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Search Results: 1 - 10 of 2718 matches for " Hjalmarsson Clara "
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Comparative evaluation of treatment with low-dose aspirin plus dipyridamole versus aspirin only in patients with acute ischaemic stroke
Arnarsdottir Lola,Hjalmarsson Clara,Bokemark Lena,Andersson Bj?rn
BMC Neurology , 2012, DOI: 10.1186/1471-2377-12-67
Abstract: Background Previous studies have suggested that pre-stroke treatment with low-dose aspirin (A) could reduce the severity of acute ischaemic stroke, but less is known on the effect of pre-stroke treatment with a combination of aspirin and dipyridamole (A + D) and post-stroke effects of these drugs. The aim of the present study was to evaluate the effect of this drug combination on acute and long-term prognosis of ischaemic stroke. Methods Patients without atrial fibrillation admitted to the stroke unit with acute ischaemic stroke (n = 554) or TIA (n = 108) were studied during acute hospital care and up to 12 months after discharge from hospital. Results Prior to acute stroke 62 patients were treated with A + D while 247 patients were treated with A only. No beneficial effects of the combination A + D compared to A only were noted on stroke severity and/or acute in-hospital mortality. However, survival analysis by Cox-proportional hazard model demonstrated lower 12-months all-cause mortality in patients discharged with A + D (n = 275) compared with patients on A only (HR, 0.52; CI, 0.32-0.86; p = 0.011; n = 262) after adjusting for age, baseline NIHSS, previous stroke, previous myocardial infarction and type 2 diabetes. We also noted a tendency towards lower all-cause mortality at 3 months with use of A + D, but this was not statistically significant (p = 0.12). Conclusions Pre-stroke treatment with a combination of low-dose A + D does not reduce the severity of acute stroke, nor does it reduce the acute in-hospital mortality. However, treatment with A + D at discharge from hospital is seemingly associated with lower long-term mortality compared with A only, contrary to the results from previous randomised studies. However, our results must be interpreted with extreme caution considering the non-randomised study design.
The Role of Prestroke Glycemic Control on Severity and Outcome of Acute Ischemic Stroke
Clara Hjalmarsson,Karin Manhem,Lena Bokemark,Bj?rn Andersson
Stroke Research and Treatment , 2014, DOI: 10.1155/2014/694569
Abstract: Background/Aim. Relatively few studies have investigated the association of prestroke glycemic control and clinical outcome in acute ischemic stroke (IS) patients, regardless of presence of diabetes mellitus (DM). The aim of this study was to investigate the importance of prestroke glycemic control on survival, stroke severity, and functional outcome of patients with acute IS. Methods. We performed a retrospective survival analysis of 501 patients with IS admitted to Sahlgrenska University Hospital from February 15, 2005, through May 31, 2009. The outcomes of interest were acute and long-term survival; the stroke severity (NIHSS) and the functional outcome, mRS, at 12 months. Results. HbA1c was a good predictor of acute (HR 1.45; CI, 1.09 to 1.93, ) and long-term mortality (HR 1.29; CI 1.03 to 1.62; ). Furthermore, HbA1c >6% was significantly correlated with acute stroke severity (OR 1.29; CI 1.01 to 1.67; ) and predicted worse functional outcome at 12 months (OR 2.68; CI 1.14 to 6.03; ). Conclusions. Our study suggests that poor glycemic control (baseline HbA1c) prior to IS is an independent risk factor for poor survival and a marker for increased stroke severity and unfavorable long-term functional outcome. 1. Introduction Hyperglycemia (HG) in relation to acute IS is common both in patients with and in patients without a diagnosis of DM, and it has been suggested to worsen survival. However, recent results from several clinical studies indicate that particularly patients with stroke and stress HG, but not diabetes, have increased mortality [1–3]. On the contrary, older data by Woo et al. [4] found that patients with acute IS and similar glucose concentrations had similar outcome regardless of whether they had diabetes or not. According to a review published by Capes et al. [5], acute HG predicted increased risk of in-hospital mortality after ischemic stroke (IS) in nondiabetic patients and increased risk of poor functional recovery in nondiabetic stroke survivors. The recent results of Nardi et al. [2] are also in line with this conclusion. In a study published in 2012, Hu et al. [1] evaluated the effects of HG and prestroke glycemic control, as measured by HbA1c, on all-cause and cardiovascular mortality among 1277 IS patients and found a significant association between initial glucose level and mortality in nondiabetic patients. Surprisingly, they also found that DM patients with HbA1c <7.0% had a higher incidence rate of all-cause and cardiovascular death than those with HbA1c ≥7%. Contradictory data have been published by Kamouchi et al. in 2011,
Learning about Vegetarian Diets in School: Curricular Representations of Food and Nutrients in Elementary Health Education  [PDF]
Clara Hanson
Advances in Applied Sociology (AASoci) , 2012, DOI: 10.4236/aasoci.2012.21010
Abstract: This paper examines the way non-meat and plant based diets are discussed in four elementary curricula. The author used an open coding technique of grounded theory to understand the way food, nutrition and vegetarianism was discussed. The curricula relied heavily upon the USDA Food Pyramid and a related concept of “balance” for nutritional information. The curricula also discussed nutrition in terms of food and food groups, rather than in terms of nutrients. Although some of the curricula included information about the benefits of vegetarian diets, the high level of use of the Food Pyramid often overwhelmed the low level of information about vegetarianism.
