Development and validation of a high-performance liquid chromatography method using diode array detection for the simultaneous quantification of aripiprazole and dehydro-aripiprazole in human plasma

A high-performance liquid chromatography method with diode array detection (HPLC-DAD) was developed for quantification of aripiprazole and dehydro-aripiprazole, in human plasma. After a simple liquid-liquid extraction, chromatographic separation was carried out on a C18 reversed-phase column, using an ammonium buffer-acetonitrile mobile phase (40:60, v/v). The total run time was only 7 min at a flow-rate of 1.0 ml/min. The precision values were less than 12% and the accuracy values were ranging from 98 to 113% and the lower limit of quantification was 2 ng/ml for both compounds. Calibration curves were linear over a range of 2-1000 ng/ml. The mean trough plasma concentrations in patients treated with aripiprazole were 157 and 29 ng/ml for aripiprazole and dehydro-aripiprazole, respectively.     

Notch signaling regulates mammary stem cell function and luminal cell-fate commitment

The recent identification of mouse mammary stem cells (MaSCs) and progenitor subpopulations has enhanced the prospect of investigating the genetic control of their lineage specification and differentiation. Here we have explored the role of the Notch pathway within the mammary epithelial hierarchy. We show that knockdown of the canonical Notch effector Cbf-1 in the MaSC-enriched population results in increased stem cell activity in vivo as well as the formation of aberrant end buds, implying a role for endogenous Notch signaling in restricting MaSC expansion. Conversely, Notch was found to be preferentially activated in the ductal luminal epithelium in vivo and promoted commitment of MaSCs exclusively along the luminal lineage. Notably, constitutive Notch signaling specifically targeted luminal progenitor cells for expansion, leading to hyperplasia and tumorigenesis. These findings reveal key roles for Notch signaling in MaSCs and luminal cell commitment and further suggest that inappropriate Notch activation promotes the self-renewal and transformation of luminal progenitor cells.     

Evaluation of CHROMagar™ StrepB: A new chromogenic agar medium for aerobic detection of Group B Streptococci in perinatal samples

A selective and chromogenic medium, the CHROMagar™ StrepB agar (CHROM-B) designed for aerobic isolation of Group B Streptococci (GBS) in pregnancy-related specimens, was evaluated in a two-Phase study. CHROM-B was evaluated against CPS3 during the first Phase and against Granada afterwards. It was compared to blood agar plates (COH) and to colimycin nalidixic agar plates (CNA) over both Phases. The study which included 1356 samples, yielded 124 GBS. CHROM-B was significantly more sensitive than COH (76.6% vs 53.2% on d1 and 92.7% vs 64.5% on d2; p < 0.001 for both). CHROM-B yielded positive results sooner than CNA. CPS3 under-performed, partly because of microbiota overgrowth and partly because it did not produce a single and unique colour from the GBS colonies. CHROM-B produced its unique GBS-expected colour sooner than Granada yielding a significantly sooner result for 10% (6/60; p < 0.025). Every 124 GBS could grow typical colonies on CHROM-B and False Negatives were only due to paucimicrobial samples. Granada failed to produce the expected colour from one non-haemolytic GBS. We conclude that CHROMagar™ StrepB performed significantly better, irrespective of the haemolytic properties of GBS strains, and significantly sooner than COH, CNA, CPS3 and Granada.     

Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses

Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.