Glutamine/Glutamate Metabolism Studied with Magnetic Resonance Spectroscopic Imaging for the Characterization of Adrenal Nodules and Masses

Purpose. To assess glutamine/glutamate (Glx) and lactate (Lac) metabolism using proton magnetic resonance spectroscopic imaging (1H-MRS) in order to differentiate between adrenal gland nodules and masses (adenomas, pheochromocytomas, carcinomas, and metastases). Materials and Methods. Institutional review board approval and informed consent were obtained. A total of 130 patients (47 men) with 132 adrenal nodules/masses were prospectively assessed (54 +/- 14.8 years). A multivoxel system was used with a two-dimensional point-resolved spectroscopy/chemical-shift imaging sequence. Spectroscopic data were interpreted by visual inspection and peak amplitudes of lipids (Lip), choline (Cho), creatine (Cr), Lac, and Glx. Lac/Cr and Glx/Cr were calculated. Glx/Cr was assessed in relation to lesion size. Results. Statistically significant differences were observed in Glx/Cr results between adenomas and pheochromocytomas (P < 0.05), however, with a low positive predictive value (PPV). Glx levels were directly proportional to lesion size in carcinomas. A cutoff point of 1.44 was established for the differentiation between carcinomas larger versus smaller than 4 cm, with 75% sensitivity, 100% specificity, 100% PPV, and 80% accuracy. Lac/Cr results showed no differences across lesions. A cutoff point of -6.5 for Lac/Cr was established for carcinoma diagnosis. Conclusion. Glx levels are directly proportional to lesion size in carcinomas. A cutoff point of -6.5 Lac/Cr differentiates carcinomas from noncarcinomas.Universidade Federal de São Paulo, Dept Diagnost Imaging, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Endocrinol, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Urol, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Diagnost Imaging, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Endocrinol, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Urol, BR-04024002 São Paulo, BrazilWeb of Scienc

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

Correction: Pervasive gaps in Amazonian ecological research

In the original version of the article, the authors incorrectly stated the value of current and projected deforestation in the results: the values should be 23.50% and 27.29%, respectively. This error does not impact the results or conclusions presented in the paper. The error has now been corrected online. The authors apologize for the error and any confusion that may have resulted

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