The total release of chromium was determined in wells containing 51Cr-labeled cells with RPMI 1640, 10% FBS with 10% triton X-100. Spontaneous release was always selleck chemicals less than 10% of total release. NKCA was calculated as the mean of triplicate determinations for each E:T ratio and was expressed as percentage lysis, calculated as follows: %lysis=mean experimental counts per minute-mean spontaneous counts per minutemean maximum counts per minute-mean spontaneous counts per minute×100 The necessary sample size for our observations was calculated

using SigmaStat software (Jandel Scientific, San Rafael, CA), as described previously (Raso et al., 2007), with α = 0.05 and β = 0.20. A one-sample Kolmogorov–Smirnov test demonstrated the normality of data distribution for all measured variables. Basic data ERK inhibitor are presented as means ± standard error of the mean. Independent sample “t” tests compared subjects grouped according to their fitness percentile (i.e., P0 − P50versus P50 − P100) for aerobic power and muscle strength. Univariate and hierarchical multiple regression analysis investigated

associations of phenotypic and functional immunological parameters with aerobic power, muscle strength and mood state. Bonferroni corrections were applied where appropriate. All analyses were performed using Predictive Analytics Software 17.0 for Windows package (PASW, Inc., Chicago, IL). With few exceptions, subjects fell into the “young-old” age category. Scores for the various measures of fitness, mood state and carbohydrate intake were all at the levels anticipated for relatively inactive but otherwise healthy individuals in this age category (Table 1). The average body mass index was only a little above the ideal range, and the average participant was obtaining <40% of the estimated total energy intake of 6.90 ± 0.34 MJ day−1; Phospholipase D1 1659 ± 81 kcal day−1 from carbohydrate; however, there were wide inter-individual differences, probably due

in part to imprecise reporting and some under-reporting of overall food consumption. When aerobic power values were used to classify subjects into upper and lower halves of a fitness continuum, fitter subjects had a lower BMI (P = .033), body fat content (P = .001), and muscle strength (P = .041) ( Table 1). However, there were no significant differences of general physical characteristics when subjects were categorized in terms of muscle strength. Scores for the psychobiological variables (depression, fatigue and quality of life) were not significantly influenced by either measure of fitness. Values for a wide range of immune parameters are summarized in Table 2, with arrows indicating the anticipated trend of older individuals relative to published values for young women.