Large Animal Models for Simulating Physiology of Transfusion of Red Cell Concentrates—A Scoping Review of The Literature

Background and Objectives: Transfusion of red cell concentrates is a key component of medical therapy. To investigate the complex transfusion-associated biochemical and physiological processes as well as potential risks for human recipients, animal models are of particular importance. This scoping review summarizes existing large animal transfusion models for their ability to model the physiology associated with the storage of erythrocyte concentrates. Materials and Methods: The electronic databases PubMed, EMBASE, and Web of Science were systematically searched for original studies providing information on the intravenous application of erythrocyte concentrates in porcine, ovine, and canine animal models. Results: A total of 36 studies were included in the analysis. The majority of porcine studies evaluated hemorrhagic shock conditions. Pig models showed high physiological similarities with regard to red cell physiology during early storage. Ovine and canine studies were found to model typical aspects of human red cell storage at 42 days. Only four studies provided data on 24 h in vivo survival of red cells. Conclusions: While ovine and canine models can mimic typical human erythrocyte storage for up to 42 days, porcine models stand out for reliably simulating double-hit pathologies such as hemorrhagic shock. Large animal models remain an important area of translational research since they have an impact on testing new pharmacological or biophysical interventions to attenuate storage-related adverse effects and allow, in a controlled environment, to study background and interventions in dynamic and severe disease conditions

Search for top squark Production at the LHC at s=13\sqrt{\text{s}}=13 TeV with the ATLAS Detector Using Multivariate Analysis Techniques

Supersymmetry is a very promising extension of the Standard Model. It predicts new heavy particles, which are currently searched for in the ATLAS experiment at the Large Hadron Collider at a center-of-mass energy of 13 TeV. So far, all searches for supersymmetric particles use a cut-based signal selection. In this thesis, the use of multivariate selection techniques, Boosted Decision Trees and Artificial Neural Networks, is explored for the search for top squarks, the supersymmetric partner of the top quark. The multivariate methods increase the expected lower limit in the mass of top squarks by approximately 90 GeV from currently 990 GeV for small neutralino masses