Renal function and cardiovascular risk markers in a remote Australian Aboriginal community

Background. Australian Aborigines living in remote areas have exceedingly high rates of renal failure together with increased cardiovascular morbidity and mortality. To examine the basis of this association, we studied markers of renal function and cardiovascular (CV) risk in a coastal Aboriginal community in a remote area of the Northern Territory of Australia. End-stage renal disease (ESRD) incidence rates in that community are 15 times the national non-Aboriginal rate and CV mortality rates in the region are increased 5-fold. Methods. A cross-sectional community survey was conducted. Markers of early renal disease examined included urine albumin/creatinine ratio (ACR), serum creatinine concentration and calculated glomerular filtration rate (GFR). CV risk markers included blood pressure as well as measures of glycaemia, diabetes and serum lipids. Results. The study group included 237 people, 58% of the adult population of the community. The crude prevalence of microalbuminuria (urine ACR: 3.4-33.9 g/mol, 30-299 mg/g) was 31% and of overt albuminuria (urine ACR: greater than or equal to34 g/mol, greater than or equal to300 mg/g), 13%. The prevalence of overt albuminuria increased with age, but the prevalence of microalbuminuria was greatest in the 45-54 year age group. Microalbuminuria was associated with increasing body mass index, whereas overt albuminuria was associated with increasing glycated haemoglobin (HbA1c) and systolic blood pressure and a history of diabetes. The prevalence of elevated serum creatinine concentration (greater than or equal to120 mumol/l) was 10%. GFR (calculated using the MDRD equation) was <60 ml/min/1.73m(2) in 12% and 60-79 ml/min/1.73 m(2) in a further 36% of the study population. Although many people with albuminuria had well preserved GFRs, mean GFR was lower in people with higher levels of albuminuria. Conclusions. The high prevalence of markers of renal disease in this community was consistent with their high rates of ESRD. The distribution of microalbuminuria suggested a 'cohort effect', representing a group who will progress to overt albuminuria. The powerful association of renal disease markers with CV risk factors confirms a strong link between renal and CV disease in the early, asymptomatic stages of each. Thus, pathologic albuminuria, in part, might be a manifestation of the metabolic/haemodynamic syndrome and both conditions might arise out of a common menu of risk factors. Hence, a single agenda of primary and secondary intervention may benefit both.

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.