2,204 research outputs found

    Plasma NT pro-BNP, hs-CRP and big-ET levels at admission as prognostic markers of survival in hospitalized patients with dilated cardiomyopathy: a single-center cohort study

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    BACKGROUND: Circulating N-terminal pro-B-type natriuretic peptide (NT pro-BNP), high- sensitivity C-reactive protein (hs-CRP) and big endothelin (big-ET) have been shown to be increased in heart failure and to contribute to both hemodynamic deterioration and cardiovascular remodeling. Here, we examined the prognostic value of the three neurohormones at admission in a population of hospitalized patients with dilated cardiomyopathy (DCM). METHODS AND RESULTS: This cohort study was undertaken in 622 hospitalized patients with DCM in Fuwai Hospital from January 2005 to September 2011 (female 26.5%, 51.4 ± 14.6 years old). Standard demographics, echocardiography and routine blood samples were obtained shortly after admission. NT pro-BNP, hs-CRP and big-ET were measured, and their concentrations in relation to all-cause mortality were assessed through a mean follow-up of 2.6 ± 1.6 years. Kaplan-Meier curves showed that the all-cause mortality rates were higher in patients with NT pro-BNP > 2247 pmol/L compared to patients with NT pro-BNP < 2247 pmol/L (11.9% vs 34.8%, log-rank χ(2) = 35.588, P < 0.001), in patients with hs-CRP > 3.90 mg/L compared to patients with hs-CRP < 3.90 mg/L (12.8% vs 33.6%, log-rank χ(2) = 39.662, P < 0.001) and in patients with big-ET > 0.95 pmol/L compared to patients with big-ET <0.95 pmol/L (12.5% vs 31.0%, log-rank χ(2) = 17.890, P < 0.001). High circulating concentrations of NT pro-BNP (HR 2.217, 95% CI 1.015-4.846, P = 0.046) and hs-CRP (HR 1.922, 95% CI 1.236-2.988, P = 0.004), but not big-ET, in addition to left atrial diameter and fasting blood glucose, were independent predictors of the outcome defined as all-cause mortality. CONCLUSIONS: In a large population of patients with DCM, the circulating concentrations of NT pro-BNP and hs-CRP, but not big-ET, were independent markers of all-cause mortality

    Aqua­(dicyanamido-κN 1)(2,9-dimethyl-1,10-phenanthroline-κ2 N,N′)(nitrato-κ2 O,O′)cobalt(II)–2,9-dimethyl-1,10-phenanthroline–water (2/1/2)

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    In the title compound, 2[Co(C2N3)(NO3)(C14H12N2)(H2O)]·C14H12N2·2H2O, the CoII ion is coordinated by a bidentate 2,9-dimethyl-1,10-phenanthroline (dmphen) ligand, a bidentate nitrate anion, a water mol­ecule and a monodentate dicyan­amide group in a distorted octa­hedral geometry. One uncoordinated dmphen mol­ecule is situated on a crystallographic twofold axis and the asymmetric unit is completed by one water mol­ecule. In the crystal, mol­ecules form a one-dimensional framework in the [001] direction through O—H⋯N and O—H⋯O hydrogen bonds. The crystal packing is further stabilized by π–π stacking inter­actions between the dmphen rings of neighboring mol­ecules, with a centroid–centroid separation of 3.5641 (8) Å and a partially overlapped arrangement of parallel dmphen rings with a distance of 3.407 (2) Å

    Neonatal Maternal Deprivation Response and Developmental Changes in Gene Expression Revealed by Hypothalamic Gene Expression Profiling in Mice

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    Neonatal feeding problems are observed in several genetic diseases including Prader-Willi syndrome (PWS). Later in life, individuals with PWS develop hyperphagia and obesity due to lack of appetite control. We hypothesized that failure to thrive in infancy and later-onset hyperphagia are related and could be due to a defect in the hypothalamus. In this study, we performed gene expression microarray analysis of the hypothalamic response to maternal deprivation in neonatal wild-type and Snord116del mice, a mouse model for PWS in which a cluster of imprinted C/D box snoRNAs is deleted. The neonatal starvation response in both strains was dramatically different from that reported in adult rodents. Genes that are affected by adult starvation showed no expression change in the hypothalamus of 5 day-old pups after 6 hours of maternal deprivation. Unlike in adult rodents, expression levels of Nanos2 and Pdk4 were increased, and those of Pgpep1, Ndp, Brms1l, Mett10d, and Snx1 were decreased after neonatal deprivation. In addition, we compared hypothalamic gene expression profiles at postnatal days 5 and 13 and observed significant developmental changes. Notably, the gene expression profiles of Snord116del deletion mice and wild-type littermates were very similar at all time points and conditions, arguing against a role of Snord116 in feeding regulation in the neonatal period

    MATH5 controls the acquisition of multiple retinal cell fates

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    Math5-null mutation results in the loss of retinal ganglion cells (RGCs) and in a concurrent increase of amacrine and cone cells. However, it remains unclear whether there is a cell fate switch of Math5-lineage cells in the absence of Math5 and whether MATH5 cell-autonomously regulates the differentiation of the above retinal neurons. Here, we performed a lineage analysis of Math5-expressing cells in developing mouse retinas using a conditional GFP reporter (Z/EG) activated by a Math5-Cre knock-in allele. We show that during normal retinogenesis, Math5-lineage cells mostly develop into RGCs, horizontal cells, cone photoreceptors, rod photoreceptors, and amacrine cells. Interestingly, amacrine cells of Math5-lineage cells are predominately of GABAergic, cholinergic, and A2 subtypes, indicating that Math5 plays a role in amacrine subtype specification. In the absence of Math5, more Math5-lineage cells undergo cell fate conversion from RGCs to the above retinal cell subtypes, and occasionally to cone-bipolar cells and Müller cells. This change in cell fate choices is accompanied by an up-regulation of NEUROD1, RXRγ and BHLHB5, the transcription factors essential for the differentiation of retinal cells other than RGCs. Additionally, loss of Math5 causes the failure of early progenitors to exit cell cycle and leads to a significant increase of Math5-lineage cells remaining in cell cycle. Collectively, these data suggest that Math5 regulates the generation of multiple retinal cell types via different mechanisms during retinogenesis
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