(Tramadol-warfarin interaction)
Drug and Therapeutics Bulletin 2024;62:36
https://doi.org/10.1136/dtb.2024.000008
Traducido por Salud y Fármacos, publicado en Boletín Fármacos: Farmacovigilancia 2024; 27 (2)
Tags: riesgo de hemorragia, anticoagulantes, tratamiento del dolor y riesgo de hemorragia
Resumen de: Danbury G Susan Gladstone: Informe sobre prevención de muertes futuras [en línea], 2023. Disponible: https://www.judiciary.uk/prevention-of-future-death-reports/susan-gladstone-prevention-offuture-deaths-report/ Consultado el 8 de enero de 2024].
Puntos más importantes
En el Reino Unido, los médicos forenses tienen la obligación de informar los casos en los que consideren que se deben tomar medidas para prevenir futuras muertes.
Un forense investigó el caso de una paciente que murió de hemorragia intraparenquimatosa y subaracnoidea.
La investigación concluyó que la causa de la hemorragia fue una interacción entre warfarina y tramadol que provocó un grado significativo de anticoagulación.
Un forense británico que concluyó que la muerte de una paciente se debió a una interacción generalmente desconocida entre la warfarina y el tramadol ha pedido a NHS (National Health Service o Servicio Nacional de Salud) de Inglaterra que tome medidas para prevenir futuras muertes por dicha interacción [1].
Extreme drought events have the potential to cause dramatic changes in ecosystem structure and function, but the controls upon ecosystem stability to drought remain poorly understood. Here we used model systems of two commonly occurring, temperate grassland communities to investigate the shortterm interactive effects of a simulated 100-year summer drought event, soil nitrogen (N) availability and plant species diversity (low/high) on key ecosystem processes related to carbon (C) and N cycling. Whole ecosystem CO2 fluxes and leaching losses were recorded during drought and post-rewetting. Litter decomposition and C/N stocks in vegetation, soil and soil microbes were assessed 4 weeks after the end of drought. Experimental drought caused strong reductions in ecosystem respiration and net ecosystem CO2 exchange, but ecosystem fluxes recovered rapidly following rewetting irrespective of N and species diversity. As expected, root C stocks and litter decomposition were adversely affected by drought across all N and plant diversity treatments. In contrast, drought increased soil water retention, organic nutrient leaching losses and soil fertility. Drought responses of above-ground vegetation C stocks varied depending on plant diversity, with greater stability of above-ground vegetation C to drought in the high versus low diversity treatment. This positive effect of high plant diversity on above-ground vegetation C stability coincided with a decrease in the stability of microbial biomass C. Unlike species diversity, soil N availability had limited effects on the stability of ecosystem processes to extreme drought. Overall, our findings indicate that extreme drought events promote post-drought soil nutrient retention and soil fertility, with cascading effects on ecosystem C fixation rates. Data on above-ground ecosystem processes underline the importance of species diversity for grassland function in a changing environment. Furthermore, our results suggest that plant–soil interactions play a key role for the short-term stability of above-ground vegetation C storage to extreme drought events.
The Experience API (xAPI) allows us to collect data about any type of learning experience or activity, but does that mean we should? Should we generate massive amounts of xAPI data for every possible type of interaction and then expect to make sense of it all later? This approach can be costly in terms of data storage, but also in terms of your time.
Sunday Blake dives into the latest in learning analytics and engagement data, and asks how universities can act upon it to make our interactions with students more human.
Dashboard is a prominent artefact that users interact with when it comes to learning analytics. What is a learning analytics dashboard ? Who is it for? What is the key to effective adoption of learning analytics dashboard? Listen to Martin Hlosta and Fabio Campos sharing their experience in supporting the use of learning analytics dashboard on a large scale.
S. Mlakar, M. Haberfellner, H. Jetter, und M. Haller. Proceedings of the 2021 ACM Designing Interactive Systems Conference, New York, NY, USA, Association for Computing Machinery, (2021)event-place: Virtual Event, USA.
J. Kim, P. Guo, D. Seaton, P. Mitros, K. Gajos, und R. Miller. Proceedings of the First ACM Conference on Learning @ Scale Conference, Seite 31–40. New York, NY, USA, Association for Computing Machinery, (2014)
H. Seichter, J. Grubert, und T. Langlotz. Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services - MobileHCI \textquotesingle13, Seite 616-621. ACM Press, (2013)
L. Lucignano, F. Cutugno, S. Rossi, und A. Finzi. Proceedings of the 15th ACM on International Conference on Multimodal Interaction, Seite 197–204. New York, NY, USA, Association for Computing Machinery, (2013)
S. Anjomshoae, A. Najjar, D. Calvaresi, und K. Främling. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Seite 1078–1088. Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems, (2019)
S. Mahi, K. Nam, und C. Crick. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Seite 1359–1367. Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems, (2019)
A. Frieder, R. Lin, und S. Kraus. Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3, Seite 1281–1282. Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems, (2012)
V. Murphy, A. Littlejohn, und B. Rienties. Mixed methods social network analysis: Theories and methodologies in learning and education, Routledge, London, United Kingdom, (2020)
V. St-Amour, S. Tobin-Hochstadt, und M. Felleisen. Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications, Seite 163--178. ACM, (2012)
A. Satyanarayan, N. Weibel, und J. Hollan. Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces, Seite 105--108. New York, NY, USA, ACM, (2012)
A. Fouse, N. Weibel, E. Hutchins, und J. Hollan. CHI '11 Extended Abstracts on Human Factors in Computing Systems, Seite 299--304. New York, NY, USA, ACM, (2011)Demo.
A. Piper, N. Weibel, und J. Hollan. Proceedings of the 2013 Conference on Computer Supported Cooperative Work, Seite 215--224. New York, NY, USA, ACM, (2013)
M. Atzmueller, L. Thiele, G. Stumme, und S. Kauffeld. Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017), Eindhoven, The Netherlands, Eindhoven University of Technology, (2017)