Trypanosoma cruzi vectors in a periurban area of the Western Brazilian Amazon

The etiological agent of American trypanosomiasis is the protozoan Trypanosoma cruzi, typically transmitted by triatomines. The aim of this study was to investigate the triatomine fauna and trypanosomiasis infections in Acre State, Western Brazilian Amazon. Insect collection was performed by dissecting palm trees and installing traps. We found that T. cruzi infection rate was 24.5% and Rhodnius pictipes (57.1%) was the most abundant triatomine species. Health education as well as epidemiological and entomological surveillance are necessary to diagnose and prevent new cases of Chagas disease in the region

Isolated familial somatotropinoma: 11q13-loh and gene/protein expression analysis suggests a possible involvement of aip also in non-pituitary tumorigenesis

OBJECTIVE: Non-pituitary tumors have been reported in a subset of patients harboring germline mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene. However, no detailed investigations of non-pituitary tumors of AIP-mutated patients have been reported so far. PATIENTS: We examined a MEN1- and p53-negative mother-daughter pair with acromegaly due to somatotropinoma. Subsequently, the mother developed a large virilizing adrenocortical carcinoma and a grade II B-cell non-Hodgkin's lymphoma. DESIGN: Mutational analysis was performed by automated sequencing. Loss-of-heterozygosity (LOH) analysis was carried out by sequencing and microsatellite analysis. AIP expression was assessed through quantitative PCR (qPCR) and immunohistochemistry. RESULTS: The functional inactivating mutation c.241C>T (R81X), which blocks the AIP protein from interacting with phosphodiesterase 4A (PDE4A), was identified in the heterozygous state in the leukocyte DNA of both patients. Analyzing the tumoral DNA revealed that the AIP wild-type allele was lost in the daughter's somatotropinoma and the mother's adrenocortical carcinoma. Both tumors displayed low AIP protein expression levels. Low AIP gene expression was confirmed by qPCR in the adrenocortical carcinoma. No evidence of LOH was observed in the DNA sample from the mother's B-cell lymphoma, and this tumor displayed normal AIP immunostaining. CONCLUSIONS: Our study presents the first molecular analysis of non-pituitary tumors in AIP-mutated patients. The finding of AIP inactivation in the adrenocortical tumor suggests that further investigation of the potential role of this recently identified tumor suppressor gene in non-pituitary tumors, mainly in those tumors in which the cAMP and the 11q13 locus are implicated, is likely to be worthwhile

Doença de Chagas na Amazônia Ocidental Brasileira: panorama epidemiológico no período de 2007 a 2018

Introduction: Chagas disease (CD) is a disease caused by the protozoan flagellates of the Kinetoplastid order Trypanosoma cruzi. Approximately 8,000,000 people are infected worldwide, mainly in Latin America, causing disabilities and more than 10,000 deaths per year. Objective: This study aimed to describe the epidemiological panorama of CD in the Western Brazilian Amazon from 2007 to 2018. Methods: In this ecological study, secondary data regarding the confirmed cases of T. cruzi infection in the states of Acre, Amazonas, Rondônia, and Roraima were collected from the Single Health System Notification Information System of the Department of Informatics of the Single Health System and were analyzed. The data were used to characterize the epidemiological profile of T. cruzi infection and to determine the frequency of infection in Western Amazonia. Results: A total of 184 cases of CD were reported in Western Amazonia, and the highest number of cases was reported in the states of Amazonas and Acre. Conclusion: The epidemiological panorama of the Western Brazilian Amazon from 2007 to 2018 includes a greater number of cases of T. cruzi infection in men aged 20–39 years and those living in rural areas. Oral transmission was prevalent in the region during the study, and the highest number of cases was reported in the months of April and December. Epidemiological data are an important resource for understanding the dynamics of CD and the main aspects related to the health-disease process.Introdução: A doença de Chagas (DC) é uma enfermidade causada pelo protozoário flagelado da ordem Kinetoplastida denominado Trypanosoma cruzi. Estima-se que oito milhões de pessoas estejam infectadas em todo o mundo, principalmente na América Latina, causando incapacidades e mais de dez mil mortes por ano. Objetivo: Descrever o panorama epidemiológico da doença de Chagas na Amazônia Ocidental brasileira no período de 2007 a 2018. Método: Trata-se de um estudo ecológico e com coleta e análise de dados referentes aos casos confirmados de infecção por T. cruzi nos estados do Acre, Amazonas, Rondônia e Roraima, por meio de fontes secundárias oriundos do Sistema de Informação de Agravos de Notificação do Sistema Único de Saúde (SINAN) do Departamento de Informática do Sistema Único de Saúde (DATASUS). Os dados foram utilizados para caracterizar o perfil epidemiológico dos infectados por T. cruzi e determinar a frequência da infecção na Amazônia Ocidental. Resultados: Houve a notificação de 184 casos de doença de Chagas na Amazônia Ocidental com mais registros nos estados do Amazonas e Acre. Conclusão: O panorama epidemiológico da Amazônia Ocidental Brasileira no período de 2007 a 2018, compreende uma maior quantidade de casos em indivíduos do sexo masculino, na faixa etária dos 20-39 anos, e provenientes de zona rural.  A forma de contágio prevalente na região durante o estudo foi a oral e a maior sazonalidade compreendeu os meses de abril e dezembro. Dados epidemiológicos são um importante recurso para a compreensão da dinâmica da DC e os principais aspectos relacionados no processo saúde-doença.

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