Doença Policística Hepática associada a Doença Policística Renal Autossômica Dominante: relato de caso - gravidade de doença hepática e revisão de literatura

A doença renal policística é uma doença de caráter genético hereditário, sendo um importante causa de doença renal crônica terminal com necessidade de Terapia de Substituição Renal (TSR). Além da clássica doença renal crônica a DRPAD pode apresentar várias manifestações extra-renais, entre elas, a doença cística hepática que acomete cerca de 90% dos pacientes. Infecções dos cistos hepáticos são complicações raras, porém graves. Neste artigo iremos apresentar um caso clínico com acometimento cístico hepático e renal maciço, além de uma breve revisão sobre complicações e possibilidades terapêuticas

Relato de caso: suspeita clínica e diagnóstico de síndrome nefrótica em paciente adulto em pronto-socorro

A Síndrome Nefrótica é caracterizada pela presença de proteinúria maciça (maior que 3.5g/1,73m² em 24 horas), hipoalbuminemia (<3g/dL) e edema periférico, podendo ser acompanhada também por hiperlipidemia e hipercoagulabilidade. Pode ser decorrente de condições primárias ou associada a doenças sistêmicas. Em pacientes adultos, cerca de 30% apresentam doenças sistêmicas concomitantes; o restante está relacionado a alterações primárias dos rins. A evolução dessa síndrome depende de qual glomerulopatia está envolvida, o que determina a possibilidade de resposta ao tratamento e o risco de evolução para doença renal crônica (DRC).

Non-compacted myocardium in an adult with acute neurological deficit in the Emergency Department: a case report

Non-Compacted Myocardium (NCM) is an uncommon cardiac condition with a genetic predisposition, often characterized by trabeculae and distinct myocardial layers. This case report discusses a 59-year-old hypertensive, diabetic male with acute neurological symptoms. Diagnosed with an ischemic stroke, subsequent investigations revealed features indicative of NCM. Confirmatory Cardiac Magnetic Resonance Imaging (CMR) and echocardiography were pivotal for diagnosis. The patient received specialized outpatient follow-up, emphasizing the importance of early diagnosis and tailored treatment. This report contributes to the understanding of NCM's diverse clinical presentations and underscores the significance of a multidisciplinary approach for effective patient care

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