The stability of cerebral glucose utilization was examined in nine right-handed, healthy men (age, 24.88 +/- 2.93 years) using positron emission tomography (PET) and the [18F]-fluorodeoxglucose (FDG) method. Each study was run twice at intervals of 1-12 weeks with the subject at rest. The average cerebral metabolic rate for glucose (CMRGlu) was 5.40 +/- 0.71 mg/100 g per min (coefficient of variance, 13.08). The average intraindividual variation of CMRGlu was 7.91% +/- 15.46% (P = 0.13). Metabolic indices (MI: regional/mean cortical CMRGlu) were used to determine the regional cerebral metabolic distribution. The interindividual (coefficient of variance, 7.13) and intraindividual variabilities (average variation, -0.12% +/- 8.76%) of MI were smaller than those of metabolic rates. No reproducible significant asymmetry was observed. The FDG method used with subjects at rest thus yields low intraindividual variability of both cerebral glucose consumption and regional metabolic distribution, even at an interval of several weeks. Cerebral glucose utilization measured under such conditions may act as a reliable reference for determination of the influences of physiological (activation), pharmacological or pathological processes on cerebral glucose metabolism.

In previous works we have studied the time of death of bone residuals through the following parameters: total lipids, triglicerides, cholesterol, free fatty acids, total proteins, zinc, iron, manganese, and phosphorus. These elements were quantified in groups of recent bones of 1 and 2 years and of 10, 15, 18, and 20 years postmortem. In this present work we are putting these results under statistical analysis consisting of a stepwise regression. This program selects and introduces in the regression the element that shows the highest correlation with the time of death. In successive steps the partial correlations between the date and the elements not already included in the regression are studied, while keeping the effects of the elements already included fixed. As a result we put forward three formulas in which the time of death appears linked with the parameters that define it best. In the first the time of death of the bones Y is estimated according to the protein X1. Y = 40.0014 − 7.4275X1 In the second formula the time of death Y, is estimated according to proteins X1 and triglicerides X2. Y = 45.5970 − 10.8096X1 + 0.4104X2 And in the thrid formula the time of death Y is estimated according to proteins X1, triglicerides X2, and cholesterol X3. Y = 52.2032 − 7.8213X1 + 0.6355X2 − 3.4930 In the three formulas the coefficients of the correlation between the time of death and the variables are improved when the logarithms of the variables are taken, instead of the original measurements.