Survey on current practices for neurological prognostication after cardiac arrest.

PURPOSE: To investigate current practices and timing of neurological prognostication in comatose cardiac arrest patients. METHODS: An anonymous questionnaire was distributed to the 8000 members of the European Society of Intensive Care Medicine during September and October 2012. The survey had 27 questions divided into three categories: background data, clinical data, decision-making and consequences. RESULTS: A total of 1025 respondents (13%) answered the survey with complete forms in more than 90%. Twenty per cent of respondents practiced outside of Europe. Overall, 22% answered that they had national recommendations, with the highest percentage in the Netherlands (>80%). Eighty-nine per cent used induced hypothermia (32-34 °C) for comatose cardiac arrest patients, while 11% did not. Twenty per cent had separate prognostication protocols for hypothermia patients. Seventy-nine per cent recognized that neurological examination alone is not enough to predict outcome and a similar number (76%) used additional methods. Intermittent electroencephalography (EEG), brain computed tomography (CT) scan and evoked potentials (EP) were considered most useful. Poor prognosis was defined as cerebral performance category (CPC) 3-5 (58%) or CPC 4-5 (39%) or other (3%). When prognosis was considered poor, 73% would actively withdraw intensive care while 20% would not and 7% were uncertain. CONCLUSION: National recommendations for neurological prognostication after cardiac arrest are uncommon and only one physician out of five uses a separate protocol for hypothermia treated patients. A neurological examination alone was considered insufficient to predict outcome in comatose patients and most respondents advocated a multimodal approach: EEG, brain CT and EP were considered most useful. Uncertainty regarding neurological prognostication and decisions on level of care was substantial.

Long noncoding RNAs in cardiac development and ageing.

A large part of the mammalian genome is transcribed into noncoding RNAs. Long noncoding RNAs (lncRNAs) have emerged as critical epigenetic regulators of gene expression. Distinct molecular mechanisms allow lncRNAs either to activate or to repress gene expression, thereby participating in the regulation of cellular and tissue function. LncRNAs, therefore, have important roles in healthy and diseased hearts, and might be targets for therapeutic intervention. In this Review, we summarize the current knowledge of the roles of lncRNAs in cardiac development and ageing. After describing the definition and classification of lncRNAs, we present an overview of the mechanisms by which lncRNAs regulate gene expression. We discuss the multiple roles of lncRNAs in the heart, and focus on the regulation of embryonic stem cell differentiation, cardiac cell fate and development, and cardiac ageing. We emphasize the importance of chromatin remodelling in this regulation. Finally, we discuss the therapeutic and biomarker potential of lncRNAs.

Functional importance of cardiac enhancer-associated noncoding RNAs in heart development and disease.

The key information processing units within gene regulatory networks are enhancers. Enhancer activity is associated with the production of tissue-specific noncoding RNAs, yet the existence of such transcripts during cardiac development has not been established. Using an integrated genomic approach, we demonstrate that fetal cardiac enhancers generate long noncoding RNAs (lncRNAs) during cardiac differentiation and morphogenesis. Enhancer expression correlates with the emergence of active enhancer chromatin states, the initiation of RNA polymerase II at enhancer loci and expression of target genes. Orthologous human sequences are also transcribed in fetal human hearts and cardiac progenitor cells. Through a systematic bioinformatic analysis, we identified and characterized, for the first time, a catalog of lncRNAs that are expressed during embryonic stem cell differentiation into cardiomyocytes and associated with active cardiac enhancer sequences. RNA-sequencing demonstrates that many of these transcripts are polyadenylated, multi-exonic long noncoding RNAs. Moreover, knockdown of two enhancer-associated lncRNAs resulted in the specific downregulation of their predicted target genes. Interestingly, the reactivation of the fetal gene program, a hallmark of the stress response in the adult heart, is accompanied by increased expression of fetal cardiac enhancer transcripts. Altogether, these findings demonstrate that the activity of cardiac enhancers and expression of their target genes are associated with the production of enhancer-derived lncRNAs.

A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment.

OBJECTIVES: The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI). METHODS: Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects. RESULTS: Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics. CONCLUSION: Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features.