Average bioequivalence for two-sequence two-period crossover design with incomplete data

Discovering monotonic stemness marker genes from time-series stem cell microarray data

Background

Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DE_total) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DE_total of each gene. MFSelector can successfully identify genes with monotonic characteristics.

Results

We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.

Conclusions

We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/.

Analysis of cross-over studies with missing data

This paper addresses some aspects of the analysis of cross-over trials with missing or incomplete data. A literature review on the topic reveals that many proposals provide correct results under the missing completely at random assumption while only some consider the more general missing at random situation. It is argued that mixed-effects models have a role in this context to recover some of the missing intra-subject from the inter-subject information, in particular when missingness is ignorable. Eventually, sensitivity analyses to deal with more general missingness mechanisms are presented.

Bioinformatic approaches to metabolic pathways analysis

The growth and development in the last decade of accurate and reliable mass data collection techniques has greatly enhanced our comprehension of cell signaling networks and pathways. At the same time however, these technological advances have also increased the difficulty of satisfactorily analyzing and interpreting these ever-expanding datasets. At the present time, multiple diverse scientific communities including molecular biological, genetic, proteomic, bioinformatic, and cell biological, are converging upon a common endpoint, that is, the measurement, interpretation, and potential prediction of signal transduction cascade activity from mass datasets. Our ever increasing appreciation of the complexity of cellular or receptor signaling output and the structural coordination of intracellular signaling cascades has to some extent necessitated the generation of a new branch of informatics that more closely associates functional signaling effects to biological actions and even whole-animal phenotypes. The ability to untangle and hopefully generate theoretical models of signal transduction information flow from transmembrane receptor systems to physiological and pharmacological actions may be one of the greatest advances in cell signaling science. In this overview, we shall attempt to assist the navigation into this new field of cell signaling and highlight several methodologies and technologies to appreciate this exciting new age of signal transduction.

Bioequivalence assessment of two formulations of ibuprofen

BACKGROUND

This study assessed the relative bioavailability of two formulations of ibuprofen. The first formulation was Doloraz(®), produced by Al-Razi Pharmaceutical Company, Amman, Jordan. The second forumulation was Brufen(®), manufactured by Boots Company, Nottingham, UK.

METHODS AND RESULTS

A prestudy validation of ibuprofen demonstrated long-term stability, freeze-thaw stability, precision, and accuracy. Twenty-four healthy volunteers were enrolled in this study. After overnight fasting, the two formulations (test and reference) of ibuprofen (100 mg ibuprofen/5 mL suspension) were administered as a single dose on two treatment days separated by a one-week washout period. After dosing, serial blood samples were drawn for a period of 14 hours. Serum harvested from the blood samples was analyzed for the presence of ibuprofen by high-pressure liquid chromatography with ultraviolet detection. Pharmacokinetic parameters were determined from serum concentrations for both formulations. The 90% confidence intervals of the ln-transformed test/reference treatment ratios for peak plasma concentration and area under the concentration-time curve (AUC) parameters were found to be within the predetermined acceptable interval of 80%-125% set by the US Food and Drug Administration.

CONCLUSION

Analysis of variance for peak plasma concentrations and AUC parameters showed no significant difference between the two formulations and, therefore, Doloraz was considered bioequivalent to Brufen.

Protein microarray platforms for clinical proteomics

Proteomics for clinical applications is presently in a state of transition. It has become clear that the classical approaches based on 2-DE and/or MS need to be complemented by different kinds of technologies. The well-known problems include sample complexity, sensitivity, quantitation, reproducibility, and analysis time. We suggest that the new technologies for clinical proteomics can be supported by antibody-centric protein microarray platforms. These platforms presently include antibody microarrays and lysate, or reverse capture/reverse phase protein microarrays. Other forms of these arrays are in less mature developmental stages, including ORF and self assembling protein microarrays. Bioinformatic support for interpreting these arrays is becoming more available as the whole field of systems biology begins to mature. The present set of applications for these platforms is profoundly focused on certain common cancers, immunology, and cystic fibrosis. However, we predict that many more disease entities will become studied as knowledge of the power and availability of these platforms becomes more widely established. We anticipate that these platforms will eventually evolve to accommodate label-free detection technologies, human genome-scale numbers of analytes, and increases in analytic and bioinformatic speeds.