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Among all organs, lungs are the last ones to grow and develop fully. As a result, extreme premature neonates may suffer from respiratory failure due to their immature lungs and will require respiratory support in the form of mechanical ventilation or extracorporeal membrane oxygenation (ECMO). In addition, extreme prematurity is recognized as the primary cause of neonatal morbidity and mortality. The conventional standard of care for respiratory support of preterm neonates with respiratory failure are invasive and may lead to long-term morbidities and complications. Hence, a non-invasive respiratory support technique named “Artificial Placenta” has been developed to address the issues and challenges associated with the current technologies. An artificial placenta type device is one designed to provide required oxygenation in room air via non-invasive access to the umbilical vessels without the need of any external pump. In this thesis, microfluidic and microfabrication technologies have been employed in the development of a pumpless neonatal lung assist device (LAD) for preterm neonates in two approaches: 1) design and develop novel microfabrication techniques to fabricate advanced microfluidic blood oxygenators with high gas exchange capacity and reduced form factor and 2) design and construct several modular LADs based on the oxygenators that were developed to fulfill the required gas transfer needs for these babies. The new microfluidic blood oxygenators with double-sided gas transfer channels were found to enhance oxygenation up to 343 % in room air and be easily scaled-up to achieve higher gas exchange capacities without a noticeable increase in priming volume. Furthermore, this microfabrication method has been utilized to make the largest all PDMS ultra-thin double-sided blood oxygenator with higher gas exchange capabilities. Also, a novel composite material made of PDMS and PTFE was introduced that conferred high flexibility to the oxygenator to decrease the form factor of such devices. This device was one of the first microfluidic blood oxygenators with enough flexibility to be deformed, bent, or rolled without limitation and losing its functionality. In order to satisfy the gas transfer need of these preterm neonates, few microfluidic-based modular LADs were constructed to support different birth weights up to 2 kg. The main design criteria for such a LAD in this research was low pressure drops (capable of being operated by a baby’s heart), an oxygen transfer of 1.3 – 1.9 mL min-1 kg-1 of body weight (or an increase in oxygen saturation level from ~ 75 % to ~ 100 % and ideally in room air), and low priming volume (less than 10 % of the total blood volume of a baby). These LADs first were evaluated in vitro to measure their gas exchange capacities and those which could meet needed oxygenation would be tested in vivo. For the first time, it was shown that a pumpless microfluidic-based LAD could support a newborn piglet and provide adequate oxygenation in room air or the oxygen-rich environment. The application of these microfluidic blood oxygenators was not only limited to preterm neonates but also can be used to develop LADs for adult patients. / Thesis / Doctor of Philosophy (PhD)

Background- Mediastinal staging is the rate-limiting step prior to initiation of lung cancer treatment and is essential in identifying the most appropriate treatment for the patient. However, this process is often complex and involves multiple imaging modalities including invasive and non-invasive methods for the assessment of lymph nodes in the mediastinum which are error prone. The use of Artificial Intelligence may be able to provide more accurate and precise measurements and eliminate error associated with medical imaging. Methods-This thesis was conducted in three parts. In Part 1, we synthesized and critically appraised the methodological quality of existing studies that use Artificial Intelligence to diagnosis and stage lung cancer from thoracic imaging based on lymph node features. In Part 2, we determined the inter-rater reliability of segmentation of the ultrasonographic lymph node features performed by an experienced endoscopist (manually) compared to NeuralSeg (automatically). In Part 3, we developed and validated a deep neural network through a clinical prediction model to determine if NeuralSeg could learn and identify ultrasonographic lymph node features from endobronchial ultrasound images in patients undergoing lung cancer staging. Results- In Part 1, there were few studies in the Artificial Intelligence literature that provided a complete and detailed description of the design, Artificial Intelligence architecture, validation strategies and performance measures. In Part 2, NeuralSeg and the experienced endosonographer possessed excellent inter-rater correlation (Intraclass Correlation Coefficient = 0.76, 95% CI= 0.70 – 0.80, p<0.0001). In Part 3, NeuralSeg’s algorithm had an accuracy of 73.78% (95% CI: 68.40% to 78.68%), a sensitivity of 18.37% (95% CI: 8.76% to 32.02%) and specificity of 84.34% (95% CI: 79.22% to 88.62%). Conclusions- Analysis of staging modalities for lung cancer using Artificial Intelligence may be useful for when results are inconclusive or uninterpretable by a human reader. NeuralSeg’s high specificity may inform decision-making regarding biopsy if results are benign. Prospective external validation of algorithms and direct comparisons through cut-off thresholds are required to determine their true predictive capability. Future work with a larger dataset will be required to improve and refine the algorithm prior to trials in clinical practice. / Thesis / Master of Science (MSc) / Before deciding on treatment for patients with lung cancer, a critical step in the investigation is finding out whether the lymph nodes in the chest contain cancer cells. This is accomplished through medical imaging of the lymph nodes or taking a biopsy of the lymph node tissue using a needle attached to a scope that is entered through the airway wall. The purpose of these tests is to ensure that lung cancer patients receive the optimal treatment option. However, imaging of the lymph nodes is heavily reliant on human interpretation, which can be error prone. We aimed to critically analyze and investigate the use of Artificial Intelligence to enhance clinician performance for image interpretation. We performed a search of the medical literature for the use of Artificial Intelligence to diagnosis lung cancer from medical imaging. We also taught a computer program, known as NeuralSeg, to learn and identify cancerous lymph nodes from ultrasound imaging. This thesis provides a significant contribution to the Artificial Intelligence literature and provides recommendations for future research.

In addition to ventilation practices, the application of water via sprays is the most economical and popular means of combating respirable dust in an underground coal mine. Due to a noticeable increase in black lung among coal miners and new dust regulations, surfactants or wetting agents have been used to aid in dust suppression. The surfactant facilitates the wetting process by lowering the surface tension and allowing the hydrophobic coal dust to come into contact with the water. One of the most straightforward and effective benchtop tests is a simple wetting test. Although there are variations of this type of test, principle and technique remain the same. A known amount of dust was placed on the surface of a solution and the time it takes for all the dust to fall through the interface would be the wetting rate. This investigation examined the specific density of the bulk dust and concentration of a surfactant in solution and their effects on the wetting rate. It was found that both factors were significant in determining the wetting rate. It was seen that the surfactant had a more significant effect on the dust which consisted mostly of coal particle when compared to a dust with a higher non-coal mineral content. Additionally, full-scale tests were conducted to determine the effect of the surfactant at a constant concentration. During the field implementation, the surfactant was pumped through the mines spray water to the cutter heads of the continuous miner. A large number of uncontrollable variables present during the implementation, made determining the effects difficult, and the resulting impact from the surfactant inconclusive. Further long-term testing would be needed while accounting for all of the identified variables. Significantly higher concentration was however found when using the continuous personal dust monitor as opposed to the older personal dust samples when left in the same environment. Additionally, a very significant drop in dust concentrations was observed when the miner operators were allowed to activate the scrubbers. / Master of Science