Addressing Cervical Cancer Disparities in Texas: Expansion of a Community-Based Prevention initiative For Medically Underserved Populations

Although cervical cancer is preventable, significant disparities exist in access to screening and prevention services. In medically underserved areas (MUAs) of Texas, these rates are 55% higher compared to the remainder of the US. In 2019, we expanded a multicomponent, comprehensive program to improve cervical cancer prevention in partnership with 13 clinics and mobile vans in MUAs of Texas. Our multicomponent intervention program consists of community education and patient navigation coupled with a training/mentoring program for local medical providers to perform diagnostic procedures and treatment for patients with abnormal screening results. Hands-on training courses to learn these skills are coupled with biweekly telementoring conferences using Project ECHO® (Extension for Community Healthcare Outcomes). This program was implemented in 2015 and expanded to other MUAs in Texas in 2019. From March 2019 to August 2022, 75,842 individuals were educated about cervical cancer screening and HPV vaccination. A total of 44,781 women underwent screening for cervical cancer, and 2,216 underwent colposcopy and 264 underwent LEEP. High-grade cervical dysplasia was diagnosed in 658 individuals and invasive cervical cancer in 33 individuals. We trained 22 providers to perform colposcopy and/or LEEP. In addition, 78 Project ECHO telementoring sessions were held with an average of 42 attendees per session, with 72 individual patient cases discussed. Our comprehensive community-based prevention initiative for medically underserved populations has led to a significant number of individuals undergoing cervical cancer screening in MUAs, as well as improved access to colposcopy and LEEP services

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

Vulvar Paget Disease: a series of cases in southern Brazil

# Background Vulvar Paget's disease is rare and manifests clinically as erythematous itchy skin lesion with areas of hyperkeratosis. The current report describes the diagnosis, management and outcomes data from a case series of women diagnosed with vulvar Paget's disease in a tertiary hospital in southern Brazil. # Methods A retrospective review of medical records of women with vulvar Paget's disease at a single institution in the period 2000-2020 was carried out. Fisher's exact test was used to compare recurrence in relation to the status of surgical margins after primary treatment and in relation to the surgical modality. Quantitative variables were described using mean and categorical variables using absolute and relative frequencies. # Results Ten patients were identified with the diagnosis of vulvar Paget's disease and two of them were excluded due to lack of information in medical records, therefore eight patients are described. The majority of the patients self-identified as white (87,5%, 7/8) and the median age at diagnosis was 65 years (range 45-81). The most common clinical symptoms were vulvar pruritus (62.5%, 5/8) and burning (37.5%, 3/8). It was not possible to identify the type of initial surgery in three patients, as they started follow-up at the institution after undergoing primary treatment at other institutions. The remaining five patients underwent surgery as their primary treatment -- simple vulvectomy (60%, 3/5) and radical vulvectomy (40%, 2/5). In total, 75% (6/8) of patients had disease recurrence. Radiotherapy and imiquimod were used at the time of recurrence in three patients (50%, 3/6), but surgery remained the most common treatment for recurrence (83%, 5/6). The margin status of surgical specimens from patients starting treatment at the institution was negative in four (80%, 4/5) and positive in one woman (20%, 1/5). There was no significant difference in recurrence rates in patients with negative or positive margins, nor in relation to the surgical modality of the primary treatment. There were two deaths (25%, 2/8), one of them due to complications from Paget's disease and the other one due to metastatic urothelial adenocarcinoma. # Conclusions Vulvar Paget's disease has a significant morbidity and limited data are available, especially in Brazil. Due to the rarity of the disease, no randomized clinical trials are available in the literature and therefore it is difficult to compare the results of surgical treatment and other therapeutic modalities. There is an opportunity to explore best options for adequate Paget's disease treatment