Comparative effects of fractional radiofrequency and microneedling on the genitalia of postmenopausal women: Histological and clinical changes

Objectives: The authors aimed to evaluate clinical and histological changes induced by Fractional Radiofrequency (FRF) and microneedling in vulvar tissue. Methods: Thirty postmenopausal women were randomly divided into G1 (FRF) and G2 (microneedling) groups. Sub-ablative FRF was executed using disposable fractionated electrodes with an intensity of 8 mJ. Microneedling was performed using a derma roller system. The authors evaluated before and after treatment using the Vaginal Laxity Questionnaire (VLQ), EuroQol Five-Dimensional (EQ-5D) questionnaire, and the Blatt and Kupperman Menopausal Index (BKMI). Additionally, the authors performed biopsies of the labia majora for histological analysis pre- and post-treatment. Data were expressed as mean (± standard deviation). A paired t-test was used for intra-group comparison (pre- and post-treatment), with an independent t-test used to compare intergroup data (both pre- and post-treatment). Results: In the G1 group, the VLQ values showed differences compared to the pre-treatment values with the data obtained 60 days after the beginning of the sessions (p = 0.01). Similarly, the data changes of the G2 group proved to be significant (p = 0.001) across the same time interval. In comparing the groups, VLQ values were not different (p > 0.05). Regarding histological analysis, FRF demonstrated improvement concerning the number of fibroblasts, blood vessels, and fatty degeneration (p < 0.05) compared to the control. Additionally, FRF and microneedling samples showed higher type III collagen and vimentin expression in the immunohistochemical analysis (p < 0.05). Conclusions: The therapies were found to be effective in treating the flaccidity of the female external genitalia. Additionally, histological changes were observed after interventions suggesting collagen remodeling

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