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      Β-blockers treatment of cardiac surgery patients enhances isolation and improves phenotype of cardiosphere-derived cells

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          Β-blockers (BB) are a primary treatment for chronic heart disease (CHD), resulting in prognostic and symptomatic benefits. Cardiac cell therapy represents a promising regenerative treatment and, for autologous cell therapy, the patients clinical history may correlate with the biology of resident progenitors and the quality of the final cell product. This study aimed at uncovering correlations between clinical records of biopsy-donor CHD patients undergoing cardiac surgery and the corresponding yield and phenotype of cardiospheres (CSs) and CS-derived cells (CDCs), which are a clinically relevant population for cell therapy, containing progenitors. We describe a statistically significant association between BB therapy and improved CSs yield and CDCs phenotype. We show that BB-CDCs have a reduced fibrotic-like CD90 + subpopulation, with reduced expression of collagen-I and increased expression of cardiac genes, compared to CDCs from non-BB donors. Moreover BB-CDCs had a distinctive microRNA expression profile, consistent with reduced fibrotic features (miR-21, miR-29a/b/c downregulation), and enhanced regenerative potential (miR-1, miR-133, miR-101 upregulation) compared to non-BB. In vitro adrenergic pharmacological treatments confirmed cytoprotective and anti-fibrotic effects of β1-blocker on CDCs. This study shows anti-fibrotic and pro-commitment effects of BB treatment on endogenous cardiac reparative cells, and suggests adjuvant roles of β-blockers in cell therapy applications.

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          Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets.

          Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data.
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            Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology.

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              MicroRNA-21 contributes to myocardial disease by stimulating MAP kinase signalling in fibroblasts.

              MicroRNAs comprise a broad class of small non-coding RNAs that control expression of complementary target messenger RNAs. Dysregulation of microRNAs by several mechanisms has been described in various disease states including cardiac disease. Whereas previous studies of cardiac disease have focused on microRNAs that are primarily expressed in cardiomyocytes, the role of microRNAs expressed in other cell types of the heart is unclear. Here we show that microRNA-21 (miR-21, also known as Mirn21) regulates the ERK-MAP kinase signalling pathway in cardiac fibroblasts, which has impacts on global cardiac structure and function. miR-21 levels are increased selectively in fibroblasts of the failing heart, augmenting ERK-MAP kinase activity through inhibition of sprouty homologue 1 (Spry1). This mechanism regulates fibroblast survival and growth factor secretion, apparently controlling the extent of interstitial fibrosis and cardiac hypertrophy. In vivo silencing of miR-21 by a specific antagomir in a mouse pressure-overload-induced disease model reduces cardiac ERK-MAP kinase activity, inhibits interstitial fibrosis and attenuates cardiac dysfunction. These findings reveal that microRNAs can contribute to myocardial disease by an effect in cardiac fibroblasts. Our results validate miR-21 as a disease target in heart failure and establish the therapeutic efficacy of microRNA therapeutic intervention in a cardiovascular disease setting.

                Author and article information

                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                14 November 2016
                : 6
                [1 ]Department of Medical Surgical Sciences and Biotechnology, “La Sapienza” University of Rome , Italy
                [2 ]Department of Cardiovascular, Respiratory, Nephrological, Anesthesiological, and Geriatric Sciences, “Umberto I” Hospital, “La Sapienza” University of Rome , Italy
                [3 ]Department of AngioCardioNeurology, IRCCS Neuromed , Pozzilli, Italy
                Author notes

                These authors contributed equally to this work.

                Copyright © 2016, The Author(s)

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