Inhibition of CD26/DPP-IV enhances donor muscle cell engraftment and stimulates sustained donor cell proliferation

<p>Abstract</p> <p>Background</p> <p>Transplantation of myogenic stem cells possesses great potential for long-term repair of dystrophic muscle. In murine-to-murine transplantation experiments, CXCR4 expression marks a population of adult murine satellite cells with robust engraftment potential in <it>mdx </it>mice, and CXCR4-positive murine muscle-derived SP cells home more effectively to dystrophic muscle after intra-arterial delivery in <it>mdx^5cv</it>mice. Together, these data suggest that CXCR4 plays an important role in donor cell engraftment. Therefore, we sought to translate these results to a clinically relevant canine-to-canine allogeneic transplant model for Duchenne muscular dystrophy (DMD) and determine if CXCR4 is important for donor cell engraftment.</p> <p>Methods</p> <p>In this study, we used a canine-to-murine xenotransplantation model to quantitatively compare canine muscle cell engraftment, and test the most effective cell population and modulating factor in a canine model of DMD using allogeneic transplantation experiments.</p> <p>Results</p> <p>We show that CXCR4 expressing cells are important for donor muscle cell engraftment, yet FACS sorted CXCR4-positive cells display decreased engraftment efficiency. However, diprotin A, a positive modulator of CXCR4-SDF-1 binding, significantly enhanced engraftment and stimulated sustained proliferation of donor cells <it>in vivo</it>. Furthermore, the canine-to-murine xenotransplantation model accurately predicted results in canine-to-canine muscle cell transplantation.</p> <p>Conclusions</p> <p>Therefore, these results establish the efficacy of diprotin A in stimulating muscle cell engraftment, and highlight the pre-clinical utility of a xenotransplantation model in assessing the relative efficacy of muscle stem cell populations.</p

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Test case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the Fault Exposing Potential (FEP) parameters associated to the test cases. FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a cost-effective FEP approximation for prioritizing Modified Condition Decision Coverage (MCDC) test cases. A strict negative correlation between the FEP of a MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing

Select Atrophied Regions in Alzheimer disease (SARA): An improved volumetric model for identifying Alzheimer disease dementia

INTRODUCTION: Volumetric biomarkers for Alzheimer disease (AD) are attractive due to their wide availability and ease of administration, but have traditionally shown lower diagnostic accuracy than measures of neuropathological contributors to AD. Our purpose was to optimize the diagnostic specificity of structural MRIs for AD using quantitative, data-driven techniques. METHODS: This retrospective study assembled several non-overlapping cohorts (total n = 1287) with publicly available data and clinical patients from Barnes-Jewish Hospital (data gathered 1990-2018). The Normal Aging Cohort (n = 383) contained amyloid biomarker negative, cognitively normal (CN) participants, and provided a basis for determining age-related atrophy in other cohorts. The Training (n = 216) and Test (n = 109) Cohorts contained participants with symptomatic AD and CN controls. Classification models were developed in the Training Cohort and compared in the Test Cohort using the receiver operating characteristics areas under curve (AUCs). Additional model comparisons were done in the Clinical Cohort (n = 579), which contained patients who were diagnosed with dementia due to various etiologies in a tertiary care outpatient memory clinic. RESULTS: While the Normal Aging Cohort showed regional age-related atrophy, classification models were not improved by including age as a predictor or by using volumetrics adjusted for age-related atrophy. The optimal model used multiple regions (hippocampal volume, inferior lateral ventricle volume, amygdala volume, entorhinal thickness, and inferior parietal thickness) and was able to separate AD and CN controls in the Test Cohort with an AUC of 0.961. In the Clinical Cohort, this model separated AD from non-AD diagnoses with an AUC 0.820, an incrementally greater separation of the cohort than by hippocampal volume alone (AUC of 0.801, p = 0.06). Greatest separation was seen for AD vs. frontotemporal dementia and for AD vs. non-neurodegenerative diagnoses. CONCLUSIONS: Volumetric biomarkers distinguished individuals with symptomatic AD from CN controls and other dementia types but were not improved by controlling for normal aging

Facioscapulohumeral Dystrophy: Incomplete Suppression of a Retrotransposed Gene

Each unit of the D4Z4 macrosatellite repeat contains a retrotransposed gene encoding the DUX4 double-homeobox transcription factor. Facioscapulohumeral dystrophy (FSHD) is caused by deletion of a subset of the D4Z4 units in the subtelomeric region of chromosome 4. Although it has been reported that the deletion of D4Z4 units induces the pathological expression of DUX4 mRNA, the association of DUX4 mRNA expression with FSHD has not been rigorously investigated, nor has any human tissue been identified that normally expresses DUX4 mRNA or protein. We show that FSHD muscle expresses a different splice form of DUX4 mRNA compared to control muscle. Control muscle produces low amounts of a splice form of DUX4 encoding only the amino-terminal portion of DUX4. FSHD muscle produces low amounts of a DUX4 mRNA that encodes the full-length DUX4 protein. The low abundance of full-length DUX4 mRNA in FSHD muscle cells represents a small subset of nuclei producing a relatively high abundance of DUX4 mRNA and protein. In contrast to control skeletal muscle and most other somatic tissues, full-length DUX4 transcript and protein is expressed at relatively abundant levels in human testis, most likely in the germ-line cells. Induced pluripotent (iPS) cells also express full-length DUX4 and differentiation of control iPS cells to embryoid bodies suppresses expression of full-length DUX4, whereas expression of full-length DUX4 persists in differentiated FSHD iPS cells. Together, these findings indicate that full-length DUX4 is normally expressed at specific developmental stages and is suppressed in most somatic tissues. The contraction of the D4Z4 repeat in FSHD results in a less efficient suppression of the full-length DUX4 mRNA in skeletal muscle cells. Therefore, FSHD represents the first human disease to be associated with the incomplete developmental silencing of a retrogene array normally expressed early in development

Comparison of \nu\mu-Ar multiplicity distributions observed by MicroBooNE to GENIE model predictions

We measure a large set of observables in inclusive charged current muon neutrino scattering on argon with the MicroBooNE liquid argon time projection chamber operating at Fermilab. We evaluate three neutrino interaction models based on the widely used GENIE event generator using these observables. The measurement uses a data set consisting of neutrino interactions with a final state muon candidate fully contained within the MicroBooNE detector. These data were collected in 2016 with the Fermilab Booster Neutrino Beam, which has an average neutrino energy of 800 MeV, using an exposure corresponding to 5E19 protons-on-target. The analysis employs fully automatic event selection and charged particle track reconstruction and uses a data-driven technique to separate neutrino interactions from cosmic ray background events. We find that GENIE models consistently describe the shapes of a large number of kinematic distributions for fixed observed multiplicity.Comment: 31 pages, 39 figures, 10 table

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article