1,721,145 research outputs found
Terlipressin as rescue treatment of refractory shock in a neonate
No full textPatients with septic shock may develop refractory hypotension despite maximal inotropic support with impairment of clinical outcome. Terlipressin, a long-acting vasopressin analogue, is reported to be effective as rescue treatment of refractory septic shock in adult and paediatric patients, while clinical experience in neonates is definitely scarce. We report a neonate with systemic inflammatory response syndrome after surgery for abdominal neuroblastoma who received terlipressin as rescue treatment after failure of volume load and catecholamines. Terlipressin promptly reversed hypotension and improved tissue perfusion without adverse effects. Conclusion: Terlipressin appears an effective rescue treatment in patients with refractory vasodilatory septic shock. Further studies are required to assess its efficacy and safety in neonatal population. © 2008 The Author(s)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Exergy-based comparative assessment of "Zero CO2 Emission" Coal Gasification Processes feeding H2-Fuelled Power Plants
No full textCausal interaction modeling on ultra-processed food manufacturing
No full textIn recent years computer science theories have been applied to manufacturing improving products quality, fault detection and process monitoring. However, there is a lack of research in the identification of causal relationships among data. These associations of cause-effect are important since they allow root causes to be analysed, they highlight the most influential process variables and they embed a typical human reasoning model that is largely applied in manufacturing. Compared to knowledge-based approach, data driven causal discovery (DCD) enables causal modeling without overloading expert operators and scales faster. However, DCD is challenging to be applied especially in small-medium enterprises where machines raw data are stored without the support of specialized data analyst team. In this work, we aim to automatically reconstruct the causal interaction model of the production flow from raw data. We use PCMCI, a constraint-based causal discovery algorithm, that handles both linear and nonlinear relationships in time series. We validate our method on a synthetic realization that emulates manufacturing features and on real data with domain expert support. The obtained results confirm that PCMCI is able to recognize more than 50% of causal relationships without any false positives. The application of the PCMCI method in an ultra-processed food manufacturer allows to propose a novel causal interaction model integrating data-driven and expert's knowledge
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