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Oral Inhalation Airway Geometry: Inhaled Particles for Drug Delivery
No full textAs Computational Fluid Dynamic (CFD) moves further towards virtual twin models of the pharynx for drug inhalation research, the intra-subject variability of the upper airway requires investigation to ensure realistic geometries are used. The soft palate physiology during all breathing modes was reported by Rodenstein & Stanescu (1984). It was conclusively found that the soft palate movements were consistent in all the tested subjects, rising during oral respiration and lowering during nasal breathing. In the 1990’s and 2000’s the experimental capabilities allowed for the creation of models, namely the United States Pharmecopia (USP) model (Sheinin, 2020) as well as the Lovelace model (Cheng et al., 1999). These models continue to be used in experimental and in-silico studies on drug inhalation. Virtual twins have become increasingly popular, however, their complex geometry makes them more sensitive to variations both within and between subjects. Furthermore, in many recent publications (Chaugule et al., 2022; Cheng et al., 2018), mouth-throat models appear to use nasal breathing scans for oral inhalation simulations, neglecting the findings of Rodenstein & Stanescu (1984).</p
The 51 Paintings Suite: a long term study of trauma memory and metamodern affect in slow films
No full textThe 51 Paintings Suite (2006 - 2024) is an 18 year long term study about trauma memory and slow films intersecting metamodern affect. The project recontextualised poses of characters from medieval German plague era paintings into new locations and contexts through slow films. The developmental phase of the study occurred between 2006-2012 and the application phase occurred between 2012-2024. The nature of the study results have sought to digress trauma memory through metamodernism that led to the discovery of an affectual working model which established a new way to comprehend metamodern film. The context of such established an epistemological reading of affect embodied in a structure of reason. This differs from what current scholarship determines as a structure of feeling since challenged by applying epistemological modelling through the oscillation between modernism as a singularity and postmodernism as a relativism. The resultant body of work attests to this challenging by making a significant contribution to metamodern film and contemporary art.</p
Automatic Blood Clot Detection for Extracorporeal Life Support Systems (ECMO Machines)
No full textExtracorporeal Membrane Oxygenation (ECMO) is a life support technique used to treat critically ill patients with cardiac and pulmonary failure. During ECMO, blood is taken from the patient and pumped under pressure to the oxygenator, where it is oxygenated and warmed, and finally returned to the patient. Due to the turbulent flow and contact with artificial surfaces, ECMO activates hemostasis, causing both patient-related and circuit-related clotting. Circuit clotting requiring component exchange is one of ECMO's most frequent and expensive complications. Clot formation in the circuit can result in life-threatening thromboembolisms if they detach and enter the patient. The obstruction of a blood vessel in this manner can lead to cardiac arrest and stroke. Hospitals have multiple techniques for monitoring patient and circuit thrombosis. However, our literature review found that these techniques cannot accurately measure the amount of clot formation in a circuit component. Additionally, these techniques cannot predict clot location, which we posit is vital information to reduce the number of complications in ECMO. The best time to exchange ECMO components is also unknown, with protocols varying between hospitals. Improving ECMO clot detection would provide clinicians with more information about the condition of the patient and the circuit components. This information could be used in further studies to find the optimal time to exchange ECMO components and improve patient outcomes. We focused our research on the oxygenator since it is the most challenging component to assess and the most frequent site of clot formation. To investigate the full scope of the problem, we completed an extensive literature review on the topic, subsequently published as a review paper. This paper explores why oxygenator clot detection is an essential area of ongoing research in ECMO and analyses the benefits and limitations of current hospital techniques. The paper then assesses solutions proposed by other researchers and presents two promising areas for future work. These areas are the in-depth analysis of pressure signals in the ECMO circuit and the use of real-time imaging techniques to provide continuous and accurate clot detection. Following the literature review, this thesis explores how we investigated pressure fluctuations and ultrasound imaging for clot detection in the oxygenator. We detail the construction of a mock ECMO loop to perform customizable experiments using polydimethylsiloxane (PDMS) clot phantoms. We used both real ECMO oxygenators and modifiable 3D-printed prototypes. We describe building an ultrasound clot assessment system, including the numerous electrical and software complications we overcame to produce a functional product. We use time series feature extraction and machine learning to predict the location and geometry of clots within our oxygenators, with promising results. We then evaluate our ultrasound system on animal blood, demonstrating the ability to differentiate between clotted and non-clotted pig blood. Finally, this thesis will describe our experiments on an actual ECMO circuit with human blood in collaboration with Monash University. We found that our ultrasound system potentially detected oxygenator clotting before any other hospital technique.</p
The use of green infrastructure and irrigation in the mitigation of urban heat in a desert city
