USO DE AGREGADOS PLAQUETÁRIOS NA REGENERAÇÃO ÓSSEA GUIADA NA ODONTOLOGIA

Platelets are known for their role in hemostasis, where they help prevent blood loss at sites of vascular injury. To do this, they adhere, aggregate, and form a procoagulant surface leading to thrombin generation and fibrin formation. Platelets also release substances that promote tissue repair and influence the reactivity of vascular cells and other blood cells in angiogenesis and inflammation. They contain storage pools of growth factors as well as cytokines. Platelet-rich plasma (PRP) was first developed in the mid-1990s, with widespread use not only in dentistry, but also in many areas of medicine, including maxillofacial surgery, orthopedic surgery, and aesthetic medicine. In recent years, PRP has been extensively investigated in regenerative dentistry. It contains growth factors that influence wound healing, so it can contribute a lot to tissue repair. In surgery, PRP reduces bleeding while improving soft tissue healing and bone regeneration.As plaquetas são conhecidas por seu papel na hemostasia, onde ajudam a prevenir a perda de sangue em locais de lesão vascular. Para fazer isso, eles aderem, agregam e formam uma superfície pró-coagulante levando à geração de trombina e formação de fibrina. As plaquetas também liberam substâncias que promovem o reparo tecidual e influenciam a reatividade das células vasculares e outras células sanguíneas na angiogênese e na inflamação. Eles contêm pools de armazenamento de fatores de crescimento, bem como citocinas. . O plasma rico em plaquetas (PRP) foi inicialmente desenvolvido em meados da década de 1990, com uso generalizado não apenas na odontologia, mas também em muitas áreas da medicina, incluindo cirurgia maxilofacial, cirurgia ortopédica e medicina estétic. Nos últimos anos, o PRP tem sido extensivamente investigado nao dontologia regenerativa. Ele contém fatores de crescimento que influenciam a cicatrização de feridas, de modo que pode contribuir muito para o reparo tecidual. Na cirurgia, o PRP reduz o sangramento enquanto melhora a cicatrização dos tecidos moles e a regeneração óssea

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