scholarly journals From interacting agents to density-based modeling with stochastic PDEs

2021 ◽  
Vol 16 (1) ◽  
pp. 1-32
Author(s):  
Luzie Helfmann ◽  
Nataša Djurdjevac Conrad ◽  
Ana Djurdjevac ◽  
Stefanie Winkelmann ◽  
Christof Schütte
Keyword(s):  
Stochastic Pdes ◽  
Interacting Agents

Spatial Competition with Interacting Agents

2016 ◽  
Vol 10 (3) ◽  
pp. 75-91 ◽  
Author(s):  
Bertrand Ottino-Loffler ◽  
Forrest Stonedahl ◽  
Vipin Veetil ◽  
Uri Wilensky
Keyword(s):  
Spatial Competition ◽  
Interacting Agents

scholarly journals The use of a clinical decision support tool to assess the risk of QT drug–drug interactions in community pharmacies

2021 ◽  
Vol 12 ◽  
pp. 204209862199609
Author(s):  
Florine A. Berger ◽  
Heleen van der Sijs ◽  
Teun van Gelder ◽  
Patricia M. L. A. van den Bemt
Keyword(s):  
Decision Support ◽  
Drug Interactions ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Community Pharmacies ◽  
Support Tool ◽  
Interacting Agents ◽  
Before And After ◽  
Structured Approach

Introduction: The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. Methods: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. Results: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. Conclusion: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. Plain Language Summary The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug–drug interactions (QT-DDIs). Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included. Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used. Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

scholarly journals Highly Selective Anti-Cancer Activity of Cholesterol-Interacting Agents Methyl-β-Cyclodextrin and Ostreolysin A/Pleurotolysin B Protein Complex on Urothelial Cancer Cells

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0137878 ◽  
Author(s):  
Nataša Resnik ◽  
Urška Repnik ◽  
Mateja Erdani Kreft ◽  
Kristina Sepčić ◽  
Peter Maček ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Protein Complex ◽  
Urothelial Cancer ◽  
Interacting Agents ◽  
Anti Cancer ◽  
Cancer Activity

Collective Choice and Mutual Knowledge Structures

1998 ◽  
Vol 01 (02n03) ◽  
pp. 221-236 ◽  
Author(s):  
Diana Richards ◽  
Brendan D. McKay ◽  
Whitman A. Richards
Keyword(s):  
Complex System ◽  
Social Choice ◽  
Group Decision ◽  
Knowledge Structure ◽  
Collective Choice ◽  
Sense Data ◽  
Mutual Knowledge ◽  
Interacting Agents ◽  
Aggregation Of Information ◽  
Domain Independent

The conditions under which the aggregation of information from interacting agents results in a stable or an unstable collective outcome is an important puzzle in the study of complex systems. We show that if a complex system of aggregated choice respects a mutual knowledge structure, then the prospects of a stable collective outcome are considerably improved. Our domain-independent results apply to collective choice ranging from perception, where an interpretation of sense data is made by a collection of perceptual modules, to social choice, where a group decision is made from a set of preferences held by individuals.

Allelomimesis as a generic clustering mechanism for interacting agents

2003 ◽  
Vol 320 ◽  
pp. 590-600 ◽  
Author(s):  
Dranreb Earl Juanico ◽  
Christopher Monterola ◽  
Caesar Saloma
Keyword(s):  
Interacting Agents

Foundations for Knowledge and Belief Management

2021 ◽  
Vol 17 (3) ◽  
pp. 88-99
Author(s):  
Roderic A. Girle
Keyword(s):  
Intelligent Systems ◽  
Turing Test ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Conceptual Structures ◽  
Interacting Agents ◽  
Questions And Answers ◽  
Multi Agent ◽  
The Difference ◽  
Dialogue Theory

Three foundational principles are introduced: intelligent systems such as those that would pass the Turing test should display multi-agent or interactional intelligence; multi-agent systems should be based on conceptual structures common to all interacting agents, machine and human; and multi-agent systems should have an underlying interactional logic such as dialogue logic. In particular, a multi-agent rather than an orthodox analysis of the key concepts of knowledge and belief is discussed. The contrast that matters is the difference between the different questions and answers about the support for claims to know and claims to believe. A simple multi-agent system based on dialogue theory which provides for such a difference is set out.

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