Publikasjoner

Extracellular Vesicles as Next-Generation Diagnostics and Advanced Therapy Medicinal Products

Stawarska, Agnieszka; Bamburowicz-Klimkows, Magdalena; Rundén-Pran, Elise; Dusinska, Maria; Cimpan, Mihaela Roxana; Rios Mondragon, Ivan; Grudzinski, Ireneusz P.

Extracellular vesicles (EVs) hold great promise for clinical application as new diagnostic and therapeutic modalities. This paper describes major GMP-based upstream and downstream manufacturing processes for EV large-scale production, also focusing on post-processing technologies such as surface bioengineering and uploading studies to yield novel EV-based diagnostics and advanced therapy medicinal products. This paper also focuses on the quality, safety, and efficacy issues of the bioengineered EV drug candidates before first-in-human studies. Because clinical trials involving extracellular vesicles are on the global rise, this paper encompasses different clinical studies registered on clinical-trial register platforms, with varying levels of advancement, highlighting the growing interest in EV-related clinical programs. Navigating the regulatory affairs of EVs poses real challenges, and obtaining marketing authorization for EV-based medicines remains complex due to the lack of specific regulatory guidelines for such novel products. This paper discusses the state-of-the-art regulatory knowledge to date on EV-based diagnostics and medicinal products, highlighting further research and global regulatory needs for the safe and reliable implementation of bioengineered EVs as diagnostic and therapeutic tools in clinical settings. Post-marketing pharmacovigilance for EV-based medicinal products is also presented, mainly addressing such topics as risk assessment and risk management.

MDPI

Extreme climate of the global troposphere and stratosphere in the 1940-42 related to El Nino.

Brönnimann, S.; Luterbacher, J.; Staehelin, J.; Svendby, T. M.; Hansen, G.; Svenøe, T.

Extreme precipitation event over North China in August 2010: observations, monthly forecasting, and link to intra-seasonal variability of the Silk-Road wave-train across Eurasia.

Orsolini, Y.; Zhang, L.; Peters, D.; Fraedrich, K.

Extreme precipitation events over north China in August 2010 and their link to eastward-propagating wave-trains across Eurasia: observations and monthly forecasting.

Orsolini, Y. J.; Zhang, L.; Peters, D. H. W.; Fraedrich, K.; Zhu, X.; Schneidereit, A.; van den Hurk, B.

Extreme stratospheric vortex events in the ECMWF seasonal forecast model with re-analyses

Orsolini, Yvan

EYE-CLIMA: A Horizon Europe project using atmospheric inversions to improve national estimates of greenhouse gas emissions

Winiwarter, Wilfried; Thompson, Rona Louise; Stohl, Andreas; Peylin, Philippe; Ciais, Philippe; Boesch, Hartmuth; Aalto, Tuula; Berchet, Antoine; Kanakidou, Maria; Peters, Glen Philip; Shchepashchenko, Dmitry; Chang, Jean-Pierre; Fuß, Roland; Pisso, Ignacio; Engelen, Richard; Arneth, Almuth; Buchmann, Nina; Reimann, Stefan; Platt, Stephen Matthew; Krishnankutty, Nalini

Eyjafjallajökull volcanic ash concentrations determined using Spin Enhanced Visible and Infrared Imager measurements.

Prata, A.J.; Prata, A.T.

Facing the challenges of the International Airways Volcano Watch: the 2004/05 eruptions of Manam, Papua New Guinea.

Tupper, A.; Itikarai, I.; Richards, M.S.; Prata, F.; Carn, S.; Rosenfeld, D.

Factors affecting bioaccumulation of cyclic volatile methyl siloxanes in a subarctic benthopelagic food web.

Krogseth, I. S.; Undeman, E.; Evenset, A.; Christensen, G. N.; Whelan, M. J.; Breivik, K.; Evenset, A.; Warner, N. A.

