Frontal lobes white matter abnormalities mimicking cystic leukodystrophy in Wilson's disease

Univ Fed Sao Paulo, Dept Neurol & Neurocirurgia, Div Neurol Geral, Sao Paulo, SP, BrazilUniv Fed Sao Paulo, Dept Neurol & Neurocirurgia, Div Neurol Geral, Sao Paulo, SP, BrazilWeb of Scienc

Clinical and epidemiological profiles of non-traumatic myelopathies

Non-traumatic myelopathies represent a heterogeneous group of neurological conditions. Few studies report clinical and epidemiological profiles regarding the experience of referral services. Objective: To describe clinical characteristics of a non-traumatic myelopathy cohort. Method: Epidemiological, clinical, and radiological variables from 166 charts of patients assisted between 2001 and 2012 were compiled. Results: The most prevalent diagnosis was subacute combined degeneration (11.4%), followed by cervical spondylotic myelopathy (9.6%), demyelinating disease (9%), tropical spastic paraparesis (8.4%) and hereditary spastic paraparesis (8.4%). Up to 20% of the patients presented non-traumatic myelopathy of undetermined etiology, despite the broad clinical, neuroimaging and laboratorial investigations. Conclusion: Regardless an extensive evaluation, many patients with non-traumatic myelopathy of uncertain etiology. Compressive causes and nutritional deficiencies are important etiologies of non-traumatic myelopathies in our population.Univ Fed Sao Paulo, Dept Neurol & Neurocirurgia, Div Geral Neurol, Sao Paulo, SP, BrazilUniv Fed Sao Paulo, Dept Neurol & Neurocirurgia, Div Geral Neurol, Sao Paulo, SP, BrazilWeb of Scienc

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