Re-envisioning nursing education and practice in Nigeria for the 21st century  [PDF]
Clara Agbedia
Open Journal of Nursing (OJN) , 2012, DOI: 10.4236/ojn.2012.23035
Abstract: The paper explores the current situation and future development of nursing education and practice in Nigeria and their influence on health care. As the role of the nurse continues to expand, Nigerian nurses must be skillful in effectively using evidence-based and clinically relevant information to facilitate the best possible nursing care. Major issues and challenges in this regard are discussed with some recommendations on the way forward.
Bayesian kernel-based system identification with quantized output data
Giulio Bottegal,Gianluigi Pillonetto,H?kan Hjalmarsson
Statistics , 2015,
Abstract: In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo (MCMC) methods to provide an estimate of the system. In particular, we show how to design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods when employed in identification of systems with quantized data.
Postpartum behavioral profiles in Wistar rats following maternal separation - altered exploration and risk-assessment behavior in MS15 dams
Loudin Daoura,My Hjalmarsson,Sadia Oreland,Ingrid Nylander,Erika Roman
Frontiers in Behavioral Neuroscience , 2010, DOI: 10.3389/fnbeh.2010.00037
Abstract: The rodent maternal separation (MS) model is frequently used to investigate the impact of early environmental factors on adult neurobiology and behavior. The majority of MS studies assess effects in the offspring and few address the consequences of repeated pup removal in the dam. Such studies are of interest since alterations detected in offspring subjected to MS may, at least in part, be mediated by variations in maternal behavior and the amount of maternal care provided by the dam. The aim of this study was to investigate how daily short (15 min; MS15) and prolonged (360 min; MS360) periods of MS affects the dam by examining postpartum behavioral profiles using the multivariate concentric square field? (MCSF) test. The dams were tested on postpartum days 24–25, i.e., just after the end of the separation period and weaning. The results reveal a lower exploratory drive and lower risk-assessment behavior in MS15 dams relative to MS360 or animal facility reared dams. The present results contrast some of the previously reported findings and provide new information about early post-weaning behavioral characteristics in a multivariate setting. Plausible explanations for the results are provided including a discussion how the present results fit into the maternal mediation hypothesis.
Piecewise Toeplitz Matrices-based Sensing for Rank Minimization
Kezhi Li,Cristian R. Rojas,Saikat Chatterjee,H\rakan Hjalmarsson
Mathematics , 2014,
Abstract: This paper proposes a set of piecewise Toeplitz matrices as the linear mapping/sensing operator $\mathcal{A}: \mathbf{R}^{n_1 \times n_2} \rightarrow \mathbf{R}^M$ for recovering low rank matrices from few measurements. We prove that such operators efficiently encode the information so there exists a unique reconstruction matrix under mild assumptions. This work provides a significant extension of the compressed sensing and rank minimization theory, and it achieves a tradeoff between reducing the memory required for storing the sampling operator from $\mathcal{O}(n_1n_2M)$ to $\mathcal{O}(\max(n_1,n_2)M)$ but at the expense of increasing the number of measurements by $r$. Simulation results show that the proposed operator can recover low rank matrices efficiently with a reconstruction performance close to the cases of using random unstructured operators.
Optimal input design for non-linear dynamic systems: a graph theory approach
Patricio E. Valenzuela,Cristian R. Rojas,H?kan Hjalmarsson
Mathematics , 2013,
Abstract: In this article a new algorithm for the design of stationary input sequences for system identification is presented. The stationary input signal is generated by optimizing an approximation of a scalar function of the information matrix, based on stationary input sequences generated from prime cycles, which describe the set of finite Markov chains of a given order. This method can be used for solving input design problems for nonlinear systems. In particular it can handle amplitude constraints on the input. Numerical examples show that the new algorithm is computationally attractive and that is consistent with previously reported results.
A Note on the SPICE Method
Cristian R. Rojas,Dimitrios Katselis,H?kan Hjalmarsson
Computer Science , 2012, DOI: 10.1109/TSP.2013.2272291
Abstract: In this article, we analyze the SPICE method developed in [1], and establish its connections with other standard sparse estimation methods such as the Lasso and the LAD-Lasso. This result positions SPICE as a computationally efficient technique for the calculation of Lasso-type estimators. Conversely, this connection is very useful for establishing the asymptotic properties of SPICE under several problem scenarios and for suggesting suitable modifications in cases where the naive version of SPICE would not work.
A kernel-based approach to Hammerstein system identification
Riccardo Sven Risuleo,Giulio Bottegal,H?kan Hjalmarsson
Computer Science , 2014,
Abstract: In this paper, we propose a novel algorithm for the identification of Hammerstein systems. Adopting a Bayesian approach, we model the impulse response of the unknown linear dynamic system as a realization of a zero-mean Gaussian process. The covariance matrix (or kernel) of this process is given by the recently introduced stable-spline kernel, which encodes information on the stability and regularity of the impulse response. The static non-linearity of the model is identified using an Empirical Bayes approach, i.e. by maximizing the output marginal likelihood, which is obtained by integrating out the unknown impulse response. The related optimization problem is solved adopting a novel iterative scheme based on the Expectation-Maximization (EM) method, where each iteration consists in a simple sequence of update rules. Numerical experiments show that the proposed method compares favorably with a standard algorithm for Hammerstein system identification.
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