Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas

INTRODUCTION: Breast cancer is a heterogeneous group of tumors, and can be subdivided on the basis of histopathological features, genetic alterations and gene-expression profiles. One well-defined subtype of breast cancer is characterized by a lack of HER2 gene amplification and estrogen and progesterone receptor expression ('triple-negative tumors'). We examined the histopathological and gene-expression profile of triple-negative tumors to define subgroups with specific characteristics, including risk of developing distant metastases. METHODS: 97 triple-negative tumors were selected from the fresh-frozen tissue bank of the Netherlands Cancer Institute, and gene-expression profiles were generated using 35K oligonucleotide microarrays. In addition, histopathological and immunohistochemical characterization was performed, and the findings were associated to clinical features. RESULTS : All triple-negative tumors were classified as basal-like tumors on the basis of their overall gene-expression profile. Hierarchical cluster analysis revealed five distinct subgroups of triple-negative breast cancers. Multivariable analysis showed that a large amount of lymphocytic infiltrate (HR = 0.30, 95% CI 0.09-0.96) and absence of central fibrosis in the tumors (HR = 0.14, 95% CI 0.03-0.62) were associated with distant metastasis-free survival. CONCLUSION: Triple-negative tumors are synonymous with basal-like tumors, and can be identified by immunohistochemistry. Based on gene-expression profiling, basal-like tumors are still heterogeneous and can be subdivided into at least five distinct subgroups. The development of distant metastasis in basal-like tumors is associated with the presence of central fibrosis and a small amount of lymphocytic infiltrat

Characterization and genome sequencing of a Citrobacter freundii phage CfP1 harboring a lysin active against multidrug-resistant isolates

Citrobacter spp., although frequently ignored, is emerging as an important nosocomial bacterium able to cause various superficial and systemic life-threatening infections. Considered to be hard-to-treat bacterium due to its pattern of high antibiotic resistance, it is important to develop effective measures for early and efficient therapy. In this study, the first myovirus (vB_CfrM_CfP1) lytic for Citrobacter freundii was microbiologically and genomically characterized. Its morphology, activity spectrum, burst size, and biophysical stability spectrum were determined. CfP1 specifically infects C. freundii, has broad host range (>85 %; 21 strains tested), a burst size of 45 PFU/cell, and is very stable under different temperatures (20 to 50 °C) and pH (3 to 11) values. CfP1 demonstrated to be highly virulent against multidrug-resistant clinical isolates up to 12 antibiotics, including penicillins, cephalosporins, carbapenems, and fluroquinoles. Genomically, CfP1 has a dsDNA molecule with 180,219 bp with average GC content of 43.1 % and codes for 273 CDSs. The genome architecture is organized into function-specific gene clusters typical for tailed phages, sharing 46 to 94 % nucleotide identity to other Citrobacter phages. The lysin gene encoding a predicted D-Ala-D-Ala carboxypeptidase was also cloned and expressed in Escherichia coli and its activity evaluated in terms of pH, ionic strength, and temperature. The lysine optimum activity was reached at 20 mM HEPES, pH 7 at 37 °C, and was able to significantly reduce all C. freundii (>2 logs) as well as Citrobacter koseri (>4 logs) strains tested. Interestingly, the antimicrobial activity of this enzyme was performed without the need of pretreatment with outer membrane-destabilizing agents. These results indicate that CfP1 lysin is a good candidate to control problematic Citrobacter infections, for which current antibiotics are no longer effective.This study was funded by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER006684), and the PhD grants SFRH/BPD/111653/2015 and SFRH/BPD/69356/2010

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe

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