Troponin Elevations After Electroconvulsive Therapy: The Need for Caution

BACKGROUND: Electroconvulsive therapy is used to treat patients with severe or resistant depression. Troponin elevations are associated with an adverse prognosis, and it is well known that central nervous system insults can cause biochemical evidence of cardiac injury. No study previously has studied this with electroconvulsive therapy. METHODS: Patients scheduled for electroconvulsive therapy were enrolled. Clinical information, an electrocardiogram, and a baseline sample for cardiac troponin I and T (cTnI and cTnT) were obtained. Electroconvulsive therapy was done with standard techniques. Subsequently, electrocardiograms and additional samples were obtained. cTnT was measured with the Roche assay and cTnI with the Dade Stratus equipment. Values above the 99th percentile were considered abnormal. RESULTS: Seventy patients completed the study. Four patients had elevated levels of cTn before treatment. In 3 patients, the elevations persisted. Four additional patients developed elevated cTn levels during electroconvulsive therapy. Two of the patients with cTn elevations died. No other events occurred during follow-up. CONCLUSIONS: Elevations of cTn occurred in 11.5% of patients treated with electroconvulsive therapy. Some of the elevations preceded therapy and some occurred during treatment. Given the adverse prognostic importance of cTn elevations in general, in addition to additional studies, an increased degree of medical scrutiny may be appropriate for this group of patients and for those receiving electroconvulsive therapy

Infrared Thermography and Ultrasonography to Indirectly Monitor the Influence of Liner Type and Overmilking on Teat Tissue Recovery

Eight Danish Holstein cows were milked with a 1-mm thick specially designed soft liner on their right rear teat and a standard liner mounted under extra high tension on their left rear teat. Four of the animals were overmilked for 5 min. Rear teats were subjected to ultrasound examination on the first day and to infrared thermography on the second day. Teats were submersed in ethanol 20 min post-milking on the second day. Ultrasonography measurements showed that teat canal length increased by 30–41% during milking. Twenty minutes after milking, teats milked with modified standard liners still had elongated teat canals while teats milked with the soft liner were normalized. Overmilking tended to increase teat wall thickness. Approximately 80% of variability in teat canal length, from before teat preparation to after milking, could be explained by changes during teat preparation. Thermography indicated a general drop in teat temperature during teat preparation. Teat temperature increased during milking and continued to increase until the ethanol challenge induced a significant drop. Temperatures approached pre-challenge rather than pre-milking temperatures within 10 minutes after challenge. Teat temperatures were dependent on type of liner. Mid-teat temperatures post-challenge relative to pre-teat preparation were dependent on overmilking. Thermography and ultrasound were considered useful methods to indirectly and non invasively evaluate teat tissue integrity

Real World Bayesian Optimization Using Robots to Clean Liquid Spills

Developing robots that can contribute to cleaning could have a significant impact on the lives of many. Cleaning wet liquid spills is a particularly challenging task for a robotic system, and has several high impact applications. This is a hard task to physically model due to the complex interactions between cleaning materials and the surface. As such, to the authors' knowledge there has been no prior work in this area. A new method for finding optimal control parameters for the cleaning of liquid spills is required by developing a robotic system which iteratively learns to clean through physical experimentation. The robot creates a liquid spill, cleans and assesses performance and uses Bayesian optimization to find the optimal control parameters for a given size of liquid spill. The automation process enabled the experiment to be repeated more than 400 times over 20 hours to find the optimal wiping control parameters for many different conditions. We then show that these solutions can be extrapolated for different spill conditions. The optimized control parameters showed reliable and accurate performances, which in some cases, outperformed humans at the same task.This work was supported by BEKO PLC and Symphony Kitchens. We are especially thankful for the valuable inputs from Dr Graham Anderson and Dr Natasha Conway

Structural identification of oxidized acyl-phosphatidylcholines that induce platelet activation

