Prevalence Of Maxillary Sinus Jaw Mucuous Cysts In University Dental Radiology Service

Background: Mucosal cyst of the maxillary sinus or antral pseudocyst is one of great importance injury, being the pathology that affects more the maxillary sinus. Their discovery, in most cases, it is for the interpretation of the images in routine panoramic radiography. Aims: The research aimed to evaluate the prevalence of mucous cyst in maxillary sinus in radiology clinic at Ceara Federal University. Material and Methods: To this study conduction, were analyzed 1996 panoramic radiographs from a digital file obtained between April 2011 to April 2013 Results:. Aspects as gender, affected side and teeth absence next to the cyst in the respective quadrant were evaluated.It was observed in the sample the occurrence of 45 patients with suggested images of mucous cysts in maxillary sinus,making a prevalence of 2,25%. From them, 26 (57,8%)were female and 19 (42,2%) were male. 48 maxillary sinuswere affected with the wound, from which28 (58,3%) it was in the left side and 20 (41,7%) in the right site. Three patients presented the wound in both sides, what represents 6,7% of the affected patients. From those 48 Mucous retention cyst, 40 (83,3%) were not related to an edentulous area in ipsilateral quadrant and 8 (16,7%) were shown next to an edentulous area. Conclusion: The conclusion was that the cyst of retention mucous in the maxillary sinus had prevalence in males and in the left side of the maxillary sinus. It was not found a relation between the cyst and the edentulous area

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