Factors of change: using trophic magnification factors (TMFs) to assess changes in POP bioaccumulation in Arctic food webs. NILU F

Warner, N.A.; Hallanger, I.G.; Ruus, A.; Evenset, A.; Herzke, D.; Gabrielsen, G.W.; Borgå, K.

Fagernestunnelen i Narvik. Vurdering av luftforurensning ved tunnelmunninger. NILU OR

Haugsbakk, I.

FAIRMODE 2017-2019 roadmap.

Thunis, P.; Belis, C.; Henrichs, T.; Hoss, F.; Ortiz, A. G.; Janssen S.; Guerreiro, C.; Tarrasón, L.; Guevara, M.; Pirovano, G.; Clappier, A.; Monteiro, A.

FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking. Version 3.3.

Janssen, S.; Thunis, P.; Adani, M.; Piersanti, A.; Carnevale, C.; Cuvelier, C.; Durka, P.; Georgieva, E.; Guerreiro, Cristina; Malherbe, L.; Maiheu, B.; Meleux, F.; Monteiro, A.; Miranda, A.; Olesen, H.; Pfafflin, F.; Stocker, J.; Sousa Santos, Gabriela; Stidworthy, A.; Stortini, M.; Trimpeneers, E.; Viaene, P.; Vitali, L.; Vincent, K.; Wesseling, J.

The development of the procedure for air quality model benchmarking in the context of the Air Quality Directive 2008/50/EC (AQD) has been an on-going activity in the context of the FAIRMODE community, chaired by the JRC. A central part of the studies was the definition of proper modelling quality indicators and criteria to be fulfilled in order to allow sufficient level of quality for a given model application under the AQD. The focus initially on applications related to air quality assessment has gradually been expanded to other applications, such as forecasting and planning. The main purpose of this Guidance Document is to explain and summarise the current concepts of the modelling quality objective methodology, elaborated in various papers and documents in the FAIRMODE community, addressing model applications for air quality assessment and forecast. Other goals of the Document are linked to presentation and explanation of templates for harmonised reporting of modelling results. Giving an overview of still open issues in the implementation of the presented methodology, the document aims at triggering further research and discussions. A core set of statistical indicators is defined using pairs of measurement-modelled data. The core set is the basis for the definition of a modelling quality indicator (MQI) and additional modelling performance indicators (MPI), which take into account the measurement uncertainty. The MQI describes the discrepancy between measurements and modelling results (linked to RMSE), normalised by measurement uncertainty and a scaling factor. The modelling quality objective (MQO) requires MQI to be less than or equal to 1. With an arbitrary selection of the scaling factor of 2, the fulfilment of the MQO means that the allowed deviation between modelled and measured concentrations is twice the measurement uncertainty. Expressions for the MQI calculation based on time series and yearly data are introduced. MPI refer to aspects of correlation, bias and standard deviation, applied to both the spatial and temporal dimensions. Similarly to the MQO for the MQI, modelling performance criteria (MPC) are defined for the MPI; they are necessary, but not sufficient criteria to determine whether the MQO is fulfilled. The MQO is required to be fulfilled at 90% of the stations, a criterion which is implicitly taken into account in the derivation of the MQI. The associated modelling uncertainty is formulated, showing that in case of MQO fulfilment the modelling uncertainty must not exceed 1.75 times the measurement one (with the scaling factor fixed to 2). A reporting template is presented and explained for hourly and yearly average data. In both cases there is a diagram and a table with summary statistics. In a separate section open issues are discussed and an overview of related publications and tools is provided. Finally, a chapter on modelling quality objectives for forecast models is introduced. In Annex 1, we discuss the measurement uncertainty which is expressed in terms of concentration and its associated uncertainty. The methodology for estimating the measurement uncertainty is overviewed and the parameters for its calculation for PM, NO2 and O3 are provided. An expression for the associated modelling uncertainty is also given. This aim of this document is to support modelling groups, local, regional and national authorities in their modelling application, in the context of air quality policy.

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