Quantification of lean bodyweight

Background: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18-82 years), bodyweight (40.7-216.5kg) and BMI values (17.1-69.9 kg/m(2)). Patients in population B had BMI values of 18.7-38.4 kg/m(2). A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r(2) = 0.78, mean error [ME] = 2.30 x 10(-3), root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r(2) = 0.93, ME = -0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r(2) = 0.85, ME -0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.

White and gray matter development in human fetal, newborn and pediatric brains

Brain anatomy is characterized by dramatic growth from the end of the second trimester through the neonatal stage. The characterization of normal axonal growth of the white matter tracts has not been well-documented to date and could provide important clues to understanding the extensive inhomogeneity of white matter injuries in cerebral palsy (CP) patients. However, anatomical studies of human brain development during this period are surprisingly scarce and histology-based atlases have become available only recently. Diffusion tensor magnetic resonance imaging (DTMRI) can reveal detailed anatomy of white matter. We acquired diffusion tensor images (DTI) of postmortem fetal brain samples and in vivo neonates and children. Neural structures were annotated in two-dimensional (2D) slices, segmented, measured, and reconstructed three-dimensionally (3D). The growth status of various white matter tracts was evaluated on cross-sections at 19-20 gestational weeks, and compared with 0-month-old neonates and 5- to 6-year-old children. Limbic, commissural, association, and projection white matter tracts and gray matter structures were illustrated in 3D and quantitatively characterized to assess their dynamic changes. The overall pattern of the time courses for the development of different white matter is that limbic fibers develop first and association fibers last and commissural and projection fibers are forming from anterior to posterior part of the brain. The resultant DTNIRI-based 3D human brain data will be a valuable resource for human brain developmental study and will provide reference standards for diagnostic radiology of premature newborns. (c) 2006 Elsevier Inc. All rights reserved.

Guideline to reference gene selection for quantitative real-time PCR

Today, quantitative real-time PCR is the method of choice for rapid and reliable quantification of mRNA transcription. However, for an exact comparison of mRNA transcription in different samples or tissues it is crucial to choose the appropriate reference gene. Recently glyceraldehyde 3-phosphate dehydrogenase and P-actin have been used for that purpose. However, it has been reported that these genes as well as alternatives, like rRNA genes, are unsuitable references, because their transcription is significantly regulated in various experimental settings and variable in different tissues. Therefore, quantitative real-time PCR was used to determine the mRNA transcription profiles of 13 putative reference genes, comparing their transcription in 16 different tissues and in CCRF-HSB-2 cells stimulated with 12-O-tetradecanoylphorbol-13-acetate and ionomycin. Our results show that Classical reference genes are indeed unsuitable, whereas the RNA polymerase II gene was the gene with the most constant expression in different tissues and following stimulation in CCRF-HSB-2 cells. (C) 2003 Elsevier Inc. All rights reserved.

Evaluation of performance reference compounds in polyethylene-based passive air samplers

Polyethylene-based passive air samplers (PSDs) were loaded with performance reference compounds (PRCs) and deployed in a wind tunnel to examine the effects of wind speed on sampler performance. PRCs could be loaded reproducibly into PSDs, with coefficients of variation only exceeding 20% for the more volatile compounds. When PSDs were exposed to low (0.5-1.5 m s(-1)) and high (3.5-4.5 m s(-1)) wind speeds, PRC loss rate constants generally increased with increasing wind speed and decreased with increasing sampler/air partition coefficients. PSD-based air concentrations calculated using PRC loss rate constants and sampler/air partition coefficients and air concentrations measured using active samplers compared closely. PRCs can be used to account for the effect of differences in wind speeds on sampler performance and measure air concentrations with reasonable accuracy. (c) 2006 Elsevier Ltd. All rights reserved.

A reference methodology for conducting ontological analyses

The ontological analysis of conceptual modelling techniques is of increasing popularity. Related research did not only explore the ontological deficiencies of classical techniques such as ER or UML, but also business process modelling techniques such as ARIS or even Web services standards such as BPEL4WS. While the selected ontologies are reasonably mature, it is the actual process of an ontological analysis that still lacks rigor. The current procedure leaves significant room for individual interpretations and is one reason for criticism of the entire ontological analysis. This paper proposes a procedural model for the ontological analysis based on the use of meta models, the involvement of more than one coder and metrics. This model is explained with examples from various ontological analyses.