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Hypoxic ischaemic encephalopathy (HIE) is a significant cause of death and disability. Therapeutic hypothermia (TH) is the only available standard of treatment, but 45-55% of cases still result in death or neurodevelopmental disability following TH. This work has focussed on developing a new brain tissue physiology and biochemistry systems biology model that includes temperature effects, as well as a Bayesian framework for analysis of model parameter estimation. Through this, we can simulate the effects of temperature on brain tissue oxygen delivery and metabolism, as well as analyse clinical and experimental data to identify mechanisms to explain differing behaviour and outcome. Presented here is an application of the model to data from two piglets treated with TH following hypoxic-ischaemic injury showing different responses and outcome following treatment. We identify the main mechanism for this difference as the Q10 temperature coefficient for metabolic reactions, with the severely injured piglet having a median posterior value of 0.133 as opposed to the mild injury value of 5.48. This work demonstrates the use of systems biology models to investigate underlying mechanisms behind the varying response to hypothermic treatment.

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Hypoxic ischemic encephalopathy (HIE) is a significant cause of death and neurological disability in newborns. Therapeutic hypothermia at 33.5 °C is one of the most common treatments in HIE and generally improves outcome; however 45-55% of injuries still result in death or severe neurodevelopmental disability. We have developed a systems biology model of cerebral oxygen transport and metabolism to model the impact of hypothermia on the piglet brain (the neonatal preclinical animal model) tissue physiology. This computational model is an extension of the BrainSignals model of the adult brain. The model predicts that during hypothermia there is a 5.1% decrease in cerebral metabolism, 1.1% decrease in blood flow and 2.3% increase in cerebral tissue oxygenation saturation. The model can be used to simulate effects of hypothermia on the brain and to help interpret bedside recordings.

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Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain distributions of parameters that can better guard against misinterpretation of results, as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a 'healthy' and 'impaired' state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space.

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Artefacts are a common and unwanted aspect of any measurement process, especially in a clinical environment, with multiple causes such as environmental changes or motion. In near-infrared spectroscopy (NIRS), there are several existing methods that can be used to identify and remove artefacts to improve the quality of collected data.We have developed a novel Automatic Broadband Artefact Detection (ABroAD) process, using machine learning methods alongside broadband NIRS data to detect common measurement artefacts using the broadband intensity spectrum. Data were collected from eight subjects, using a broadband NIRS monitoring over the frontal lobe with two sensors. Six different artificial artefacts - vertical head movement, horizontal head movement, frowning, pressure, ambient light, torch light - were simulated using movement and light changes on eight subjects in a block test design. It was possible to identify both light artefacts to a good degree, as well as pressure artefacts. This is promising and, by expanding this work to larger datasets, it may be possible to create and train a machine learning pipeline to automate the detection of various artefacts, making the analysis of collected data more reliable.

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Multimodal monitoring of the brain generates a great quantity of data, providing the potential for great insight into both healthy and injured cerebral dynamics. In particular, near-infrared spectroscopy can be used to measure various physiological variables of interest, such as haemoglobin oxygenation and the redox state of cytochrome-c-oxidase, alongside systemic signals, such as blood pressure. Interpreting these measurements is a complex endeavour, and much work has been done to develop mathematical models that can help to provide understanding of the underlying processes that contribute to the overall dynamics. BCMD is a software framework that was developed to run such models. However, obtaining, installing and running this software is no simple task. Here we present WeBCMD, an online environment that attempts to make the process simpler and much more accessible. By leveraging modern web technologies, an extensible and cross-platform package has been created that can also be accessed remotely from the cloud. WeBCMD is available as a Docker image and an online service.