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(2022)ThesisExperimental and probabilistic methods were used to assess the risk of exposure to Legionella sp from aerators used in groundwater treatment plants. Factors considered include an assessment of conditions conducive to Legionella growth, detachment and inhalation by operators; the use of coupon studies to understand temporal changes and biofilm formation; and modelling the risk of Legionella using iterative Bayesian networks (BNs). A survey of 13 groundwater treatment plants (GWTPs) aerators, including tray, open and semi-enclosed systems were identified to feature design and operational risk factors favouring elevated levels of nutrients, water stagnation, challenging water quality, aerosolisation, and inconsistent operation and maintenance. Based on these observations, design considerations for the next generation of safer aerators that can overcome identified Legionella risks factors were outlined. Analysis of 300 sampling events from the aerators over five years indicated an average of 7% increase in colony counts between the inlet and outlet, indicating growth of Legionella within the aerators. In total, 28% of all samples collected from aerator surfaces testing positive for Legionella. However, there was no correlation between the type of aerator and Legionella positivity. Coupons were placed in aerators to assess temporal changes in fouling developed after 6 weeks of operation. The biological activity per unit area (ATP/cm2) was higher for samples collected on the sprayed (vertically placed) coupons (277 ng ATP/cm2) compared with the submerged (horizontally laid) (73 ng ATP/cm2) coupons. Concentrations of dissolved organic carbon (DOC) in the biofilm formed on the coupons were statistically similar for the two tested conditions. Comparing fouling characteristics from the lab and full-scale coupons confirmed the impact of surface orientation and influent characteristics on biofilm formation. In terms of cleaning of the fouled surface, NaOCl at (concentration greater than 6%) was found to achieve 99.9% efficiency in biofilm inactivation. Oxalic acid (concentration greater than 1%) significantly removed inorganic materials like iron and manganese. Combining biocides and antiscalants was therefore recommended to efficiently address fouling challenges in aerators. A BN which considered risk of exposure due to growth and transmission was developed using a fishbone diagram and bowtie analysis. The initial iterative output BN model was elicited deterministically through expert weighted scoring process and discretisation approach and defined relative contributions of risk variables. The BN model also efficiently categorised and differentiated Legionella risk thresholds. A revised BN model conceptually mapped and estimated the causes and consequences of Legionella aerosolisation separately. The Legionella growth sub-model showed weak prediction accuracy with a negative kappa coefficient, signifying inconsistency in predicted and observed Legionella occurrence. The effect of water quality was further explored with a data-driven learning approach using diverse historical water quality records. The optimised BN model utilised the greedy thick thinning approach, complemented with domain knowledge, and achieved superior performance accuracy exceeding 90%. The results indicated that water temperature, free chlorine, season, and heterotrophic plate count can be utilised to track Legionella occurrence in water systems.
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(2022)ThesisThis thesis focuses on the development and applications of magnetic resonance electrical properties tomography (MREPT), which is an emerging imaging modality to noninvasively obtain the electrical properties of tissues, such as conductivity and permittivity. Chapter 2 describes the general information about human research ethics, MRI scanner, MR sequence and the method of phase-based MREPT implemented in this thesis. Chapter 3 examines the repeatability of phase-based MREPT in the brain conductivity measurement using balanced fast field echo (bFFE) and turbo spin echo (TSE) sequences, and investigate the effects of compressed SENSE, whole-head B_1 shimming and video watching during scan on the measurement precision. Chapter 4 investigates the conductivity signal in response to short-duration visual stimulus, compares the signal and functional activation pathway with that of BOLD, and tests the consistency of functional conductivity imaging (funCI) with visual stimulation across participants. Chapter 5 extends the use of functional conductivity imaging to somatosensory stimulation and trigeminal nerve stimulation to evaluate the consistency of functional conductivity activation across different types of stimuli. In addition, visual adaptation experiment is performed to test if the repetition suppression effect can be observed using funCI. Chapter 6 explores if resting state conductivity networks can be reliably constructed using resting state funCI, evaluates the consistency of persistent homology architectures, and compares the links between nodes in the whole brain. Chapter 7 investigates the feasibility of prostate conductivity imaging using MREPT, and distinctive features in the conductivity distribution between healthy participants and participants with suspected abnormalities.
