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

Tumor staging, HPV genotypes and WIF1 methylation: associations with prognosis and survival

Submitted by JÚLIO HEBER SILVA ([email protected]) on 2016-11-21T15:56:00Z No. of bitstreams: 2 Dissertação - Keila Patrícia Almeida de Carvalho - 2016.pdf: 1984915 bytes, checksum: 0d6b814fa8ededd81b851b12fb3ff2f8 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Cláudia Bueno ([email protected]) on 2016-11-21T20:25:40Z (GMT) No. of bitstreams: 2 Dissertação - Keila Patrícia Almeida de Carvalho - 2016.pdf: 1984915 bytes, checksum: 0d6b814fa8ededd81b851b12fb3ff2f8 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2016-11-21T20:25:40Z (GMT). No. of bitstreams: 2 Dissertação - Keila Patrícia Almeida de Carvalho - 2016.pdf: 1984915 bytes, checksum: 0d6b814fa8ededd81b851b12fb3ff2f8 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-10-14Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqIntroduction. Squamous cell carcinoma is the most common type of cervical cancer, followed by endocervical adenocarcinoma. Tumor staging is important to evaluate prognosis. Cervical infection by one of the oncogenic types of HPV, especially HPV 16 and 18 is a pre-requisite for developing invasive cancer. Epigenetics alterations, as DNA methylation, are well-known carcinogenic mechanisms. WIF1 is a tumor suppressor gene which silencing by methylation helps in neoplastic progression. Objective. The goal of this study was to evaluate staging, HPV genotypes and WIF1 methylation in cervical cancer and to test association between these variables with age, prognosis and survival. Methods. It was included 95 cases obtained in Araujo Jorge Hospital (Goiânia/ GO): 73 of invasive squamous cell carcinoma and 22 of invasive endocervical adenocarcinoma. DNA was extracted from paraffin-embedded biopsies using phenol-chloroform. HPV detection and genotyping were conducted using the kit INNO- LiPA HPV GENOTYPING EXTRA® (Innogenetics™). Methylation of WIF1 gene was evaluated by methylation-specific PCR (MSP). Odds Ratio was used to calculate statistical association. The calculation of survival used the Kaplan-Meier method and the log-hank test was used to compare means of survival and prognostic factors. A value of p<0.05 was considered statically significant. Results. There was significant association between tumor stages III and IV and worse prognosis (OR = 7.32). The 5-year overall survival was 79.1% and it was significant higher in cases with tumor stages I and II (p = 0.001). Women between 50 and 60 years had more chances having tumors in stages III and IV (OR = 0.15). Although, considering mean and median ages of women included, those over 51 years were more likely having tumor stages III and IV (OR = 5.92). No association was found between worse prognosis or lower survival and histological type, HPV infection and WIF1 methylation. Conclusion. Worse prognosis and lower survival was determined by tumor stages III and IV.Introdução. O tipo histológico mais comum do câncer do colo do útero é o carcinoma de células escamosas, seguido pelos adenocarcinomas endocervicais. O estadiamento tumoral é importante para avaliar o prognóstico. A infecção da cérvice por um dos genótipos oncogênicos de HPV, especialmente os HPV 16 e 18, é pré-requisito para o desenvolvimento do câncer invasor. Alterações epigenéticas, como a metilação do DNA, são mecanismos conhecidos de carcinogênese. O gene WIF1 é supressor tumoral e seu silenciamento por metilação auxilia na progressão neoplásica. Objetivo. O objetivo desse estudo foi avaliar o estadiamento, genótipos de HPV e a metilação do gene WIF1 e testar a associação entre estas variáveis e a idade, o prognóstico e a sobrevida de mulheres com câncer do colo do útero. Métodos. Foram incluídos 95 casos obtidos no Hospital Araújo Jorge (Goiânia/GO) sendo 73 de carcinomas escamosos invasores e 22 de adenocarcinomas endocervicais invasores. O DNA foi extraído com fenol-clorofórmio dos materiais de biópsia incluídos em parafina e a detecção e genotipagem de HPV foram realizadas utilizando-se o kit INNO- LiPA HPV GENOTYPING EXTRA® (Innogenetics™). A análise de metilação do gene WIF1 foi realizada através da técnica PCR específica para metilação (MSP). As associações estatísticas foram feitas com cálculo de Odds Ratio e a sobrevida foi calculada pelo método de Kaplan-Meier, sendo a comparação das médias de sobrevida e os fatores prognósticos analisada pelo teste log-rank. Um valor de p<0,05 foi considerado estatisticamente significativo. Resultados. Houve associação estatisticamente significante entre neoplasias diagnosticadas nos estágios III e IV e pior prognóstico (OR = 7,32). A sobrevida global em cinco anos foi de 79,1% e foi significativamente maior nos casos com estágios I e II (p = 0,001). Mulheres na faixa etária entre 50 e 60 anos idade mostraram-se com maior chance de serem portadoras de câncer do colo do útero em estágios I e II (OR = 0,15). Contudo, considerando a média e mediana da idade das mulheres incluídas, aquelas com idade acima dos 51 anos tiveram mais chance de serem diagnosticadas com câncer em estágios III e IV (OR = 5,92). Não houve associação significante entre tipo histológico, infecção por HPV ou metilação do gene WIF1 e pior prognóstico ou menor sobrevida. Conclusão. Pior prognóstico e menor sobrevida das mulheres foram determinados pelo estadiamento tumoral em III e IV

