Although the differentiation of ES cells to cardiomyocytes has been firmly established, the extent to which corresponding cardiac precursor cells can contribute to other cardiac populations remains unclear. To determine the molecular and cellular characteristics of cardiac-fated populations derived from mouse ES (mES) cells, we isolated cardiac progenitor cells (CPCs) from differentiating mES cell cultures by using a reporter cell line that expresses GFP under the control of a cardiac-specific enhancer element of Nkx2-5, a transcription factor expressed early in cardiac development. This ES cell-derived CPC population initially expressed genetic markers of both stem cells and mesoderm, while differentiated CPCs displayed markers of 3 distinct cell lineages (cardiomyocytes, vascular smooth muscle cells, and endothelial cells) -Flk1 (also known as Kdr), c-Kit, and Nkx2-5, but not Brachyury -and subsequently expressed Isl1. Clonally derived CPCs also demonstrated this multipotent phenotype. By transcription profiling of CPCs, we found that mES cell-derived CPCs displayed a transcriptional signature that paralleled in vivo cardiac development. Additionally, these studies suggested the involvement of genes that we believe were previously unknown to play a role in cardiac development. Taken together, our data demonstrate that ES cell-derived CPCs comprise a multipotent precursor population capable of populating multiple cardiac lineages and suggest that ES cell differentiation is a valid model for studying development of multiple cardiac-fated tissues.

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.

A liquid chromatographic/mass spectrometric method to quantitate atorvastatin (AT) and its active metabolites ortho-hydroxy (o-AT) and para-hydroxy (p-AT) atorvastatin in human, dog, and rat plasma was validated. The method consisted of washing plasma samples at high pH with diethyl ether and subsequently extracting the analytes and two internal standards, [d5]-atorvastatin ([d5]-AT) and [d5]-ortho-hydroxy atorvastatin ([d5]-o-AT), from acidified plasma by using diethyl ether. The ether layer was evaporated to dryness and the residue reconstituted in ammonium acetate (20 mM, pH 4.0)-acetonitrile-isopropanol (60:40:1, v/v/v). Chromatographic separation of analytes was achieved by using a YMC J'Sphere H80 (C-18) 150 x 2 mm, 4 microns particle size, column with a mobile phase consisting of acetonitrile-0.1% acetic acid, (70:30, v/v). Analytes were detected by using MS/MS. Sample introduction and ionization was by electrospray ionization in the positive ion mode. The method proved suitable for routine quantitation of AT, o-AT, and p-AT over the concentration range of 0.250 to 25.0 ng/mL. Approximate retention time ranges of p-AT, o-AT, [d5]-o-AT, AT, and [d5]-AT were 2.27 +/- 0.21, 3.36 +/- 0.23, 3.54 +/- 0.46, 4.12 +/- 0.61, and 4.65 +/- 0.65 min, respectively. No peaks interfering with quantitation were observed throughout the validation processes. Mean recoveries of AT, o-AT, and p-AT from plasma ranged 100%-107%, 70.6%-104%, and 47.6%-85.6%, respectively. Mean recoveries of the [d5]-AT and [d5]-o-AT internal standards ranged 98.0%-99.9% and 97.3%, respectively. Interassay precision, based on the percent relative deviation for replicate quality controls for AT, o-AT, and p-AT, was < or = 7.19%, 8.28%, and 12.7%, respectively. Interassay accuracy for AT, o-AT, and p-AT was +/- 10.6%, 5.86%, and 15.8%, respectively. AT, o-AT, and p-AT in human, dog, and rat plasma quality controls were stable to three freeze-thaw cycles. AT, o-AT, and p-AT were stable frozen for 127, 30 and 270 days in human, dog, and rat plasma quality control samples, respectively. Human plasma quality control samples containing AT, o-AT, and p-AT were stable for at least 4 days at ambient room temperature and 37 degrees C. The lower limit of quantitation for all analytes was 0.250 ng/mL for a 1.0-mL sample aliquot.

This double-blind, placebo controlled, crossover study provides evidence of the efficacy of botulinum toxin B in the treatment of overactive bladder. Autonomic side effects were observed in 4 patients. The short duration of action will presumably limit the use to patients who have experienced tachyphylaxis with botulinum toxin A.

This study confirms that to estimate accurately the volume of the prostate using the prolate ellipsoid formula, the current methodology needs to be changed. The largest anteroposterior and transverse diameters may need to be measured in different transverse scan slices and the largest craniocaudal diameter in a sagittal scan away from the midline. If volume estimation is to be repeated then step planimetry is reliable and TRUS using the prolate ellipsoid formula is not.