Differential expression of HIF-1α in CD44+CD24-/low breast ductal carcinomas

Abstract Background Cancer stem cell (CSC) hypothesis postulates that tumors are maintained by a self-renewing CSC population that is also capable of differentiating into non-self-renewing cell populations that constitute the bulk of tumor. Stem cells renewal and differentiation can be directly influenced by the oxygen levels of determined tissues, probably by the reduction of oxidative DNA damage in hypoxic regions, thus leading to a friendlier microenvironment, regarding to clonal expansion and for resistance to chemotherapeutic regimens. Furthermore, there have been strong data indicating a pivotal role of hypoxic niche in cancer stem cells development. There are evidence that hypoxia could drive the maintenance of CSC, via HIF-1α expression, but it still to be determined whether hypoxia markers are expressed in breast tumors presenting CD44+CD24-/low immunophenotype. Methods Immunohistochemical analysis of CD44+CD24-/low expression and its relationship with hypoxia markers and clinical outcome were evaluated in 253 samples of breast ductal carcinomas. Double-immunolabeling was performed using EnVision Doublestain System (Dako, Carpinteria, CA, USA). Slides were then scanned into high-resolution images using Aperio ScanScope XT and then, visualized in the software Image Scope (Aperio, Vista, CA, USA). Results In univariate analysis, CD44+CD24-/low expression showed association with death due to breast cancer (p = 0.035). Breast tumors expressing CD44+CD24-/low immunophenotype showed relationship with HIF-1α (p = 0.039) and negativity for HER-2 (p = 0.013). Conclusion Considering that there are strong evidences that the fraction of a tumour considered to be cancer stem cells is plastic depending upon microenvironmental signals, our findings provide further evidence that hypoxia might be related to the worse prognosis found in CD44+CD24-/low positive breast tumors.

A new approach for modeling and control of nonlinear systems via norm-bounded linear differential inclusions

A systematic approach to model nonlinear systems using norm-bounded linear differential inclusions (NLDIs) is proposed in this paper. The resulting NLDI model is suitable for the application of linear control design techniques and, therefore, it is possible to fulfill certain specifications for the underlying nonlinear system, within an operating region of interest in the state-space, using a linear controller designed for this NLDI model. Hence, a procedure to design a dynamic output feedback controller for the NLDI model is also proposed in this paper. One of the main contributions of the proposed modeling and control approach is the use of the mean-value theorem to represent the nonlinear system by a linear parameter-varying model, which is then mapped into a polytopic linear differential inclusion (PLDI) within the region of interest. To avoid the combinatorial problem that is inherent of polytopic models for medium- and large-sized systems, the PLDI is transformed into an NLDI, and the whole process is carried out ensuring that all trajectories of the underlying nonlinear system are also trajectories of the resulting NLDI within the operating region of interest. Furthermore, it is also possible to choose a particular structure for the NLDI parameters to reduce the conservatism in the representation of the nonlinear system by the NLDI model, and this feature is also one important contribution of this paper. Once the NLDI representation of the nonlinear system is obtained, the paper proposes the application of a linear control design method to this representation. The design is based on quadratic Lyapunov functions and formulated as search problem over a set of bilinear matrix inequalities (BMIs), which is solved using a two-step separation procedure that maps the BMIs into a set of corresponding linear matrix inequalities. Two numerical examples are given to demonstrate the effectiveness of the proposed approach.

Desafios atuais da modelagem preditiva de distribuição de espécies

A modelagem preditiva tem sido aplicada para analisar a distribuição geográfica de espécies, a partir de extrapolações das características ambientais dos locais conhecidos de ocorrência. O interesse por esse tipo de modelagem deve-se à necessidade de respostas rápidas e fundamentadas para as ameaças que as espécies têm enfrentado, devido à perda de habitat, invasão de espécies exóticas, mudanças climáticas, entre outros. Este artigo oferece uma visão geral dos avanços recentes no campo da modelagem e visa incentivar a discussão e aplicação desse método, que pode auxiliar tanto na aquisição de conhecimento básico sobre a biologia das espécies, quanto na análise e formulação de políticas para sua conservação.

Analysis of genetic lineages and their correlation with virulence genes in enterococcus faecalis clinical isolates from root canal and systemic infections

Introduction: Enterococcus faecalis is a member of the mammalian gastrointestinal microbiota but has been considered a leading cause of hospital-acquired infections. In the oral cavity, it is commonly detected from root canals of teeth with failed endodontic treatment. However, little is known about the virulence and genetic relatedness among E. faecalis isolates from different clinical sources. This study compared the presence of enterococcal virulence factors among root canal strains and clinical isolates from hospitalized patients to identify virulent clusters of E. faecalis. Methods: Multilocus sequence typing analysis was used to determine genetic lineages of 40 E. faecalis clinical isolates from different sources. Virulence clusters were determined by evaluating capsule (cps) locus polymorphisms, pathogenicity island gene content, and antibiotic resistance genes by polymerase chain reaction. Results: The clinical isolates from hospitalized patients formed a phylogenetically separate group and were mostly grouped in the clonal complex 2, which is a known virulent cluster of E. faecalis that has caused infection outbreaks globally. The clonal complex 2 group comprised capsule-producing strains harboring multiple antibiotic resistance and pathogenicity island genes. On the other hand, the endodontic isolates were more diverse and harbored few virulence and antibiotic resistance genes. In particular, although more closely related to isolates from hospitalized patients, capsuleproducing E. faecalis strains from root canals did not carry more virulence/antibiotic genes than other endodontic isolates. Conclusions: E. faecalis isolates from endodontic infections have a genetic and virulence profile different from pathogenic clusters of hospitalized patients’ isolates, which is most likely due to niche specialization conferred mainly by variable regions in the genome.