Mansonella ozzardi in Amazonas, Brazil: Prevalence and distribution in the municipality of Coari, in the middle Solimões River

This study investigated some epidemiological aspects of the Mansonella ozzardi in municipality of Coari, Amazonas. Clinical symptoms were correlated with the filarial infection and the parasitic infection rates (PIR) were estimated in simuliid vectors. The general M. ozzardi human prevalence rate was 13.3% (231/1733), of which 10.2% (109/1069) were from the urban area and 18.4% (122/664) from the rural area. The prevalence rates were higher in men (14.5% urban and 19.7% rural) than in women (6.7% urban and 17.2% rural) and occurred in most age groups. The indices of microfilaremics were higher in people ≥ 51 years old (26.9% urban and 61.5% rural). High prevalence rates were observed in retired people (27.1% urban area), housewives and farmer (41.6% and 25%, respectively, in rural area). The main clinical symptoms were joint pains and sensation of leg coldness. Only Cerqueirellum argentiscutum (Simuliidae) transmits M. ozzardi in this municipality (PIR = 5.6% urban and 7.1% rural). M. ozzardi is a widely distributed parasitic disease in Coari. Thus, temporary residency in the region of people from other localities involved with the local gas exploitation might be a contributing factor in spreading the disease

Transcriptional profiles of the human pathogenic fungus paracoccidioides brasiliensis in mycelium and yeast cells

This work was supported by MCT, CNPq, CAPES, FUB, UFG, and FUNDECT-MS. PbGenome Network: Alda Maria T. Ferreira, Alessandra Dantas, Alessandra J. Baptista, Alexandre M. Bailão, Ana Lídia Bonato, André C. Amaral, Bruno S. Daher, Camila M. Silva, Christiane S. Costa, Clayton L. Borges, Cléber O. Soares, Cristina M. Junta, Daniel A. S. Anjos, Edans F. O. Sandes, Eduardo A. Donadi, Elza T. Sakamoto-Hojo, Flábio R. Araújo, Flávia C. Albuquerque, Gina C. Oliveira, João Ricardo M. Almeida, Juliana C. Oliveira, Kláudia G. Jorge, Larissa Fernandes, Lorena S. Derengowski, Luís Artur M. Bataus, Marcus A. M. Araújo, Marcus K. Inoue, Marlene T. De-Souza, Mauro F. Almeida, Nádia S. Parachin, Nadya S. Castro, Odair P. Martins, Patrícia L. N. Costa, Paula Sandrin-Garcia, Renata B. A. Soares, Stephano S. Mello, and Viviane C. B. ReisParacoccidioides brasiliensis is the causative agent of paracoccidioidomycosis, a disease that affects 10 million individuals in Latin America. This report depicts the results of the analysis of 6,022 assembled groups from mycelium and yeast phase expressed sequence tags, covering about 80% of the estimated genome of this dimorphic, thermo-regulated fungus. The data provide a comprehensive view of the fungal metabolism, including overexpressed transcripts, stage-specific genes, and also those that are up- or down-regulated as assessed by in silico electronic subtraction and cDNA microarrays. Also, a significant differential expression pattern in mycelium and yeast cells was detected, which was confirmed by Northern blot analysis, providing insights into differential metabolic adaptations. The overall transcriptome analysis provided information about sequences related to the cell cycle, stress response, drug resistance, and signal transduction pathways of the pathogen. Novel P. brasiliensis genes have been identified, probably corresponding to proteins that should be addressed as virulence factor candidates and potential new drug targets

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