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(2022)ThesisWith the rapid development of various geospatial technologies including remote sensing, mobile devices, and Global Position System (GPS), spatio-temporal data are abundantly available nowadays. Extracting valuable knowledge from spatio-temporal data is of crucial importance for many real-world applications such as intelligent transportation, social services, and intelligent distribution. With the fast increase of the amount and resolution of spatio-temporal data, traditional data mining methods are becoming obsolete. In recent years, deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have made promising achievements in many fields based on the strong ability in automated feature extraction and have been broadly used in different spatio-temporal data mining tasks. Many methods have been developed, and more diverse data were collected in recent decades, however, the existing methods have faced challenges from multi-source geospatial data. This thesis investigates four efficient techniques in different scenarios for spatio-temporal data mining that take advantage of multi-source geospatial data to overcome the limitations of traditional data mining methods. This study investigates spatio-temporal data mining from four different perspectives. Firstly, a multi-elemental geolocation inference method is proposed to predict the location of tweets without geo-tags. Secondly, an optimization model is proposed to detect multiple Areas-of-Interest (AOIs) simultaneously and solve the multi-AOIs detection problem. Thirdly, a multi-task Res-U-Net model with attention mechanism is developed for the extraction of the building roofs and the whole building shapes from remote sensing images, then an offset vector method is used to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. Lastly, a novel decoder fusion model is introduced to extract interior road network from remote sensing images and GPS trajectory data. And this method is effective for multi-source data mining. The proposed four methods use different techniques for spatio-temporal data mining to improve the detection performance. Numerous experiments show that the techniques developed in this thesis can detect ground features efficiently and effectively and overcome the limitations of conventional algorithms. The studies demonstrate that exploiting spatial information from multi-source geospatial data can improve the detection accuracy in comparison with single-source geospatial data.
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(2022)ThesisLess than 10% of patients with pancreatic ductal adenocarcinoma (PDAC) survive more than 5 years. One of the characteristic features that drive the aggressive nature of PDAC is its multicellular, heterogeneous, and fibrotic microenvironment. We previously identified a cytoskeletal protein, βIII-tubulin, as a novel therapeutic target in PDAC. However, the PDAC cell survival mechanisms controlled by βIII-tubulin were previously unknown. We also identified a major gap in the ability of human PDAC preclinical models to accurately mimic the 3D multicellular architecture and stroma of the disease. Thus, the aims of this work were (1) to evaluate the pro-survival role of βIII-tubulin in PDAC; (2) to establish a new patient derived tumour explant model that maintains all features of the PDAC microenvironment; and (3) to use the tumour explant model to test the clinical potential of silencing βIII-tubulin expression as well as two stromal targets that had been previously explored by our lab: solute carrier 7A11 (SLC7A11) and heat shock protein 47 (HSP47) Here, we identified that silencing βIII-tubulin in pancreatic cancer cells activated extrinsic apoptosis and increased their sensitivity to extrinsic apoptosis inducers including tumour necrosis factor-α (TNFα), Fas-ligand (FasL), and TNF-related apoptosis inducing factor (TRAIL). We next established the patient derived PDAC tumour explant model. We cultured whole-tissue tumour explants from PDAC patients for 12 days and demonstrated that explants maintained their 3D multicellular architecture, proliferative state, and collagen fibrosis. We also demonstrated the ability to deliver chemotherapeutics and siRNA-nanoparticles to the tumour explants. Finally, we tested the utility of this model to investigate the clinical potential of silencing three different therapeutic targets. We showed that therapeutic silencing of βIII-tubulin combined with TRAIL increased extrinsic apoptosis, decreased cell proliferation, and decreased tumour cell number. Inhibition of the stromal target SLC7A11 reduced tumour cell number and inhibited activity of stromal cancer-associated fibroblasts. Silencing of another target, HSP47, also led to a reduction in tumour cells and decreased cell proliferation. Overall, this work has discovered a previously unexplored role of βIII-tubulin as a brake on extrinsic cell death and has developed a new human PDAC preclinical model with utility in the drug development and precision medicine pipeline.
