1987: Resistance (anti-Baudrillard)

Ponencia leída en el Foro de Comunicaciones IkasArt II (BEC Barakaldo, 2010.06.17)

Association between dietary phylloquinone intake and peripheral metabolic risk markers related to insulin resistance and diabetes in elderly subjects at high cardiovascular risk

Background: Vitamin K has been related to glucose metabolism, insulin sensitivity and diabetes. Because inflammation underlies all these metabolic conditions, it is plausible that the potential role of vitamin K in glucose metabolism occurs through the modulation of cytokines and related molecules. The purpose of the study was to assess the associations between dietary intake of vitamin K and peripheral adipokines and other metabolic risk markers related to insulin resistance and type 2 diabetes mellitus. Methods: Cross-sectional and longitudinal assessments of these associations in 510 elderly participants recruited in the PREDIMED centers of Reus and Barcelona (Spain). We determined 1-year changes in dietary phylloquinone intake estimated by food frequency questionnaires, serum inflammatory cytokines and other metabolic risk markers. Results: In the cross-sectional analysis at baseline no significant associations were found between dietary phylloquinone intake and the rest of metabolic risk markers evaluated, with exception of a negative association with plasminogen activator inhibitor-1. After 1-year of follow-up, subjects in the upper tertile of changes in dietary phylloquinone intake showed a greater reduction in ghrelin (-15.0%), glucose-dependent insulinotropic peptide (-12.9%), glucagon-like peptide-1 (-17.6%), IL-6 (-27.9%), leptin (-10.3%), TNF (-26.9%) and visfatin (-24.9%) plasma concentrations than those in the lowest tertile (all p<0.05). Conclusion: These results show that dietary phylloquinone intake is associated with an improvement of cytokines and other markers related to insulin resistance and diabetes, thus extending the potential protection by dietary phylloquinone on chronic inflammatory diseases.

Multi-drug resistance profiles and the genetic features of Acinetobacter baumannii isolates from Bolivia

Introduction: Acinetobacter baumannii is opportunistic in debilitated hospitalised patients. Because information from some South American countries was previously lacking, this study examined the emergence of multi-resistant A. baumannii in three hospitals in Cochabamba, Bolivia, from 2008 to 2009. Methodology: Multiplex PCR was used to identify the main resistance genes in 15 multi-resistant A. baumannii isolates. RT-PCR was used to measure gene expression. The genetic environment of these genes was also analysed by PCR amplification and sequencing. Minimum inhibitory concentrations were determined for key antibiotics and some were determined in the presence of an efflux pump inhibitor, 1-(1-napthylmethyl) piperazine. Results: Fourteen strains were found to be multi-resistant. Each strain was found to have the bla(OXA-58) gene with the ISAba3-like element upstream, responsible for over-expression of the latter and subsequent carbapenem resistance. Similarly, ISAba1, upstream of the bla(ADC) gene caused over-expression of the latter and cephalosporin resistance; mutations in the gyrA(Ser83 to Leu) and parC (Ser-80 to Phe) genes were commensurate with fluoroquinolone resistance. In addition, the adeA, adeB efflux genes were over-expressed. All 15 isolates were positive for at least two aminoglycoside resistance genes. Conclusion: This is one of the first reports analyzing the multi-drug resistance profile of A. baumannii strains isolated in Bolivia and shows that the over-expression of thebla(OXA-58), bla(ADC) and efflux genes together with aminoglycoside modifying enzymes and mutations in DNA topoisomerases are responsible for the multi-resistance of the bacteria and the subsequent difficulty in treating infections caused by them.

Landscape Genetics for the Empirical Assessment of Resistance Surfaces: The European Pine Marten (Martes martes) as a Target-Species of a Regional Ecological Network

Coherent ecological networks (EN) composed of core areas linked by ecological corridors are being developed worldwide with the goal of promoting landscape connectivity and biodiversity conservation. However, empirical assessment of the performance of EN designs is critical to evaluate the utility of these networks to mitigate effects of habitat loss and fragmentation. Landscape genetics provides a particularly valuable framework to address the question of functional connectivity by providing a direct means to investigate the effects of landscape structure on gene flow. The goals of this study are (1) to evaluate the landscape features that drive gene flow of an EN target species (European pine marten), and (2) evaluate the optimality of a regional EN design in providing connectivity for this species within the Basque Country (North Spain). Using partial Mantel tests in a reciprocal causal modeling framework we competed 59 alternative models, including isolation by distance and the regional EN. Our analysis indicated that the regional EN was among the most supported resistance models for the pine marten, but was not the best supported model. Gene flow of pine marten in northern Spain is facilitated by natural vegetation, and is resisted by anthropogenic landcover types and roads. Our results suggest that the regional EN design being implemented in the Basque Country will effectively facilitate gene flow of forest dwelling species at regional scale.

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

Characterisation of patients with brachydactyly type E associated or not with hormonal resistance: clinical, genetic and radiological approach

334 p.