Hepatic Lipidosis Due to Obesity in a Free-Living Snake (Boa constrictor amarali)

Background: Liver performs several important functions to the maintenance of physiological mechanisms. Some liver diseases may directly affect anatomical and physiological aspects of this organ, and may lead to a permanent liver injury. In snakes, the most common causes of liver disease are infections, however, approaches on non-infectious liver diseases are scarce. Therefore, the objective of this study was to describe macroscopically and microscopically liver alterations in a Boa constrictor amarali snake.Case: A adult male boa (Boa constrictor amarali) snake of 110 cm of length and weight of 3.270 kg from free-living conditions, and without previous history was rescued in an urban area and taken by the Environmental Police to the Laboratory for Teaching and Research in Wild Animals (LAPAS) of the Federal University of Uberlândia’s (UFU) Veterinary Hospital, in Uberlândia MG, Brazil. The animal died and a significant amount of adipose tissue was found throughout the extension of the coelomic cavity at necropsy, limiting the visualization of its internal organs. Fragments of altered organs were collected and packed in a universal collector containing a 10% buffered formalin solution. These samples were sent to the Animal Pathology Laboratory (LPA) of the UFU. Macroscopically, the stomach presented a reddish mucosa, and mucous contents. The liver was pale, with a yellowish color and a friable consistency. Microscopically, dilated hepatic sinusoids filled with red blood cells were observed; the hepatocytes were enlarged, and its cytoplasm were filled with vacuoles ofvaried sizes that did not stain (severe diffuse lipidosis). It was also found occurrence of multifocal areas with loss of tissue architecture, and hepatocytes in karyolysis, charactering necrosis; and a discrete amount of multifocal mononuclear inflammatory infiltrate (multifocal hepatitis).Discussion: Obesity is connected to the occurrence of hepatic steatosis, since snakes are ectothermic animals that depend on environmental factors to maintain their metabolic rates. Obesity is a common problem in reptiles kept in captivity because they usually have constantly available food and little space to move. However, this was also observed in this study in a free-living animal found in an urbanized environment. Urbanization provides greater availability of food, and the animal does not need to go long distances to find a pray; this causes greater gain of body weight. Reptiles subjected to hot environments lose weight rapidly due to their relatively high metabolic rates. However, when subjected to low temperatures, they have a decrease in metabolism, compromising absorption, digestion, and liver metabolism, which causes fat accumulation. The animal under study is sedentary and it is a marked characteristic of this species; this strengthen the hypothesis that the animal moved little to feed because it was in an environment with high availability of prey. The animal presented accumulation of fat throughout the coelomic cavity, causing the rate of accumulation of triglycerides in the hepatocytes to exceed its metabolic degradation rate, resulting in steatosis. The early diagnosis of hepatic alterations favors the appropriatetreatment, allowing the prevention of irreversible damage to this organ, and avoid the animal’s death.Keywords: ectotherm, hepatocellular lipidosis, snakes, amaral’s boa, hepatic steatosis

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