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(2022)ThesisCardiovascular disease (CVD) is the leading cause of global mortality. Two forms of CVD are acute coronary syndromes (ACS) and heart failure (HF). Patients with either are prone to repeat hospitalisations, which are detrimental to both patients and the healthcare system. Traditional care models are suboptimal in preventing readmissions. Mobile health interventions (MHIs) are attractive due to the computing power and convenience of the smartphone. Firstly, the literature regarding MHIs in CVD is systematically reviewed and meta-analysed. MHIs improved medication adherence in ACS patients and hospitalisation rates in HF patients. The review noted limitations of published trials and identified features of successful MHIs, which were incorporated into the design of a novel smartphone app-based model of care (TeleClinical Care, TCC). TCC allows home measurement of blood pressure, heart rate and weight by patients. The readings are automatically transmitted to a central server, where clinicians can identify abnormalities and intervene accordingly. A pilot RCT comparing TCC and usual care (UC) to UC alone was performed (n=164). Patients using TCC had fewer readmissions at 6 months (41 vs. 21, hazard ratio 0.51, P= 0.015), and were more likely to be adherent with medications (75% vs. 50%, P= 0.001) and complete cardiac rehabilitation (39% vs. 18%, odds ratio 2.9, P= 0.02) compared to patients in the control arm. A process evaluation of the RCT was subsequently undertaken, which identified several contributory factors to TCC’s success, such as a helpful orientation protocol for team members, and high background rates of HF outreach service and cardiologist follow-up in both trial arms. Via a series of interviews, methods to improve the future delivery of TCC were identified, particularly relating to its integration into mainstream healthcare. Patterns of smartphone ownership among cardiac inpatients were also examined. Age, sex, diagnosis, and private health insurance subscription influenced smartphone ownership. These data will help identify patients who may be excluded from MHIs. The thesis contains a cost-effectiveness model of TCC if applied widely. When enrolment exceeds 237 patients, TCC will reduce healthcare costs relative to UC, resultant to readmission prevention. Enrolment of 500 patients is projected to save $100,000 annually. In conclusion, TCC is demonstrated as a feasible, beneficial, safe, and cost-effective intervention for patients with CVD.
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(2022)ThesisSecreted by pancreatic β-cells, insulin is the major anabolic hormone, regulating the metabolism of fats, proteins, and carbohydrates. Defects in insulin production or action can lead to diabetes characterized by derangements in glucose handling and metabolic disease. Diabetes affects 420 million people worldwide, increasing morbidity, mortality and placing a burden on healthcare of nations. There is a need for rapid and accurate monitoring of insulin levels to optimize diabetes management and facilitate early diagnosis of insulin related chronic diseases. Conventional strategies such as HPLC, MALDI-TOF, ELISA, etc. used for insulin detection are not suitable for point-of-care testing (POCT) as they are expensive, and require sample preparation, sophisticated instruments, and skilled personnel. Our goal was to develop techniques to allow POCT for insulin in real time. In this study we developed two lateral flow assays (LFAs) based POCT platforms using aptamers as the biorecognition molecules for colorimetric and fluorescent detection of insulin. A range of conditions were tested such as concentrations of aptamers, reporter molecules used, volume of sample required, and assay time to obtain quantify insulin levels using a standard LFA reader. The colorimetric LFAs had linear detection range of 0.01-1 ng.mL-1 and LOD of 0.01 ng.mL-1. The fluorescent LFAs exhibited a linear detection range of 0-4 ng.mL-1 and 0.1 ng.mL-1 LOD. Various signal amplification strategies were incorporated, ie., gold-silver amplification technique and rolling circular amplification (RCA) to further enhance the signal. The developed colorimetric LFAs were successfully used for insulin quantification in rat blood, human blood, and human saliva samples. Although insulin levels were quantified within 12 min, some issues arose such as coagulation, need for dilution, and non-uniform flow through the test strips. Further work is required to optimize blood handling to progress an insulin POCT in real time. Future work could develop a multiplexed strip for detection of different analytes such as HbA1c, glucose, and C-peptide for better management of diabetes, along with a smartphone reader App. This research goes some way to addressing the challenge of providing a reliable and rapid approach for highly sensitive and specific detection of insulin for POCT applications.
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Antimicrobial Polymers as Adjuvants/Synergists of Antibiotics to Combat Multidrug-Resistant Bacteria(2022)ThesisThe emergence of multidrug-resistant (MDR) bacteria due to the overuse and misuse of antibiotics in the medical and agricultural sectors has now become a critical global healthcare issue. Antimicrobial peptides (AMPs) and synthetic mimics thereof have shown promise in combating MDR bacteria effectively, mainly because of their mechanism of action that disrupts bacteria cell membrane, consequently hindering resistance development in bacteria. However, these antimicrobials also exhibit toxicity to healthy mammalian cells at high dosage. To overcome this toxicity issue, the application of combination therapy alongside traditional antibiotics could enable the administration of these membrane-disrupting antimicrobials at lower dosage. Herein, this thesis investigates the synergetic effects of new tri-systems for combination therapy against Gram-negative bacteria which contain: i) an AMP (colistin methanesulfonate); ii) an antimicrobial polymer as AMP mimic and; iii) commercial antibiotics. It was found that colistin and the antimicrobial polymer could combine synergistically with any of the three antibiotics, doxycycline, rifampicin, and azithromycin, against wild type and MDR strains of Pseudomonas aeruginosa. Crucially, given the lower dosage of antimicrobial polymer used in these combination systems, the therapeutic index (also known as selectivity index), which is an indicator of an antimicrobial system to preferentially target bacteria over mammalian cells, is higher than the standalone agents. Furthermore, in this thesis, other selected antimicrobial polymers that are active toward mycobacteria instead of Gram-negative were also investigated as potential adjuvants or synergists to potentiate the antimicrobial activity of antibiotics against Mycobacterium smegmatis via a two-component system. Among the different families of antibiotics screened, it was found that these polymers only act as adjuvants of aminoglycosides. Overall, this thesis yields valuable new insights on combination therapy that will be useful toward combating MDR bacteria in clinical settings.