Human papillomavirus genotypes 68 and 58 are the most prevalent genotypes in women from quilombo communities in the state of Maranhão, Brazil

Objectives: To determine the frequency of human papillomavirus (HPV) types and behavioral characteristics related to cytological abnormalities in women descendants of slaves, who live in isolated communities known as quilombos in the state of Maranhão, Brazil. Methods: Cervicovaginal specimens of 353 women were analyzed by conventional cytology and genotyping. HPV detection and genotyping was performed using a linear array HPV genotyping test kit. Behavioral factors and their association with cytological abnormalities were analyzed, as well as the association between cytological abnormalities and HPV infection. Results: The frequency of HPV infection was 13%, and infection with high-risk HPV types was more frequent than with low-risk types (10.2% vs. 2.8%). The most prevalent genotypes were HPV 68 (3.1%) and HPV 58 (2.6%). HPV-positive women were 6.5 times more likely than HPV-negative women to be diagnosed with cytological abnormalities. There was a significant association between HPV infection and the presence of cytological abnormalities in women 31–40 years of age and in women 51–60 years of age. Conclusions: A distinct profile of high-risk HPV genotypes was detected, with predominance of types 68 and 58. It is possible that the results of the present study are due to specific characteristics of the population, which is geographically isolated and maintains conservative sexual habits

Human papillomavirus (HPV) genotype distribution in penile carcinoma: Association with clinic pathological factors

<div><p>Background</p><p>Penile carcinoma (PC) is a rare, highly mutilating disease, common in developing countries. The evolution of penile cancer includes at least two independent carcinogenic pathways, related or unrelated to HPV infection.</p><p>Objectives</p><p>To estimate the prevalence, identify HPV genotypes, and correlate with clinicopathological data on penile cancer.</p><p>Methods</p><p>A retrospective cohort study involving 183 patients with PC undergoing treatment in a referral hospital in Goiânia, Goiás, in Midwestern Brazil, from 2003 to 2015. Samples containing paraffin embedded tumor fragments were subjected to detection and genotyping by INNO-LiPA HPV. The clinicopathological variables were subjected to analysis with respect to HPV positivity and used prevalence ratio (PR), adjusted prevalence ratio (PRa) and 95% confidence interval (CI) as statistical measures.</p><p>Results</p><p>The prevalence of HPV DNA in PC was 30.6% (95% CI: 24.4 to 37.6), high-risk HPV 24.9% (95% CI: 18.9 to 31.3), and 62.5% were HPV 16. There was a statistical association between the endpoints HPV infection and HPV high risk, and the variable tumor grade II-III (p = 0.025) (p = 0.040), respectively. There was no statistical difference in disease specific survival at 10 years between the HPV positive and negative patients (p = 0.143), and high and low risk HPV (p = 0.325).</p><p>Conclusions</p><p>The prevalence of HPV infection was 30.6%, and 80.3% of the genotypes were identified as preventable by anti-HPV quadrivalent or nonavalent vaccine. HPV infections and high-risk HPV were not associated with penile carcinoma prognosis in this study.</p></div

Prevalence of HPV-DNA in 183 cases of penile carcinoma in a referral hospital in Goias, Brazil.

<p>Prevalence of HPV-DNA in 183 cases of penile carcinoma in a referral hospital in Goias, Brazil.</p

Bivariate and multivariate analysis of factors associated with infection by high-risk HPV.

<p>Bivariate and multivariate analysis of factors associated with infection by high-risk HPV.</p