Dermal reaction and bigeminal premature ventricular contractions due to neostigmine: a case report

<p>Abstract</p> <p>Introduction</p> <p>Neostigmine is a frequently used acetylcholinesterase inhibitor administered to reverse muscular relaxation caused by nondepolarizing neuromuscular relaxants in patients recovering from general anesthesia. Severe allergic reactions and urticaria are rarely reported following the use of neostigmine bromide, and never with methylsulfate-containing drugs. In this case, bigeminal premature ventricular contractions added to urticaria provides a warning about the possibility of a life-threatening situation.</p> <p>Case presentation</p> <p>We report the case of a 23-year-old Persian woman who presented with bigeminal premature ventricular contractions along with urticarial lesions on her arm and trunk as soon as she was administered neostigmine methylsulfate after undergoing a laparoscopy for ectopic pregnancy.</p> <p>Conclusion</p> <p>This case report could be of value not only for anesthesiologists who routinely use neostigmine but also for others who administer the pharmaceutical preparation in other situations. The report presents a rare case of drug reaction following neostigmine use. As a result, one should consider any drug a probable cause of drug reaction. The preparation of resuscitative facilities, therefore, is necessary prior to the prescription of the medication.</p

An AUC-based Permutation Variable Importance Measure for Random Forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html

Magnesium hydroxide addition reduces aqueous carbon dioxide in wastewater discharged to the ocean

Ocean alkalinity enhancement (OAE) reduces the concentration of dissolved carbon dioxide (CO2) in seawater, leading to atmospheric carbon dioxide removal (CDR). Here we report laboratory experiments and a field-trial of alkalinity enhancement through addition of magnesium hydroxide to wastewater and its subsequent discharge to the coastal ocean. In wastewater, a 10% increase of average alkalinity (+0.56 mmol/kg) led to a 74% reduction in aqueous CO2 (−0.41 mmol/kg) and pH increase of 0.4 units to 7.78 (efficiency 0.73 molCO2/mol alkalinity). The alkalinization signal was limited to within a few metres of the ocean discharge, evident as 27.2 μatm reduction in CO2 partial pressure and 0.017 unit pH increase, and was consistent with rapid dilution of the alkali-treated wastewater. While this proof of concept field trial did not achieve CDR due to its small scale, it demonstrated the potential of magnesium hydroxide addition to wastewater as a CDR solution

Roc632: An overview

The present paper aims to analyze and explore the ROC632 package, specifying its main characteristics and functions. More specifically, the goal of this study is the evaluation of the effectiveness of the package and its strengths and weaknesses. This package was created in order to overcome the lack of information concerning incomplete time-to-event data, adapting the 0.632+ bootstrap estimator for the evaluation of time dependent ROC curves. By applying this package to a specific dataset (DLBCLpatients), it becomes possible to assess tangible data, determining if it is able to analyze complete and incomplete data efficiently and without bias.(undefined)info:eu-repo/semantics/publishedVersio

Economic tools to promote transparency and comparability in the Paris Agreement

The Paris Agreement culminates a six-year transition towards an international climate policy architecture based on parties submitting national pledges every five years1. An important policy task will be to assess and compare these contributions2, 3. We use four integrated assessment models to produce metrics of Paris Agreement pledges, and show differentiated effort across countries: wealthier countries pledge to undertake greater emission reductions with higher costs. The pledges fall in the lower end of the distributions of the social cost of carbon and the cost-minimizing path to limiting warming to 2 °C, suggesting insufficient global ambition in light of leaders’ climate goals. Countries’ marginal abatement costs vary by two orders of magnitude, illustrating that large efficiency gains are available through joint mitigation efforts and/or carbon price coordination. Marginal costs rise almost proportionally with income, but full policy costs reveal more complex regional patterns due to terms of trade effects

Rapid Imaging of Tumor Cell Death in vivo using the C2A domain of Synaptotagmin-I

Cell death is an important target for imaging the early response of tumors to treatment. We describe here validation of a phosphatidylserine-binding agent for detecting tumor cell death in vivo based on the C2A domain of Synaptotagmin-I. Methods: The capability of near infrared fluorophore-labeled and 99mTechnetium- and 111Indium-labeled derivatives of C2Am for imaging tumor cell death, using planar near infrared fluorescence (NIRF) imaging and single photon computed tomography (SPECT) respectively, was evaluated in implanted and genetically engineered mouse models of lymphoma and in a human colorectal xenograft. Results: The fluorophore labeled C2Am derivative showed predominantly renal clearance and high specificity and sensitivity for detecting low levels of tumor cell death (2-5%). There was a significant correlation (R>0.9, P<0.05) between fluorescently-labeled C2Am binding and histological markers of cell death, including cleaved caspase-3, whereas there was no such correlation with a site-directed mutant of C2Am (iC2Am) that does not bind phosphatidylserine. 99mTc-C2Am and 111In-C2Am also showed favorable biodistribution profiles, with predominantly renal clearance and low non-specific retention in liver and spleen at 24 h after probe administration. 99mTc-C2Am and 111In-C2Am generated tumor-to-muscle ratios in drug-treated tumors of 4.3× and 2.2× respectively at two hours and 7.3× and 4.1× respectively at twenty-four hours after administration. Conclusion: Given the favorable biodistribution profile of 99mTc- and 111In-labelled C2Am, and their ability to produce rapid and cell death-specific image contrast, these agents have potential for clinical translation.This work was supported by a Cancer Research UK programme grant to K.M.B. S.F. was the recipient of a Ph.D. studentship from the Cambridge Biomedical Research Centre of the National Institute of Health Research with financial support from GlaxoSmithKline UK. T.B.R. was in receipt of Intra-European Marie Curie (FP7-PEOPLE-2009-IEF, Imaging Lymphoma) and Long-term EMBO (EMBO-ALT-1145-2009) fellowships

Detecting and Characterizing Mg ii Absorption in DESI Survey Validation Quasar Spectra

We present findings of the detection of Magnesium II (Mg ii, λ = 2796, 2803 Å) absorbers from the early data release of the Dark Energy Spectroscopic Instrument (DESI). DESI is projected to obtain spectroscopy of approximately 3 million quasars (QSOs), of which over 99% are anticipated to be at redshifts greater than z > 0.3, such that DESI would be able to observe an associated or intervening Mg ii absorber illuminated by the background QSO. We have developed an autonomous supplementary spectral pipeline that detects these systems through an initial line-fitting process and then confirms the line properties using a Markov Chain Monte Carlo sampler. Based upon a visual inspection of the resulting systems, we estimate that this sample has a purity greater than 99%. We have also investigated the completeness of our sample in regard to both the signal-to-noise properties of the input spectra and the rest-frame equivalent width (W 0) of the absorber systems. From a parent catalog containing 83,207 quasars, we detect a total of 23,921 Mg ii absorption systems following a series of quality cuts. Extrapolating from this occurrence rate of 28.8% implies a catalog at the completion of the five-year DESI survey that will contain over eight hundred thousand Mg ii absorbers. The cataloging of these systems will enable significant further research because they carry information regarding circumgalactic medium environments, the distribution of intervening galaxies, and the growth of metallicity across the redshift range 0.3 ≤ z < 2.5

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier