Salivary Changes, Systemic Conditions, and Medication Use in Independently-Living Aged: A Cross-Sectional Study

Objective: To determine the unstimulated salivary flow, pH, and buffering capacity and their associations with systemic conditions and medication use in independently living aged. Material and Methods: This cross-sectional study included 72 participants with a minimum of 60 years recruited in Belo Horizonte, Brazil. A questionnaire was used to collect age, sex, presence of systemic diseases, and medications in continuous use. Salivary data collection was performed to determine unstimulated salivary flow, pH, and buffering capacity. Descriptive, bivariate, and multivariate analyses were performed (p<0.05). Results: Most of the sample had at least one systemic disease (81.9%) and used at least one medication (79.2%). Female participants (p=0.01), those with five or more systemic diseases (p<0.01), and hypertension (p=0.04) had reduced salivary flow. Participants with systemic diseases (p=0.02), taking any medication (p=0.04), in a polypharmacy regimen, and presenting hypertension (p=0.02) had more acidic salivary pH. Participants with diabetes had average salivary buffering capacity (p=0.02). In the adjusted multiple regression models, no explanatory variable was significantly associated with the salivary outcomes. Conclusion: Systemic alterations and medication use appear to be related to salivary changes in older adults. Integrative assessment of older adults is fundamental to identifying and controlling the factors that may modify their salivary characteristics

HIPERPLASIA PROSTÁTICA BENIGNA (HPB): TRATAMENTO E CONSIDERAÇÕES ANESTÉSICAS

Benign prostatic hyperplasia (BPH) is a common condition affecting elderly men, characterized by benign enlargement of the prostate that can lead to significant urinary symptoms. Therefore, management of this condition involves a variety of therapeutic options, from medications to surgical procedures. Objective: To explore the various therapeutic approaches to BPH and analyze the specific anesthetic considerations associated with these treatments.  Methodology: The Cochrane, Scielo and Medline databases were used, searching for articles published between 2022 and 2024, in Portuguese or English. Final Considerations: A multidisciplinary approach is essential for the effective management of BPH. In this way, a detailed understanding of treatment options, from drug therapies to surgical interventions, allows the personalization of care according to the individual needs of patients. Therefore, specific anesthetic considerations for each procedure are crucial to minimize risks and maximize patient safety and comfort. Furthermore, the continuous assessment of clinical outcomes and the identification of associated risk factors contribute to the continuous improvement of clinical practices.A hiperplasia prostática benigna (HPB) é uma condição comum que afeta homens idosos, caracterizada pelo aumento benigno da próstata que pode levar a sintomas urinários significativos. Dessa forma, o manejo dessa condição envolve uma variedade de opções terapêuticas, desde medicamentos até procedimentos cirúrgicos. Objetivo: Explorar as diversas abordagens terapêuticas para a HPB e analisar as considerações anestésicas específicas associadas a esses tratamentos.  Metodologia:  Foram utilizadas as bases de dados Cochrane, Scielo e Medline, buscando artigos publicados entre os anos de 2022 e 2024, nos idiomas Português ou Inglês. Considerações Finais: A abordagem multidisciplinar é essencial para o manejo eficaz da HPB. Dessa forma, o entendimento detalhado das opções de tratamento, desde terapias medicamentosas até intervenções cirúrgicas, permite a personalização do cuidado de acordo com as necessidades individuais dos pacientes. Assim, as considerações anestésicas específicas para cada procedimento são cruciais para minimizar riscos e maximizar a segurança e o conforto dos pacientes. Além disso, a avaliação contínua dos desfechos clínicos e a identificação de fatores de risco associados contribuem para a melhoria contínua das práticas clínicas

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

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora