Evaluation of random plasma cortisol and the low dose corticotropin test as indicators of adrenal secretory capacity in critically ill patients: A prospective study

It is unclear whether a random plasma cortisol measurement and the corticotropin (ACTH) test adequately reflect glucocorticoid secretory capacity in critical illness. This study aimed to determine whether these tests provide information representative of the 24 hour period. Plasma cortisol was measured hourly for 24 hours in 21 critically ill septic patients followed by a corticotropin test with 1 μ g dose administered intravenously. Serum and urine were analysed for ACTH and free cortisol respectively. Marked hourly variability in plasma cortisol was evident (coefficient of variation 8-30%) with no demonstrable circadian rhythm. The individual mean plasma cortisol concentrations ranged from 286 59 nmol/l to 796 &PLUSMN; 83 nmol/l. The 24 hour mean plasma cortisol was strongly correlated with both random plasma cortisol (r(2) 0.9, P< 0.0001) and the cortisol response to corticotropin (r(2) 0.72, P< 0.001). Only nine percent of patients increased their plasma cortisol by 250 nmol/l after corticotropin (euadrenal response). However, 35% of non-responders had spontaneous hourly rises > 250 nmol/l thus highlighting the limitations of a single point corticotropin test. Urinary free cortisol was elevated (865&PLUSMN; 937 nmol) in both corticotropin responders and non-responders suggesting elevated plasma free cortisol. No significant relationship was demonstrable between plasma cortisol and ACTH. We conclude that although random cortisol measurements and the low dose corticotropin tests reliably reflect the 24 hour mean cortisol in critical illness, they do not take into account the pulsatile nature of cortisol secretion. Consequently, there is the potential for erroneous conclusions about adrenal function based on a single measurement. We suggest that caution be exercised when drawing conclusions on the adequacy of adrenal function based on a single random plasma cortisol or the corticotropin test.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Random vibration of the functionally graded laminates in thermal environments

This work deals with the random free vibration of functionally graded laminates with general boundary conditions and subjected to a temperature change, taking into account the randomness in a number of independent input variables such as Young's modulus, Poisson's ratio and thermal expansion coefficient of each constituent material. Based on third-order shear deformation theory, the mixed-type formulation and a semi-analytical approach are employed to derive the standard eigenvalue problem in terms of deflection, mid-plane rotations and stress function. A mean-centered first-order perturbation technique is adopted to obtain the second-order statistics of vibration frequencies. A detailed parametric study is conducted, and extensive numerical results are presented in both tabular and graphical forms for laminated plates that contain functionally graded material which is made of aluminum and zirconia, showing the effects of scattering in thermo-clastic material constants, temperature change, edge support condition, side-to-thickness ratio, and plate aspect ratio on the stochastic characteristics of natural frequencies. (c) 2005 Elsevier B.V. All rights reserved.