Expression of tyrosine kinase receptor AXL is associated with worse outcome of metastatic renal cell carcinomas treated with sunitinib

Expression of tyrosine kinase receptor AXL is associated with worse outcome of metastatic renal cell carcinomas treated with sunitinibBackground: Renal cell carcinoma (RCC) represents 2%-3% of all cancers of the Western countries. Currently, sunitinib, a receptor tyrosine kinase inhibitor, particularly of PDGF and VEGF receptors, is the first-line therapy for metastatic RCC (mRCC), with significant improvement in clinical outcome. However, there is a lack of predictive biomarkers of sunitinib response. Recently, others and our group suggested that the receptor tyrosine kinase AXL may modify the response to sunitinib. Objective: To study the expression of AXL in a series patients with of mRCC treated with sunitinib and to correlate it with patient's clinic-pathological features and therapeutic response. Material and methods: Sixty-four patients with mRCC (51 clear cell carcinomas (CCCs) and 13 non-CCCs) were evaluated for AXL expression by immunohistochemistry in the primary tumor. Results: AXL positivity was observed in 47% (30/64) of cases, namely in 43% (22/51) of CCCs and 61% (8/13) of non-CCC. Considering only the clear cell subtype, the univariate analysis showed that AXL expression was statistically associated with a poor prognosis, with a median overall survival of 13 months vs. 43 months in patients with negative AXL. In this subtype, along with the AXL positivity, other prognostic factors were absence of nephrectomy, Karnofsky performance status, more than 1 site of metastasis and liver metastasis. Moreover, AXL expression was associated with shorter progression to sunitinib. Overall, the multivariate survival analysis showed that absence of nephrectomy (HR = 4.85, P = 0.001), more than 1 site of metastasis (HR = 2.99, P = 0.002), bone metastasis (HR = 2.95, P = 0.001), together with AXL expression (HR = 2.01, P = 0.048) were independent poor prognostic factor in patients with mRCC. Conclusion: AXL expression was associated with worse clinical outcome and may be an important prognostic biomarker in sunitinibtreated patients with metastatic renal cell carcinoma.this study was supported by Barretos Cancer Hospital Internal Research Funds (PAIP) of participant authors. Rui Manuel Reis is recipient of a National Council of Technological and Scientific Development (CNPq) scholarship.info:eu-repo/semantics/publishedVersio

Detection and Quantitative Analysis of Two Independent Binding Modes of a Small Ligand Responsible for DC-SIGN Clustering

DC-SIGN (dendritic cell-specific ICAM-3 grabbing non-integrin) is a C-type lectin receptor (CLRs) present, mainly in dendritic cells (DCs), as one of the major pattern recognition receptors (PRRs). This receptor has a relevant role in viral infection processes. Recent approaches aiming to block DC-SIGN have been presented as attractive anti-HIV strategies. DC-SIGN binds mannose or fucose-containing carbohydrates from viral proteins such as the HIV envelope glycoprotein gp120. We have previously demonstrated that multivalent dendrons bearing multiple copies of glycomimetic ligands were able to inhibit DC-SIGN-dependent HIV infection in cervical explant models. Optimization of glycomimetic ligands requires detailed characterization and analysis of their binding modes because they notably influence binding affinities. In a previous study we characterized the binding mode of DC-SIGN with ligand 1, which shows a single binding mode as demonstrated by NMR and X-ray crystallography. In this work we report the binding studies of DC-SIGN with pseudotrisaccharide 2, which has a larger affinity. Their binding was analysed by TR-NOESY and STD NMR experiments, combined with the CORCEMA-ST protocol and molecular modelling. These studies demonstrate that in solution the complex cannot be explained by a single binding mode. We describe the ensemble of ligand bound modes that best fit the experimental data and explain the higher inhibition values found for ligand

Antiviral Activity of Self‐Assembled Glycodendro[60]fullerene Monoadducts

A series of amphiphilic glycodendro[60]fullerene monoadducts were efficiently synthesized using the CuAAC “click chemistry” approach. These glycodendrofullerenes can self‐assemble in aqueous media, in a process favoured through π‐ π interactions between the [60]fullerene moieties. This aggregation process leads to big and well‐defined compact micelles with a uniform size and spherical‐shape. The supramolecular aggregate was characterized using electronic microscopy (SEM and TEM), light scattering methods (DLS) and X‐ray methodologies (SAXS and XRD). The antiviral efficiency of these aggregates has been tested in an experimental infection assay using Ebola virus glycoprotein (EboGP) pseudotyped viral particles on Jurkat cells overexpressing DC‐SIGN and it is observed an improvement of the IC50 value with respect to other systems endowed with a higher number of carbohydrate ligands

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

2 nd Brazilian Consensus on Chagas Disease, 2015

Abstract Chagas disease is a neglected chronic condition with a high burden of morbidity and mortality. It has considerable psychological, social, and economic impacts. The disease represents a significant public health issue in Brazil, with different regional patterns. This document presents the evidence that resulted in the Brazilian Consensus on Chagas Disease. The objective was to review and standardize strategies for diagnosis, treatment, prevention, and control of Chagas disease in the country, based on the available scientific evidence. The consensus is based on the articulation and strategic contribution of renowned Brazilian experts with knowledge and experience on various aspects of the disease. It is the result of a close collaboration between the Brazilian Society of Tropical Medicine and the Ministry of Health. It is hoped that this document will strengthen the development of integrated actions against Chagas disease in the country, focusing on epidemiology, management, comprehensive care (including families and communities), communication, information, education, and research

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