Oxidation of low-density lipoprotein (LDL) generates proinflammatory and prothrombotic mediators that may play a crucial role in cardiovascular and inflammatory diseases. In order to study platelet-activating components of oxidized LDL 1-stearoyl-2-arachidonoyl-sn-glycero-3- phosphocholine, a representative of the major phospholipid species in LDL, the 1-acyl-phosphatidylcholines (PC), was oxidized by CuCl2 and H2O2. After separation by high-performance liquid chromatography, three compounds were detected which induced platelet shape change at low micromolar concentrations. Platelet activation by these compounds was distinct from the pathways stimulated by platelet-activating factor, lysophosphatidic acid, lyso-PC and thromboxane A(2), as evidenced by the use of specific receptor antagonists. Further analyses of the oxidized phospholipids by electrospray ionization mass spectrometry structurally identified them as 1-stearoyl-2-azelaoyl-sn-glycero-3-phosphocholine (m/z 694; SAzPC), 1-stearoyl-2-glutaroyl-snglycero-3- phosphocholine (m/z 638; SGPC), and 1-stearoyl-2-( 5-oxovaleroyl)-sn-glycero-3-phosphocholine (m/z 622; SOVPC). These observations demonstrate that novel 1-acyl-PC which had previously been found to stimulate interaction of monocytes with endothelial cells also induce platelet activation, a central step in acute thrombogenic and atherogenic processes. Copyright (C) 2005 S. Karger AG, Basel

Double differentiation in a cross-national comparison of populist political movements and online media uses in the United States and the Netherlands

In a context of highly visible and politically influential populist movements, this study considers the online self-representation of the Tea Party Patriots (TPP) in the United States and the Party for Freedom (PVV) in the Netherlands. A multi-methodological approach was adopted to compare the discursive manifestation of key populism concepts: leadership characteristics, adversary definition and mobilizing information. Analyses reconstruct and account for similarities and differences in discursive framing strategies of 'double differentiation' through which both movements attempt inclusion in and exclusion from the political establishment, and, in doing so, mobilize communities of support. Altogether, this study advances the understanding of what constitutes 'unmediated' content that is presented through user-generated media production, and how self-determined media spaces have facilitated shifts in populist media legitimation and political representation in two politically unique countries

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Graphene Oxide-Based Targeting of Extracellular Cathepsin D and Cathepsin L As A Novel Anti-Metastatic Enzyme Cancer Therapy

This is the final version. Available from the publisher via the DOI in this record.Overexpression and secretion of the enzymes cathepsin D (CathD) and cathepsin L (CathL) is associated with metastasis in several human cancers. As a superfamily, extracellularly, these proteins may act within the tumor microenvironment to drive cancer progression, proliferation, invasion and metastasis. Therefore, it is important to discover novel therapeutic treatment strategies to target CathD and CathL and potentially impede metastasis. Graphene oxide (GO) could form the basis of such a strategy by acting as an adsorbent for pro-metastatic enzymes. Here, we have conducted research into the potential of targeted anti-metastatic therapy using GO to adsorb these pro-tumorigenic enzymes. Binding of CathD/L to GO revealed that CathD/L were adsorbed onto the surface of GO through its cationic and hydrophilic residues. This work could provide a roadmap for the rational integration of CathD/L-targeting agents into clinical settings.Engineering and Physical Sciences Research Council (EPSRC)FORCE Cancer Charit

On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints

We are often interested in identifying the feasible subset of a decision space under multiple constraints to permit effective design exploration. If determining feasibility required computationally expensive simulations, the cost of exploration would be prohibitive. Bayesian search is data-efficient for such problems: starting from a small dataset, the central concept is to use Bayesian models of constraints with an acquisition function to locate promising solutions that may improve predictions of feasibility when the dataset is augmented. At the end of this sequential active learning approach with a limited number of expensive evaluations, the models can accurately predict the feasibility of any solution obviating the need for full simulations. In this paper, we propose a novel acquisition function that combines the probability that a solution lies at the boundary between feasible and infeasible spaces (representing exploitation) and the entropy in predictions (representing exploration). Experiments confirmed the efficacy of the proposed function