No full textSevere urban heat, a prevalent climate change consequence, endangers city residents globally. Vegetation-based mitigation strategies are commonly employed to address this issue. However, the Middle East and North Africa are under investigated in terms of heat mitigation, despite being one of the regions most vulnerable to climate change. This study assesses the feasibility and climatic implications of wide-scale implementation of green infrastructure (GI) for heat mitigation in Riyadh, Saudi Arabia—a representative desert city characterized by low vegetation coverage, severe summer heat, and drought. Weather research forecasting model (WRF) is used to simulate GI cooling measures in Riyadh’s summer condition, including measures of increasing vegetation coverage up to 60%, considering irrigation and vegetation types (tall/short). In Riyadh, without irrigation, increasing GI fails to cool the city and can even lead to warming (0.1 to 0.3 °C). Despite irrigation, Riyadh’s overall GI cooling effect is 50% lower than GI cooling expectations based on literature meta-analyses, in terms of average peak hour temperature reduction. The study highlights that increased irrigation substantially raises the rate of direct soil evaporation, reducing the proportion of irrigation water used for transpiration and thus diminishing efficiency. Concurrently, water resource management must be tailored to these specific considerations.</p
My Unhallowed Arts: Hybridising and Remixing the Creation Scene from Frankenstein to Stitch Together New Screenwriting Methods
No full textMy field of research is media and communication, specifically developing new methods of screenwriting. This type of research is necessary so that creative works ‘can be seen as a legitimate and important research practice’ (Baker et al., 2015, p. 9). The methodologies I utilised were a combination of creative practice with adaptation theory. Creative practice research involves ‘making a creative work and/or in the documentation and theorisation of that work’ (Smith & Dean, 2009, p. 2). The main method I employed was iterative experiments, which involved adapting the creation scene from Mary Shelley’s novel Frankenstein in multiple iterations. This was accomplished by ‘adapting’ scholars’ research into creative practice methods and then using those methods to write the scenes. I am looking to further practice-based research and pioneer new creative practice methods and, in doing so, gain deeper understandings of genre hybridity and remix culture through the lens of adaptation studies.</p
Reducing Type 1 Childhood Diabetes in Saudi Arabia by Identifying and Modelling Its Key Performance Indicators
No full textThe increasing incidence of type 1 diabetes (T1D) in children is a growing global health concern. Reducing the incidence of diabetes generally is one of the goals in the World Health Organisation’s (WHO) 2030 Agenda for Sustainable Development Goals. With an incidence rate of 31.4 cases per 100,000 children and an estimated 3,800 new cases per year, Saudi Arabia is ranked 8th in the world for number of T1D cases and 5th for incidence rate. Despite the remarkable increase in the incidence of childhood T1D in Saudi Arabia, there is a lack of meticulously carried out research on T1D in children when compared with developed countries. In addition, it is crucial to recognise the critical gaps in current understanding of diabetes in children, adolescents, and young adults, with recent research indicates significant global and sub-national variations in disease incidence. Better knowledge of the development of T1D in children and its associated factors would aid medical practitioners in developing intervention plans to prevent complications and address the incidence of T1D. This study employed statistical, machine learning and classification approaches to analyse and model different aspects of childhood T1D using local case and control data. In this study, secondary data from 1,142 individual medical records (359-377 cases and 765 controls) collected from three cities located in different regions of Saudi Arabia have been used in the analysis to represent the country’s diverse population. Case and control data matched by birth year, gender and location were used to control confounders and create a more robust and clinically relevant model. It is well documented that genetic and environmental factors contribute to childhood T1D so a wide range of potential key performance indicators (KPIs) from the literature were included in this study. The collected data included information on socioeconomic status, potential genetic and environmental factors, and demographic data such as city of residence, gender and birth year. Several techniques, such as cross-validation, hyperparameter tuning and bootstrapping, were used in this study to develop models. Common statistical metrics (coefficient of determination, R-squared, root mean squared error, mean absolute error) were used to evaluate performance for the regression models while for the classification models accuracy, sensitivity, precision, F score and area under the curve were utilised as performance measures.Multiple linear regression (MLR), artificial neural network (ANN) and random forest (RF) models were developed to predict the age at onset of T1D for all children 0-14 years old, as well as for the most common age group for onset, the 5-9 year olds. To improve the performance of the MLR models, interactions between variables were considered. Additionally, risk factors associated with the age at onset of T1D were identified. The results showed that MLR and RF outperformed ANN. The logarithm of age at onset was the most suitable dependent variable. RF outperformed the others for the 5-9 years age group. Birth weight, current weight and current height influenced the age at onset in both age groups. However, preterm birth was significant only in the 0-14 years cohort, while consanguineous parents and gender were significant in the 5-9 age group. Logistic regression (LR), random forest (RF), support vector machine (SVM), naive Bayes (NB) and artificial neural network (ANN) models were utilised with case and control data to model the development of childhood T1D and to identify its key performance indicators. Full and reduced models were developed to determine the best model. The reduced models were built using the significant factors identified by the individual full model. The study found that full LR had the highest accuracy. Full RF and SVM with a linear kernel also performed well. Significant risk factors identified as being associated with developing childhood T1D include early exposure to cow’s milk, high birth weight, positive family history of T1D and maternal age over 25 years. Poisson regression (PR), RF, SVM and K-nearest neighbor (KNN) were then used to model the incidence of childhood T1D, taking in the identified significant risk factors. The interactions between variables were also considered to enhance the performance of the models. Both full and reduced models were created and compared to find the best models with the minimum number of variables. The full Poisson regression and machine learning models outperformed all other models, but reduced models with a combination of only two out of three independent variables (early exposure to cow’s milk, high birth weight and maternal age over 25 years) also performed relatively well. This study also deployed optimisation procedures with the reduced incidence models to develop upper and lower yearly profile limits for childhood T1D incidence to achieve the United Nations (UN) and Saudi recommended levels of 264 and 339 cases by 2030. The profile limits for childhood T1D then allowed us to model optimal yearly values for the number of children weighing more than 3.5kg at birth, the number of deliveries by older mothers and the number of children introduced early to cow’s milk. The results presented in this thesis will guide healthcare providers to collect data to monitor the most influential KPIs. This would enable the initiation of suitable intervention strategies to reduce the disease burden and potentially slow the incidence rate of childhood T1D in Saudi Arabia.The research outcomes lead to recommendations to establish early intervention strategies, such as educational campaigns and healthy lifestyle programs for mothers along with child health mentoring during and after pregnancy to reduce the incidence of childhood T1D. This thesis has contributed to new knowledge on childhood T1D in Saudi Arabia by: * developing a predictive model for age at onset of childhood T1D using statistical and machine learning models. * predicting the development of T1D in children using matched case-control data and identifying its KPIs using statistical and machine learning approaches. * modeling the incidence of childhood T1D using its associated significant KPIs. * developing three optimal profile limits for monitoring the yearly incidence of childhood T1D and its associated significant KPIs. * providing a list of recommendations to establish early intervention strategies to reduce the incidence of childhood T1D.</p
Alert and surveillance on H5N1 influenza virus: risks to agriculture and public health
No full textBird flu, primarily caused by avian influenza A viruses, poses significant pandemic threats due to antigenic drift and shift. The highly pathogenic avian influenza (HPAI) A(H5N1) virus has caused global outbreaks in birds, killing millions of wild birds and poultry, with sporadic cases in humans often being fatal with a case fatality rate of 52% (893 cases in 24 countries between 2003 and 2024, with 463 deaths) reaching all continents. The 2009 H1N1 pandemic demonstrated the risks of reassortment between human, avian and swine influenza viruses.</p
Australia's first human case of H5N1 and the current H7 poultry outbreaks: implications for public health and biosecurity measures
No full textAustralia has long been free of the highly pathogenic avian influenza (HPAI)-H5N1, but recent developments have changed this status. The first confirmed human case of HPAI-H5N1, involved a 2.5-year-old girl who contracted the virus in Kolkata India between 12 and 29 February, 2024.The case was confirmed on 18 May, 2024 and the WHO was notified on 22 May. While in India, the girl visited the doctor due to, loss of appetite, fever, cough and vomiting on 28 February and received paracetamol treatment. Upon returning to Australia on 1 March, the illness was not reported to Australian airport biosecurity. The child sought medical attention and was admitted to the ICU in Melbourne on 4 March, and discharged 2.5 weeks after initial admission. The patient was infected with clade 2.3.2.1a, common in South Asian birds, especially in Bangladesh and India. This clade is different from clade 2.3.2.1c, found in Cambodian and Vietnamese poultry, which occasionally infects humans. The case marks a significant epidemiological event, emphasising the importance of vigilance against avian influenza.</p
Mosquito-borne ross river virus: A raising concern in Queensland
No full textThe recent surge in Ross River Virus (RRV) infections in Queensland, Australia, highlights the need for increased awareness and preventive actions to combat the spread of this mosquito-borne disease. The detection of RRV in mosquitoes across Queensland has raised concerns regarding the potential for widespread transmission among the population, highlighting the importance of proactive public health strategies. Ross River Virus is endemic to Australia and Papua New Guinea, causing symptoms such as multiple joint pain, fever, fatigue, and rash. While most patients recover within weeks, some may experience prolonged joint pain. The virus is transmitted by mosquitoes, primarily Culex annulirostris and Aedes vigilax, with marsupials and birds serving as non-human reservoir hosts. Preventive measures, including the use of insect repellents and avoiding mosquito bites, are crucial for reducing the risk of RRV infection (Fig. 1).</p
Mycoplasma pneumoniae returns: understanding its spread and growing impact
No full textMycoplasma pneumoniae, atypical bacterium known for causing respiratory infections, has risen as a major public health concern worldwide. This agent, primarily responsible for 'walking pneumonia', is infamous for triggering outbreaks in densely populated areas such as schools, hospitals, and military bases. Although typically mild, its global effect is profound, with around 2 million cases reported each year. The recent increase in cases in China closely mirrors the early indications of an unidentified pneumonia outbreak in Wuhan in late 2019, which signaled the beginning of the COVID-19 pandemic. Contributing factors to these developments may include influenza, respiratory syncytial virus, SARS-CoV-2, and influenza. This article sheds light on the public health challenges brought forth by Mycoplasma pneumoniae, emphasizing its notable spread in nations including Denmark, the Netherlands, South Korea, Singapore, the USA, and Sweden, thereby highlighting its extensive global reach.</p