientes aceitaram participar e receberam Acompanhamento Farmacoterapêutico. Para a avaliação da efetividade desse acompanhamento na detecção de reações, foi feita a comparação das reações relatas durante as consultas farmacêuticas e as registradas em prontuário pelos médicos. RESULTADOS: Foram realizados 67 atendimentos, onde 19 (82,6%) pacientes afirmaram a ocorrência de reações adversas que eles acreditavam estarem relacionadas aos ITQs. Nos prontuários foram encontrados relatos de 24 reações adversas, enquanto durante as consultas, os mesmos pacientes relataram 115 reações. CONCLUSÃO: Foi demonstrado que o formulário utilizado durante as consultas foi eficiente para detecção das reações adversas, havendo aumento estatisticamente significativo no número e na variedade de reações relatadas ao farmacêutico quando comparadas as registradas pelo médico assistente no prontuário. O estudo demonstrou que o acompanhamento traz benefícios para detecção de reações adversas e contribui, junto a equipe, para o manejo adequado

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