44 research outputs found

    Importance of the time of initiation of mineralocorticoid receptor antagonists on risk of mortality in patients with heart failure

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    Introduction: Several studies have definitively shown the benefit of mineralocorticoid receptor antagonists (MRAs) in patients with heart failure (HF). However, very few prior studies examined the relationship between the timing of initiation of MRAs and prognosis. In addition, on this topic, there is no information regarding the specific population of patients suffering a first episode of decompensated congestive HF. Methods: We studied a homogenous cohort of patients discharged alive from our hospital after a first episode of decompensated congestive HF, in order to clarify the association between time of aldosterone receptor antagonist (ARA) initiation (within the first 90 days after hospital discharge) and mortality. Our population was composed of a series of consecutive patients. All-cause mortality was compared between patients who initiated MRAs at discharge (early group) and those who initiated MRAs one month later and up to 90 days after discharge (delayed group). We used prescription time distribution matching to control for survival difference between groups. Results: The early and delayed groups consisted of 365 and 320 patients, respectively. During the one-year follow-up, a significant difference in mortality was demonstrated between groups. Adjusted hazard ratios (HRs) for early versus delayed initiation were 1.72 (95% confidence interval (CI) 0.96 to 2.84) at six months, and 1.93 (95% CI 1.18 to 3.14) at one year. Conclusions: Delay of MRA initiation up to 30 to 90 days after discharge implies a significant increase in mortality compared with MRA initiation at discharge, after a first episode of decompensate congestive HF

    Acute myocardial infarction with occlusion of all three main epicardial coronary arteries: When Mother Nature takes care more than physicians

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    Double-arterial coronary stent thrombosis in acute myocardial infarction (AMI) is an infrequent but severe complication, especially when the third main coronary artery is chronically occluded. The conus artery (CA) can serve as a major source of collateral when the left anterior descendent coronary artery (LAD) becomes obstructed. We report a case of a 48-year-old man presenting with AMI due to a very late double-arterial stent thrombosis (ST) following drug-eluting stent implantation and a chronic occlusion of LAD collateralized by a large anomalous CA, which provided for the entire vascularization of the coronary tree. © 2010 Springer

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

    Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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    The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

    Underlying Event measurements in pp collisions at s=0.9 \sqrt {s} = 0.9 and 7 TeV with the ALICE experiment at the LHC

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