In the execution of its legislated responsibilities, the United States Food and Drug Administration commonly refers to standard test methods detailed in the United States Pharmacopeia (USP). Microbiological test methods (contained in general chapters) are listed in chapters <51> to <80> with details regarded as enforceable where referenced as a test method. USP <61> \"Microbiological Examination of Nonsterile Products: Microbial Enumeration Tests\" is a globally harmonized chapter that has been successfully employed for the enumeration of microorganisms recoverable from nonsterile finished drug products. The content of USP <61> is not always scientifically principled nor emphatically understood by all pharmaceutical microbiologists. Consequently, misunderstanding and misapplication of USP <61> may result in analyses and assessments of microbiological quality that are flawed or erroneous. In this article, clarification is provided to assist the pharmaceutical microbiologist in the appropriate and intended use of USP <61>, including provision of details not always commonly known or understood.

Pseudomonas aeruginosa is an opportunistic pathogen that causes difficult-to-treat infections. Two well-studied divergent P. aeruginosa strain types, PAO1 and PA14, have significant genomic heterogeneity, including diverse accessory genes present in only some strains. Genome content comparisons find core genes that are conserved across both PAO1 and PA14 strains and accessory genes that are present in only a subset of PAO1 and PA14 strains. Here, we use recently assembled transcriptome compendia of publicly available P. aeruginosa RNA sequencing (RNA-seq) samples to create two smaller compendia consisting of only strain PAO1 or strain PA14 samples with each aligned to their cognate reference genome. We confirmed strain annotations and identified other samples for inclusion by assessing each sample\'s median expression of PAO1-only or PA14-only accessory genes. We then compared the patterns of core gene expression in each strain. To do so, we developed a method by which we analyzed genes in terms of which genes showed similar expression patterns across strain types. We found that some core genes had consistent correlated expression patterns across both compendia, while others were less stable in an interstrain comparison. For each accessory gene, we also determined core genes with correlated expression patterns. We found that stable core genes had fewer coexpressed neighbors that were accessory genes. Overall, this approach for analyzing expression patterns across strain types can be extended to other groups of genes, like phage genes, or applied for analyzing patterns beyond groups of strains, such as samples with different traits, to reveal a deeper understanding of regulation. IMPORTANCE Pseudomonas aeruginosa is a ubiquitous pathogen. There is much diversity among P. aeruginosa strains, including two divergent but well-studied strains, PAO1 and PA14. Understanding how these different strain-level traits manifest is important for identifying targets that regulate different traits of interest. With the availability of thousands of PAO1 and PA14 samples, we created two strain-specific RNA-seq compendia where each one contains hundreds of samples from PAO1 or PA14 strains and used them to compare the expression patterns of core genes that are conserved in both strain types and to determine which core genes have expression patterns that are similar to those of accessory genes that are unique to one strain or the other using an approach that we developed. We found a subset of core genes with different transcriptional patterns across PAO1 and PA14 strains and identified those core genes with expression patterns similar to those of strain-specific accessory genes.

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.

BACKGROUND: Exploring cellular responses to stimuli using extensive gene expression profiles has become a routine procedure performed on a daily basis. Raw and processed data from these studies are available on public databases but the opportunity to fully exploit such rich datasets is limited due to the large heterogeneity of data formats. In recent years, several approaches have been proposed to effectively integrate gene expression data for analysis and exploration at a broader level. Despite the different goals and approaches towards gene expression data integration, the first step is common to any proposed method: data acquisition. Although it is seemingly straightforward to extract valuable information from a set of downloaded files, things can rapidly get complicated, especially as the number of experiments grows. Transcriptomic datasets are deposited in public databases with little regard to data format and thus retrieving raw data might become a challenging task. While for RNA-seq experiments such problem is partially mitigated by the fact that raw reads are generally available on databases such as the NCBI SRA, for microarray experiments standards are not equally well established, or enforced during submission, and thus a multitude of data formats has emerged.
RESULTS: COMMAND>_ is a specialized tool meant to simplify gene expression data acquisition. It is a flexible multi-user web-application that allows users to search and download gene expression experiments, extract only the relevant information from experiment files, re-annotate microarray platforms, and present data in a simple and coherent data model for subsequent analysis.
CONCLUSIONS: COMMAND>_ facilitates the creation of local datasets of gene expression data coming from both microarray and RNA-seq experiments and may be a more efficient tool to build integrated gene expression compendia. COMMAND>_ is free and open-source software, including publicly available tutorials and documentation.

Prescription drug labeling is an authoritative source of information that guides the safe and effective use of approved medications. In many instances, however, labeling may fail to be updated as new information about drug efficacy emerges in the postmarket setting. When labeling becomes outdated, it loses its value for prescribers and undermines a core part of the FDA\'s mission to communicate accurate and reliable information to patients and physicians.
We compared the number of drug uses indicated on product labels to the number of uses contained in a leading drug compendium for 43 cancer drugs approved between 1999 and 2011. We defined a \"well-accepted off-label use\" of a drug as one that was not approved by the FDA and received a category 1 or 2A evidence grade.
Of the 43 drugs reviewed in this study, 34 (79%) had at least one well-accepted off-label use. In total, 253 off-label uses were identified; 91% were well accepted, and 65% were in cancer types not previously represented on labeling. Off-patent drugs had more well-accepted off-label uses than brand-name drugs, on average (mean 13.7 vs 3.8, P = .018).
The labeling for many cancer drugs, particularly for older drugs, is outdated. Although FDA-approved labeling can never be fully aligned with real-world clinical practice, steps should be taken to better align the two when high-quality data exist. Such steps, if taken, will assist patients and prescribers in discerning which uses of drugs are supported by the highest quality evidence.

Post-translational modifications (PTMs) of protein amino acids are ubiquitous and important to protein function, localization, degradation, and more. In recent years, there has been an explosion in the discovery of PTMs as a result of improvements in PTM measurement techniques, including quantitative measurements of PTMs across multiple conditions. ProteomeScout is a repository for such discovery and quantitative experiments and provides tools for visualizing PTMs within proteins, including where they are relative to other PTMS, domains, mutations, and structure. ProteomeScout additionally provides analysis tools for identifying statistically significant relationships in experimental datasets. This unit describes four basic protocols for working with the ProteomeScout Web interface or programmatically with the database download. © 2017 by John Wiley & Sons, Inc.

This cross-country study adopts a competing theories approach in which both a value perspective and a social capital perspective are used to understand the relation between religion and a country\'s business ownership rate. We distinguish among four dimensions of religion: belonging to a religious denomination, believing certain religious propositions, bonding to religious practices, and behaving in a religious manner. An empirical analysis of data from 30 OECD countries with multiple data points per country covering the period 1984-2010 suggests a positive relationship between religion and business ownership based on those dimensions that reflect the internal aspects of religiosity (i.e., believing and behaving). We do not observe a significant association for those dimensions that reflect more external aspects of religion (i.e., belonging and bonding). These results suggest that the social capital perspective prevails the value perspective, at least when internal aspects of religiosity are concerned. More generally, our study demonstrates the importance of distinguishing between different dimensions of religion when investigating the link between religion and entrepreneurship.

Interactions of antiepileptic drugs (AEDs) with other substances may lead to adverse effects and treatment failure. To avoid such interactions, clinicians often rely on drug interaction compendia. Our objective was to compare the concordance for twenty-two AEDs among three drug interaction compendia (Micromedex, Lexi-Interact, and Clinical Pharmacology) and the US Food and Drug Administration-approved product labels. For each AED, the overall concordance among data sources regarding existence of interactions and their classification was poor, with less than twenty percent of interactions listed in all four sources. Concordance among the three drug compendia decreased with the fraction of the drug excreted unchanged and was greater for established inducers of hepatic drug-metabolizing enzymes than for the drugs that are not inducers (R-square=0.83, P<0.01). For interactions classified as contraindications, major, and severe, concordance among the four data sources was, in most cases, less than 30%. Prescribers should be aware of the differences between drug interaction sources of information for both older AEDs and newer AEDs, in particular for those AEDs which are not involved in hepatic enzyme-mediated interactions.