379 research outputs found

    Cerebrovascular dysfunction in cerebral small vessel disease

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    INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    k-Means

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    Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

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    This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

    PhD students´day FMST 2023

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    The authors gave oral presentations of their work online as part of a Doctoral Students’ Day held on 15 June 2023, and they reflect the challenging work done by the students and their supervisors in the fields of metallurgy, materials engineering and management. There are 82 contributions in total, covering a range of areas – metallurgical technology, thermal engineering and fuels in industry, chemical metallurgy, nanotechnology, materials science and engineering, and industrial systems management. This represents a cross-section of the diverse topics investigated by doctoral students at the faculty, and it will provide a guide for Master’s graduates in these or similar disciplines who are interested in pursuing their scientific careers further, whether they are from the faculty here in Ostrava or engineering faculties elsewhere in the Czech Republic. The quality of the contributions varies: some are of average quality, but many reach a standard comparable with research articles published in established journals focusing on disciplines of materials technology. The diversity of topics, and in some cases the excellence of the contributions, with logical structure and clearly formulated conclusions, reflect the high standard of the doctoral programme at the faculty.Ostrav

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
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