A large body of work has focused on resistivity saturation in systems where scattering is caused by impurities or by thermal effects. Electrical resistivity saturation is here classified as either static, where impurity scattering causes saturation, or dynamic, where changing external parameters such as temperature or pressure cause saturation. Resistivity measurements up to 5 GPa show pressure-induced resistivity saturation in Fe17wt%Si by the change in sign from positive to negative of the temperature coefficient of resistivity. The pressure dependence of the Curie temperature is 10.2(8) K GPa À1 . The Debye temperature is calculated from the Bloch-Gr€ uneisen equation and yields a pressure dependence of 40 (9) 1 Introduction For many highly disordered metallic systems, specifically d-band alloys near room temperature, experimental observations have shown a negative correlation between the values of electrical resistivity (r) and temperature coefficient of electrical resistivity, TCR [1]. The TCR changes sign from positive to negative in a universal range of 150-200 mV cm. This change in sign of TCR corresponds to saturation of electrical resistivity and can be related to impurity scattering. This behaviour is evident for bulk alloys, thin films and amorphous alloys. Tsuei [2] challenged Mooij's universal range of electrical resistivity saturation and with support of additional experimental observations, he proposed a revised saturation range of 30-400 mV cm. On the other hand, temperature studies on A15 superconductors as well as on transition metal compounds such as iron alloys [3][4][5][6] reported nonlinearity in r(T) and the breakdown of Matthiessen's rule. The growth of r(T) close to room temperature is much faster than expected from Boltzman theory, and at some point it grows much slower than suggested by the Bloch-Gr€ uneisen formula and seems bounded by a value of r max $150 mΩ cm, which was also termed resistivity saturation [4].Seemingly different types of resistivity saturations (impurity induced and temperature induced) described above are suggested to originate from a common source which is based on the Ioffe-Regel criterion [7]. Resistivity

High serum levels of triglycerides (Tg) and low levels of high-density lipoprotein cholesterol (HDL-C) are characteristic of the Metabolic Syndrome (MetS). We assessed the ratio of Tg to HDL-C as a way to identify MetS and insulin resistance. We also evaluated its association with severity of carotid atherosclerosis. Data were analyzed from three cohorts totaling 13,908 participants. MetS was defined according to the International Diabetes Federation criteria. Optimal cut-off for Tg/HDL-C ratio was obtained using Youden's index in receiver-operating characteristic (ROC) curve analyses. The risk of MetS and IR in those with a Tg/HDL-C ratio above the optimum cutoff was evaluated by logistic regression analysis. A Tg/HDL-C ratio above the optimal cutoff level significantly increased the odds ratio for MetS in the three cohorts (OR 6.00, 4.04, and 3.50, least in the healthy population), identified insulin resistance defined by the homeostatic model of insulin resistance (HOMA-IR) (p < 0.0001), and was strongly associated with atherosclerosis severity (p = 0.0001). Tg/ HDL-C ratio identifies persons with MetS, insulin resistance, and severe atherosclerosis. It should be used more widely to identify patients at high risk. This is clinically important because insulin resistance is treatable.

ObjectiveThe objective of this study is to examine the magnitude and pattern of small-area geographic variation in rates of preventable hospitalisations for ambulatory care-sensitive conditions (ACSC) across Canada (excluding Québec).Design and settingA cross-sectional study conducted in Canada (excluding Québec) using data from the 2006 Canadian Census Health and Environment Cohort (CanCHEC) linked prospectively to hospitalisation records from the Discharge Abstract Database (DAD) for the three fiscal years: 2006–2007, 2007–2008 and 2008–2009.Primary outcome measurePreventable hospitalisations (ACSC).ParticipantsThe 2006 CanCHEC represents a population of 22 562 120 individuals in Canada (excluding Québec). Of this number, 2 940 150 (13.03%) individuals were estimated to be hospitalised at least once during the 2006–2009 fiscal years.MethodsAge-standardised annualised ACSC hospitalisation rates per 100 000 population were computed for each of the 190 Census Divisions. To assess the magnitude of Census Division-level geographic variation in rates of preventable hospitalisations, the global Moran’s I statistic was computed. ‘Hot spot’ analysis was used to identify the pattern of geographic variation.ResultsOf all the hospitalisation events reported in Canada during the 2006–2009 fiscal years, 337 995 (7.10%) events were ACSC-related hospitalisations. The Moran’s I statistic (Moran’s I=0.355) suggests non-randomness in the spatial distribution of preventable hospitalisations. The findings from the ‘hot spot’ analysis indicate a cluster of Census Divisions located in predominantly rural and remote parts of Ontario, Manitoba and Saskatchewan and in eastern and northern parts of Nunavut with significantly higher than average rates of preventable hospitalisation.ConclusionThe knowledge generated on the small-area geographic variation in preventable hospitalisations can inform regional, provincial and national decision makers on planning, allocation of resources and monitoring performance of health service providers.