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In this study we evaluated for a realistic head model the 3D temperature rise induced by a mobile phone. This was done numerically with the consecutive use of an FDTD model to predict the absorbed electromagnetic power distribution, and a thermal model describing bioheat transfer both by conduction and by blood flow. We calculated a maximum rise in brain temperature of 0.11 degrees C for an antenna with an average emitted power of 0.25 W, the maximum value in common mobile phones, and indefinite exposure. Maximum temperature rise is at the skin. The power distributions were characterized by a maximum averaged SAR over an arbitrarily shaped 10 g volume of approximately 1.6 W kg(-1). Although these power distributions are not in compliance with all proposed safety standards, temperature rises are far too small to have lasting effects. We verified our simulations by measuring the skin temperature rise experimentally. Our simulation method can be instrumental in further development of safety standards.

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Objective To develop a clinical prediction rule that can help the clinician to identify women at high and low risk for gestational diabetes mellitus (GDM) early in pregnancy in order to improve the efficiency of GDM screening.Design We used data from a prospective cohort study to develop the clinical prediction rule.Setting The original cohort study was conducted in a university hospital in the Netherlands.Population Nine hundred and ninety-five consecutive pregnant women underwent screening for GDM.Methods Using multiple logistic regression analysis, we constructed a model to estimate the probability of development of GDM from the medical history and patient characteristics. Receiver operating characteristics analysis and calibration were used to assess the accuracy of the model.Main outcome measure The development of a clinical prediction rule for GDM. We also evaluated the potential of the prediction rule to improve the efficiency of GDM screening.Results The probability of the development of GDM could be predicted from the ethnicity, family history, history of GDM and body mass index. The model had an area under the receiver operating characteristic curve of 0.77 (95% CI 0.69-0.85) and calibration was good (Hosmer and Lemeshow test statistic, P = 0.25). If an oral glucose tolerance test was performed in all women with a predicted probability of 2% or more, 43% of all women would be tested and 75% of the women with GDM would be identified.Conclusions The use of a clinical prediction model is an accurate method to identify women at increased risk for GDM, and could be used to select women for additional testing for GDM.

This 8-year study demonstrates that GH treatment is a potent force for counteracting the clinical course of obesity in children with PWS.