16 research outputs found

    Effect of aliskiren on post-discharge outcomes among diabetic and non-diabetic patients hospitalized for heart failure: insights from the ASTRONAUT trial

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    Aims The objective of the Aliskiren Trial on Acute Heart Failure Outcomes (ASTRONAUT) was to determine whether aliskiren, a direct renin inhibitor, would improve post-discharge outcomes in patients with hospitalization for heart failure (HHF) with reduced ejection fraction. Pre-specified subgroup analyses suggested potential heterogeneity in post-discharge outcomes with aliskiren in patients with and without baseline diabetes mellitus (DM). Methods and results ASTRONAUT included 953 patients without DM (aliskiren 489; placebo 464) and 662 patients with DM (aliskiren 319; placebo 343) (as reported by study investigators). Study endpoints included the first occurrence of cardiovascular death or HHF within 6 and 12 months, all-cause death within 6 and 12 months, and change from baseline in N-terminal pro-B-type natriuretic peptide (NT-proBNP) at 1, 6, and 12 months. Data regarding risk of hyperkalaemia, renal impairment, and hypotension, and changes in additional serum biomarkers were collected. The effect of aliskiren on cardiovascular death or HHF within 6 months (primary endpoint) did not significantly differ by baseline DM status (P = 0.08 for interaction), but reached statistical significance at 12 months (non-DM: HR: 0.80, 95% CI: 0.64-0.99; DM: HR: 1.16, 95% CI: 0.91-1.47; P = 0.03 for interaction). Risk of 12-month all-cause death with aliskiren significantly differed by the presence of baseline DM (non-DM: HR: 0.69, 95% CI: 0.50-0.94; DM: HR: 1.64, 95% CI: 1.15-2.33; P < 0.01 for interaction). Among non-diabetics, aliskiren significantly reduced NT-proBNP through 6 months and plasma troponin I and aldosterone through 12 months, as compared to placebo. Among diabetic patients, aliskiren reduced plasma troponin I and aldosterone relative to placebo through 1 month only. There was a trend towards differing risk of post-baseline potassium ≥6 mmol/L with aliskiren by underlying DM status (non-DM: HR: 1.17, 95% CI: 0.71-1.93; DM: HR: 2.39, 95% CI: 1.30-4.42; P = 0.07 for interaction). Conclusion This pre-specified subgroup analysis from the ASTRONAUT trial generates the hypothesis that the addition of aliskiren to standard HHF therapy in non-diabetic patients is generally well-tolerated and improves post-discharge outcomes and biomarker profiles. In contrast, diabetic patients receiving aliskiren appear to have worse post-discharge outcomes. Future prospective investigations are needed to confirm potential benefits of renin inhibition in a large cohort of HHF patients without D

    A Genetic Algorithm Driven Linear Programming for Battery Optimal Scheduling in nearly Zero Energy Buildings

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    EU has seen an increasing demand for both nearly zero energy buildings (nZEBs) and building integrated Photovoltaic (BIPV) systems in the last decade. This stems from the energy-driven regulations relating to building efficiency improvements, requiring more realistic and smarter techniques to strengthen their employment and overall performance. Apart from the passive energy-efficiency measures of nZEBs (such as thermal insulation, energy saving appliances, etc.), more advanced and sophisticated energy management mechanisms have to take place in order to accommodate and support their crucial contribution to sustainable development. This paper presents the daily optimum dispatch of a battery, in a building with PV, using Linear Programming (LP) driven by Genetic Algorithm (GA), aiming the minimization of the building's net energy. The obtained results show that there is a high potential of using such approaches for maintaining the net grid energy levels of a building as minimum as possible

    Artwork Style Recognition Using Vision Transformers and MLP Mixer

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    Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and difficult task in this field is the classification of artwork styles, since the artistic style of a painting is a descriptor that captures rich information about the painting. In this paper, two different Deep Learning architectures—Vision Transformer and MLP Mixer (Multi-layer Perceptron Mixer)—are trained from scratch in the task of artwork style recognition, achieving over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset. In addition, a comparative study between the most common optimizers was conducted obtaining useful information for future studies

    Music Deep Learning: Deep Learning Methods for Music Signal Processing&#x2014;A Review of the State-of-the-Art

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    The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments of Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research
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