Aspectos indicativos de envelhecimento facial precoce em respiradores orais adultos

BACKGROUND: early facial aging in mouth breathing adults. AIM: to verify the presence of indicative factors of early facial aging and to characterize the measurements of the projection of the nasogeniane fold to the tragus and of the face width in mouth and nose breathing adults. METHOD: aspects of early facial aging were observed in 60 individuals (presence of dark circles and wrinkles under the eyes, mentual wrinkles and mentual ridges). Measurements of the projection of the nasogeniane fold to the tragus and of the face width (distance between the buccinators) were taken using a digital caliper. Later, the volunteers were submitted to speech-language evaluations (anamneses and orofacial myofuntional assessment) and to an otolaryngology inspection in order to establish the diagnosis of mouth breathing (anamneses, clinical evaluation and video laryngoscopy). The obtained data were analyzed according to descriptive statistics and to the following statistic tests: Kolmogorov-Smirnov, Shapiro-Wilk, Qui-square, Mann-Withney and the T-Student test for independent variables. Differences were considered significant when the p value was inferior to .05 and the accepted beta error was of .1. RESULTS: the research sample consisted only of female volunteers. For the research group (mouth breathers) the age average was of 22.04 ± 2.25 years and, for the control group (nose breathers) the age average was of 21.94 ± 2.03 years. The presence of a high percentage of indicative factors of early facial aging was observed for the group of mouth breathers when compared to the group of nose breathers. Greater differences between the projections of the nasogenianos ridges in right and left side of the face was also observed for the group of mouth breathers. However, higher values of face widths were observed for the nose breathing individuals, configuring a discreetly more widened face in the cheek region. CONCLUSIONS: in the present study there was a higher indication of early facial aging for the group of mouth breathers

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