Analysis of the immunological biomarker profile during acute zika virus infection reveals the overexpression of CXCL10, a chemokine linked to neuronal damage

BACKGROUND Infection with Zika virus (ZIKV) manifests in a broad spectrum of disease ranging from mild illness to severe neurological complications and little is known about Zika immunopathogenesis. OBJECTIVES To define the immunologic biomarkers that correlate with acute ZIKV infection. METHODS We characterized the levels of circulating cytokines, chemokines, and growth factors in 54 infected patients of both genders at five different time points after symptom onset using microbeads multiplex immunoassay; comparison to 100 age-matched controls was performed for statistical analysis and data mining. FINDINGS ZIKV-infected patients present a striking systemic inflammatory response with high levels of pro-inflammatory mediators. Despite the strong inflammatory pattern, IL-1Ra and IL-4 are also induced during the acute infection. Interestingly, the inflammatory cytokines IL-1β, IL-13, IL-17, TNF-α, and IFN-γ; chemokines CXCL8, CCL2, CCL5; and the growth factor G-CSF, displayed a bimodal distribution accompanying viremia. While this is the first manuscript to document bimodal distributions of viremia in ZIKV infection, this has been documented in other viral infections, with a primary viremia peak during mild systemic disease and a secondary peak associated with distribution of the virus to organs and tissues. MAIN CONCLUSIONS Biomarker network analysis demonstrated distinct dynamics in concurrence with the bimodal viremia profiles at different time points during ZIKV infection. Such a robust cytokine and chemokine response has been associated with blood-brain barrier permeability and neuroinvasiveness in other flaviviral infections. High-dimensional data analysis further identified CXCL10, a chemokine involved in foetal neuron apoptosis and Guillain-Barré syndrome, as the most promising biomarker of acute ZIKV infection for potential clinical application. © 2018, Fundacao Oswaldo Cruz. All rights reserved

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

Etiology of genital ulcer disease in a sexually transmitted infection reference center in Manaus, Brazilian Amazon.

OBJECTIVES: To determine the etiology and factors associated with genital ulcer disease (GUD) among patients presenting to a sexually transmitted infections clinic in Manaus, Brazil; and to compare a multiplex polymerase chain reaction (M-PCR) assay for the diagnosis of GUD with standard methods. METHODS: Ulcer swabs were collected and used for Tzanck test and processed in an M-PCR to detect herpes simplex virus (HSV-1/2), Treponema pallidum (T. pallidum), and Haemophilus ducreyi (H. ducreyi). Sera were tested for HIV and syphilis antibodies. Multivariable analysis was used to measure the association between clinical aspects and GUD. M-PCR results were compared with syphilis serology and Tzanck tests. RESULTS: Overall, 434 GUD samples were evaluated, 84.8% from men. DNA from HSV-2 was detected in 55.3% of GUD samples, T. pallidum in 8.3%, HSV-1 in 3.2%, and 32.5% of GUD specimens were negative for the DNA of all three pathogens. No cases of H. ducreyi were identified. HIV serology among GUD patients was 3.2%. Treponemal antibodies and Tzanck test positivity for genital herpes was detected in 25 (5.8%) and in 125 (30.3%) of GUD patients, respectively. In multivariable analysis genital herpes etiology by M-PCR was associated with the vesicular, multiple and recurrent lesions whereas T. pallidum with non-vesicular, non-recurrent lesions. Compared to M-PCR, syphilis serology was 27.8% sensitive and 96.2% specific whereas Tzanck test was 43.8% sensitive and 88.9% specific. CONCLUSIONS: The predominance of genital herpes etiology suggests a revision of existing national syndromic treatment guidelines in Brazil to include antiherpetic treatment for all GUD patients. The use of M-PCR can significantly improve the diagnosis of GUD and provide a greater sensitivity than standard diagnostics

Patients Demographics, Clinical Characteristics and Distribution of Genital Ulcer-Causing Pathogens by M-PCR.

<p>HSV-1: herpes simplex virus type 1; HSV-2: herpes simplex virus type 2; TP: <i>T. pallidum</i>; HSV: herpes simplex virus type 1 and type 2; ND: pathogen not detected by M-PCR.</p