Comparison of the clinical efficacy of at-home and in-office bleaching

Background: There is a lack of consensus regarding the superiority of the two vital bleaching methods. Aims: To compare the clinical efficacies of the two methods at home and in- office. Materials & Methods: Data was collected from PubMed, Embase, Cochrane, Lilacs, Scielo and BBO. Two independent researchers selected the articles, ie., only randomized clinical trials. Where there was no initial agreement, researchers reached a consensus. The search strategy initially yielded 483 titles. After the exclusion by titles, 408 articles remained and following the abstract-based evaluation, only 5 were subjected to further analysis. Results: The most of the authors did not find any statistically significant differences between at home and in-office bleaching procedures. Conclusion: Both the at home and in-office methods alone or in association are equally efficient when a 14 day protocol is used

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

Comparison of two methods of visual magnification for removal of adhesive flash during bracket placement using two types of orthodontic bonding agents

ABSTRACT Objective: This study aimed to evaluate the effectiveness of two methods of visual magnification (operating microscope and light head magnifying glass) for removal of composite flash around orthodontic metal brackets. Material and Methods: Brackets were bonded in the center of the clinical crown of sixty well-preserved human premolars. Half of the sample was bonded with conventional Transbond XT (3M Unitek TM, USA), whereas the other half was bonded with Transbond TM Plus Color Change (3M Unitek TM, USA). For each type of composite, the choice of method to remove the flash was determined by randomly distributing the teeth into the following subgroups: A (removal by naked eye, n = 10), B (removal with the aid of light head magnifying glass, under 4x magnification, n = 10), and C (removal with the aid of an operating microscope, under 40x magnification, n = 10). Brackets were debonded and teeth taken to a scanning electron microscope (SS-x-550, Shimadzu, Japan) for visualization of their buccal surface. Quantification of composite flash was performed with Image Pro Plus software, and values were compared by Kruskal-Wallis test and Dunn’s post-hoc test at 5% significance level. Results: Removal of pigmented orthodontic adhesive with the aid of light head magnifying glass proved, in general, to be advantageous in comparison to all other methods. Conclusion: There was no advantage in using Transbond TM Plus Color Change alone. Further studies are necessary to draw a more definitive conclusion in regards to the benefits of using an operating microscope