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Objective: One-quarter of deaths in children with chronic renal failure is due to cardiovascular complications. Conventional echocardiographic methods are insufficient for evaluating systolic functions in children with chronic renal failure. The aim of the present study was to investigate cardiac functions in children with chronic renal failure by evaluating left atrial volume and functions. Methods: The present cross-sectional observational study included 44 children undergoing dialysis, 16 children with chronic renal failure but not yet on dialysis, and 20 healthy control subjects. Transthoracic echocardiography was performed for all children. Variables regarding to left ventricle and atrium (left atrial systolic force, left atrial systolic force index, left atrial volume, left ventricular mass index, and relative wall thickness) were measured using two-dimensional and M-mode echocardiography. Results: Left atrial systolic force index was negatively correlated with systolic blood pressure and left ventricular mass (p=0.01, r=0.266 and p=0.02, r=0.347, respectively). However, it was positively correlated with both early and late diastolic mitral inflow velocity (r=0.518, p=0.001 and r=0.828, p=0.001, respectively). There were no significant difference among the groups in terms of left atrial systolic force index and left atrial volume. However, left atrial systolic force index was higher in children with chronic renal failure but not yet on dialysis. Conclusion: Left atrial systolic force was negatively correlated with systolic blood pressure and left ventricular mass. These findings suggested that evaluating left atrial systolic force and left atrial volume were useful to determine diastolic dysfunction and the necessity of dialysis in patient with chronic renal failure. (Anadolu Kardiyol Derg 2014; 14: 280-5)

Weather forecasting, especially for temperature, has always been a popular subject since it affects our daily life and always includes uncertainty as statistics does. The goals of this study are (a) to forecast monthly mean temperature by benefitting meteorological variables like temperature, humidity and rainfall; and (b) to improve the forecast ability by evaluating the forecasting errors depending upon the parameter changes and local or global forecasting methods. Approximately 100 years of meteorological data from 54 automatic meteorology observation stations of Istanbul that is the mega city of Turkey are analyzed to infer about the meteorological behaviour of the city. A new partial least square (PLS) forecasting technique based on chaotic analysis is also developed by using nonlinear time series and variable selection methods. The proposed model is also compared with artificial neural networks (ANNs), which model nonlinearly the relation between inputs and outputs by working neurons like human brain. Ordinary least square (OLS), PLS and ANN methods are used for nonlinear time series forecasting in this study. Major findings are the chaotic nature of the meteorological data of Istanbul and the best performance values of the proposed PLS model.