Purpose Ischemic myocardial contracture (IMC) or “stone heart” is a condition with rapid onset following circulatory death. It inhibits transplantability of hearts donated upon circulatory death (DCD). We investigate the effectiveness of hemodynamic normalization upon withdrawal of life-sustaining therapy (WLST) in a large-animal controlled DCD model, with the hypothesis that reduction in cardiac work delays the onset of IMC. Methods A large-animal study was conducted comprising of a control group ($$n=6$$ n = 6 ) receiving no therapy upon WLST, and a test group ($$n=6$$ n = 6 ) subjected to a protocol for fully automated computer-controlled hemodynamic drug administration. Onset of IMC within 1 h following circulatory death defined the primary end-point. Cardiac work estimates based on pressure-volume loop concepts were developed and used to provide insight into the effectiveness of the proposed computer-controlled therapy. Results No test group individual developed IMC within $${1} \text { h}$$ 1 h , whereas all control group individuals did (4/6 within $${30}{\text { min}}$$ 30 min ). Conclusion Automatic dosing of hemodynamic drugs in the controlled DCD context has the potential to prevent onset of IMC up to $${1}{\text { h}}$$ 1 h , enabling ethical and medically safe organ procurement. This has the potential to increase the use of DCD heart transplantation, which has been widely recognized as a means of meeting the growing demand for donor hearts.

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This master thesis project proposes methods for individualizing closed-loop controlled anesthesia. One of the largest challenges with closed-loop anesthesia is the variation between patients in the sensitivity to the anesthetic drug, here propofol. Due to limited excitation in the process dynamics together with a high measurement noise level is it not possible to determine a full reliable model describing a patient's dynamics online. The method used here for minimizing the effects of inter-patient variability was through patient model partitioning of children and adult models. Partitioning was based on similarity measures between patients, for example age, weight and applied to a dynamic model describing each patient. For each subset resulting from partitioning, an optimal PID controller has been synthesized. This thesis has shown that the effects of inter-patient variability can be reduced using partitioning into two subsets. More subsets did not result in a significant reduction. Partitioning based on ν-gap between patient models resulted in the best attenuation of surgical stimulation disturbances. Partitioning based on age for children and weight for adults reduces the impact from surgical stimulation were proposed for clinical practices. These methods are easy to implement because the demographics are known beforehand and does not depend on actual measurements during the anesthesia. The results are substantiated by simulations and calculations of achieved attenuation with acceptable performance and preserved robustness. I would first like to thank my supervisor Kristian Soltesz. I am very grateful to you for introducing me to this interesting research field. You have always come with valuable inputs on my proceeding work and I am very grateful for all of your help. In the research group at BCCHR, University of British Columbia, Vancouver, Canada, I must acknowledge professor Guy Dumont and Dr. Klaske van Heusden for letting me visit and take part of their work at the Childrens Hospital of British Columbia. I would also like to thank Dr. Mark Ansermino for letting me visit anesthesia surgeries during my stay, giving me a broader perspective of the subject. The rest of the research group requires an acknowledgement for the warm welcome during my stay in Vancouver. I also want to thank researcher Richard Pates for the introduction and help with implementation of the ν-gap. A special thank to PhD student José Manuel González Cava who has provided me with the code to the synthesis, particularly designed for the children model set. He has also been very helpful during the project, always available for questions. 5

Pancreatic islets from obese-hyperglycemic mice were used for studying a proposed relationship between insulin release and the ß.cell content of 6-phosphogluconate. A short period of ischemia reduced the amount of this intermediate in the islets but not in the exocrine pancreas. There was a steep rise of 6-phosphogluconate in microdissected islets when the extracellular glucose concentration was increased from 0.6 to 3.0 mg/m1. A plateau of about 40 J.II1loles 6-phosphogluconate per kg islet dry weight was reached at 10 mg/mi of glucose. 6-phosphogluconate rernained unaffected when the microdissected islets were exposed to the insulin secretagogues g1ibenclamide or dibutyryl-3,5-cyclic AMP. After addition of epinephrine to or omission of Ca++ from a high glucose medium, there was, however, a significant elevation of 6phosphogluconate. The results indicate that the isIet content of 6-phosphogluconate does not necessarily reflect the rate of insulin release.

A central challenge within pharmacometrics is to establish a relation between pharmacological model parameters, such as compartment volumes and diffusion rate constants, and known population covariates, such as age and body mass. There is rich literature dedicated to the learning of functional mappings from the covariates to the model parameters, once a search class of functions has been determined. However, the state-of-the-art selection of the search class itself is ad hoc. We demonstrate how neural network-based symbolic regression can be used to simultaneously find the function form and its parameters. The method is put in relation to the literature on symbolic regression and equation learning. A conceptual demonstration is provided through examples, as is a road map to full-scale employment to pharmacological data sets, relevant to closed-loop anesthesia.