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(2022)ThesisThe arteriovenous fistula (AVF) is a vasculature created for end-stage renal disease patients who undergo haemodialysis. This vasculature is often affected by stenosis in the juxta-anastomotic (JXA) region and the presence of disturbed haemodynamics within the vessel is known to initiate such diseased conditions. A novel treatment involving the implantation of a flexible stent in the JXA region has shown potential for retaining healthy AVFs. Only a limited number of experimental studies have been conducted to understand the disturbed flow conditions, while the impact of stent implantation on the haemodynamics within the AVF is yet to be explored. The study was initiated by developing a benchtop patient-specific AVF model to conduct a Tomographic Particle Image Velocimetry (Tomo-PIV) measurement. The subsequent temporally resolved volumetric velocity field was phase-averaged to quantify fluctuations occurring over the inlet pulsatile conditions. It was noted that high turbulent kinetic energy (TKE) was generated at the JXA region. To study the effects of the stent implantation, Large Eddy Simulations (LES) comparing the AVF geometry with and without the presence of the stent implantation were conducted. The trajectory of the flow in the stented case was funnelled within the stent encapsulated region which in turn, contained the disturbed flow within the stent lumen while mitigating the generation of turbulence. Consequently, the distribution of adverse wall shear stress (WSS) in the stented region was much lower compared to that of the `stent-absent' case. Simulations were also conducted on the diseased patient AVF, before the stent implantation, to make an overall assessment of the effect of treatment. Larger and persistent regions of high TKE were noted in the vessel downstream of the stenosis despite the lower velocity of flow in the diseased model. In summary, the stent implantation in the patient AVF showed the ability to funnel flow disturbances away from the vessel wall, thereby leading to lower adverse WSS distributions. The presence of the stent also mitigated turbulence generation. These findings provide valuable insight into the favourable haemodynamic effects of this novel endovascular procedure, thus, substantiating this treatment strategy to treat vascular disease in AVFs.
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(2022)ThesisClinical data are highly complex and pose challenges to machine learning that can introduce bias or negatively affect performance. Clinical data are typically high-dimensional and of mixed types, they may contain correlated values and missing information and a large proportion of the data is often irrelevant. Clinical measurements are often repeated over time, and the data may be censored, meaning the disease of interest has not yet been observed. Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is ultimately fatal and has no cure. There are more than 55 million people worldwide living with dementia today, with AD thought to account for 60-70% of those cases, and numbers are forecast to triple by 2050. The pathological processes leading to AD begin decades before overt symptoms appear, presenting an opportunity to determine early biomarkers that might help identify individuals at risk of developing AD. Traditionally the Cox proportional hazards model has been used to analyse censored data. But the Cox model does not scale well to high dimensions and is limited by some strict assumptions. Consequently, machine learning algorithms have been adapted to handle censored data. This thesis performs a thorough comparison of the performance and stability of the available machine learning and feature selection methods for survival analysis, identifying their strengths and weaknesses. Some of these methods can be unstable in the presence of high-dimensional or correlated data. This thesis examines the reasons for these instabilities and develops new ensemble feature selection frameworks to improve the stability of feature selection. Data-driven thresholds are also developed to automatically separate the important from the redundant features, and clustering is used to handle correlated features. Improvements in stability of up to 40% are achieved. Clinical data is often collected repeatedly over time. A novel temporal pattern mining algorithm is developed to analyse this temporal data and is combined with temporal abstraction to find patterns common to those who develop AD. Survival analysis shows that these patterns are predictive of AD, with a C-Index of up to 0.74, and a novel visualisation module displays the clinically relevant results in an easily interpretable way.