Thrombolysis in stroke patients with elevated inflammatory markers.

OBJECTIVE To investigate the prognostic value of white blood cell count (WBC) on functional outcome, mortality and bleeding risk in stroke patients treated with intravenous thrombolysis (IVT). METHODS In this prospective multicenter study from the TRISP registry, we assessed the association between WBC on admission and 3-month poor outcome (modified Rankin Scale 3-6), mortality and occurrence of symptomatic intracranial hemorrhage (sICH; ECASS-II-criteria) in IVT-treated stroke patients. WBC was used as continuous and categorical variable distinguishing leukocytosis (WBC > 10 × 109/l) and leukopenia (WBC  10 mg/l) on outcomes. RESULTS Of 10,813 IVT-treated patients, 2527 had leukocytosis, 112 leukopenia and 8174 normal WBC. Increasing WBC (by 1 × 109/l) predicted poor outcome (ORadjusted 1.04[1.02-1.06]) but not mortality and sICH. Leukocytosis was independently associated with poor outcome (ORadjusted 1.48[1.29-1.69]) and mortality (ORadjusted 1.60[1.35-1.89]) but not with sICH (ORadjusted 1.17[0.94-1.45]). Leukopenia did not predict any outcome. In a subgroup, combined leukocytosis and elevated CRP had the strongest association with poor outcome (ORadjusted 2.26[1.76-2.91]) and mortality (ORadjusted 2.43[1.86-3.16]) when compared to combined normal WBC and CRP. CONCLUSION In IVT-treated patients, leukocytosis independently predicted poor functional outcome and death. Bleeding complications after IVT were not independently associated with leukocytosis

Rationale and study design of the prospective, longitudinal, observational cohort study “rISk strAtification in end-stage renal disease” (ISAR) study

Background: The ISAR study is a prospective, longitudinal, observational cohort study to improve the cardiovascular risk stratification in endstage renal disease (ESRD). The major goal is to characterize the cardiovascular phenotype of the study subjects, namely alterations in micro-and macrocirculation and to determine autonomic function. Methods/design: We intend to recruit 500 prevalent dialysis patients in 17 centers in Munich and the surrounding area. Baseline examinations include: (1) biochemistry, (2) 24-h Holter Electrocardiography (ECG) recordings, (3) 24-h ambulatory blood pressure measurement (ABPM), (4) 24 h pulse wave analysis (PWA) and pulse wave velocity (PWV), (5) retinal vessel analysis (RVA) and (6) neurocognitive testing. After 24 months biochemistry and determination of single PWA, single PWV and neurocognitive testing are repeated. Patients will be followed up to 6 years for (1) hospitalizations, (2) cardiovascular and (3) non-cardiovascular events and (4) cardiovascular and (5) all-cause mortality. Discussion/conclusion: We aim to create a complex dataset to answer questions about the insufficiently understood pathophysiology leading to excessively high cardiovascular and non-cardiovascular mortality in dialysis patients. Finally we hope to improve cardiovascular risk stratification in comparison to the use of classical and non-classical (dialysis-associated) risk factors and other models of risk stratification in ESRD patients by building a multivariable Cox-Regression model using a combination of the parameters measured in the study

Disease-specific molecular events in cortical multiple sclerosis lesions

Cortical lesions constitute an important part of multiple sclerosis pathology. Although inflammation appears to play a role in their formation, the mechanisms leading to demyelination and neurodegeneration are poorly understood. We aimed to identify some of these mechanisms by combining gene expression studies with neuropathological analysis. In our study, we showed that the combination of inflammation, plaque-like primary demyelination and neurodegeneration in the cortex is specific for multiple sclerosis and is not seen in other chronic inflammatory diseases mediated by CD8-positive T cells (Rasmussen’s encephalitis), B cells (B cell lymphoma) or complex chronic inflammation (tuberculous meningitis, luetic meningitis or chronic purulent meningitis). In addition, we performed genome-wide microarray analysis comparing micro-dissected active cortical multiple sclerosis lesions with those of tuberculous meningitis (inflammatory control), Alzheimer’s disease (neurodegenerative control) and with cortices of age-matched controls. More than 80% of the identified multiple sclerosis-specific genes were related to T cell-mediated inflammation, microglia activation, oxidative injury, DNA damage and repair, remyelination and regenerative processes. Finally, we confirmed by immunohistochemistry that oxidative damage in cortical multiple sclerosis lesions is associated with oligodendrocyte and neuronal injury, the latter also affecting axons and dendrites. Our study provides new insights into the complex mechanisms of neurodegeneration and regeneration in the cortex of patients with multiple sclerosis

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Physically motivated water modeling in control-oriented polymer electrolyte membrane fuel cell stack models

Polymer electrolyte membrane fuel cells (PEMFCs) are prone to membrane dehydration and liquid water flooding, negatively impacting their performance and lifetime. Therefore, PEMFCs require appropriate water management, which makes accurate water modeling indispensable. Unfortunately, available control-oriented models only replicate individual water-related aspects or use oversimplistic approximations. This paper resolves this challenge by proposing, for the first time, a control-oriented PEMFC stack model focusing on physically motivated water modeling, which covers phase change, liquid water removal, membrane water uptake, and water flooding effects on the electrochemical reaction. Parametrizing the resulting model with measurement data yielded the fitted model. The parameterized model delivers valuable insight into the water mechanisms, which were thoroughly analyzed. In summary, the proposed model enables the derivation of advanced control strategies for efficient water management and mitigation of the degradation phenomena of PEMFCs. Additionally, the model provides the required accuracy for control applications while maintaining the necessary computational efficiency

Methodology for efficient parametrisation of electrochemical PEMFC model for virtual observers

Determination of the optimal design of experiments that enables efficient parametrisation of fuel cell (FC) model with a minimum parametrisation data-set is one of the key prerequisites for minimizing costs and effort of the parametrisation procedure. To efficiently tackle this challenge, the paper present an innovative methodology based on the electrochemical FC model, parameter sensitivity analysis and application of D-optimal design plan. Relying on this consistent methodological basis the paper answers fundamental questions: a) on a minimum required data-set to optimally parametrise the FC model and b) on the impact of reduced space of operational points on identifiability of individual calibration parameters. Results reveal that application of D-optimal DoE enables enhancement of calibration parameters information resulting in up to order of magnitude lower relative standard errors on smaller data-sets. In addition, it was shown that increased information and thus identifiability, inherently leads to improved robustness of the FC electrochemical model

Reduced-dimensionality nonlinear distributed-parameter observer for fuel cell systems

To ensure reliable and efficient operation of fuel cell systems, it is important to monitor them online. However, placing sensors inside the fuel cell is often challenging, so virtual sensing using an efficient state observer is used in this study. Detecting local internal phenomena, such as reactants’ starvation, membrane dryout/flooding, and nitrogen accumulation, requires knowledge of the spatial distribution of internal states. Lumped-parameter models are not suitable for this, as they use a single variable to describe parameters such as hydrogen concentration. Instead, a high-order distributed-parameter fuel cell model is used to predict the spatial profiles of various internal states. An observer algorithm is employed to correct the predicted quantities using a few measurements taken at the system boundary. This update step only considers dominant dynamics from a reduced model to adjust all system states accordingly, making it computationally efficient and robust. The observer algorithm’s performance was verified against a high-fidelity model through detailed simulations