Trans-active factors controlling the IL-2 gene in adult human T-cell subsets

IL-2 secretion in total or subsets of PHA/PMA-stimulated PBMC-derived human T-lymphocytes was monitored and found to be largely due to CD4+CD8− cells. The presence and functional state of transcription factors (TF) was assessed by protein-DNA interaction assays and functional transactivation experiments in the Xenopts oocyte system, modulating IL-2 transcription by injection of proteins. The results reveal that CD4+CD8− cells contain both, functional silencer in their resting, and positive TF in their activated states while the CD4+CD8− group contains only non-functional positive TF. This demonstrates that the on/off switch of IL-2 transcription is based on the same mechanism in primary T-lymphocytes of mouse spleen and in peripheral human CD4+CD8− cells

EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest.

Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures. 94 comatose patients with EEG within 24h after CA were included. Clinical outcome was assessed at 3months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures×2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients. The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3-5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81. Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power. Quantitative methods might increase the prognostic yield of currently used multi-modal approaches

Frequency and evolution of sleep-wake disturbances after ischemic stroke: A 2-year prospective study of 437 patients.

OBJECTIVE In the absence of systematic and longitudinal data, this study prospectively assessed both frequency and evolution of sleep-wake disturbances (SWD) after stroke. METHODS In 437 consecutively recruited patients with ischemic stroke or transient ischemic attack (TIA), stroke characteristics and outcome were assessed within the 1st week and 3.2 ± 0.3 years (M±SD) after the acute event. SWD were assessed by interview and questionnaires at 1 and 3 months as well as 1 and 2 years after the acute event. Sleep disordered breathing (SDB) was assessed by respirography in the acute phase and repeated in one fifth of the participants 3 months and 1 year later. RESULTS Patients (63.8% male, 87% ischemic stroke and mean age 65.1 ± 13.0 years) presented with mean NIHSS-score of 3.5 ± 4.5 at admission. In the acute phase, respiratory event index was >15/h in 34% and >30/h in 15% of patients. Over the entire observation period, the frequencies of excessive daytime sleepiness (EDS), fatigue and insomnia varied between 10-14%, 22-28% and 20-28%, respectively. Mean insomnia and EDS scores decreased from acute to chronic stroke, whereas restless legs syndrome (RLS) percentages (6-9%) and mean fatigue scores remained similar. Mean self-reported sleep duration was enhanced at acute stroke (month 1: 07:54 ± 01:27h) and decreased at chronic stage (year 2: 07:43 ± 01:20h). CONCLUSIONS This study documents a high frequency of SDB, insomnia, fatigue and a prolonged sleep duration after stroke/TIA, which can persist for years. Considering the negative effects of SWD on physical, brain and mental health these data suggest the need for a systematic assessment and management of post-stroke SWD

NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience