Medicine & Health

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Now showing 1 - 9 of 9
  • (2021) Collins, Scott
    Chronic liver diseases including cirrhosis and primary liver cancer are a significant health burden worldwide. Liver cirrhosis is end stage liver injury resulting in a progressive fibrosis phenotype, in which the hepatic architecture is distorted. The most common cause of cirrhosis is chronic liver injury caused by hepatitis B, alcohol related liver disease, hepatitis C or non-alcoholic steatohepatitis. Primary liver cancer is a leading cause of cancer mortality globally and is commonly observed as a progression of liver cirrhosis. Liver injury usually occurs because of immune-mediated or direct injury to the hepatocytes and involves multiple cellular subsets; including hepatic stellate cells, liver adipocytes, liver resident macrophages kupffer cells, endothelial cells and infiltrating immune cells. Injury to these cells result in the release of reactive oxygen species, proinflammatory signals, proliferation-associated cytokines, and the activation of repair pathways. A chronic activation of these signals can result in dysregulation of the normal repair response and generation of a pathogenic fibrotic response. A broadly canonical response, chronic inflammation drives fibrosis and cirrhosis irrespective of liver injury aetiology. The burden of liver disease provides the impetus to pursue the use of representative in vitro models of liver function and responses to injury. Improved 2D and 3D in vitro disease models would enhance our understanding of the causes of liver injury and the development of cirrhosis and primary liver cancer while increasing the efficacy of preclinical drug discovery. Current 2D in vitro assays based on cell lines such as HepG2 that have reduced metabolic capacities compared to primary hepatocytes ex vivo, and the use of primary human hepatocytes suffers from high donor-to-donor variation and only retain in vivo characteristics for a short time ex vivo. The shortcomings of 2D cell culture models have driven the development of 3D cell culture techniques. The advantages of 3D models include replicating the complex attributes of the liver beyond liver specific metabolism, such as increased cell density, organisation, and cell-cell signalling, O2 zonation, as well as the anatomy of the liver lobule and the circulatory system. After a comprehensive review of all the current in vitro models of the liver we hypothesised that a liver organoid cell culture model co-cultured with myofibroblast like hepatic stellate cells can model liver injury. An organoid cell culture is defined as a collection of cells culturing several cell types that develop from stem cells or organ progenitors and self-organise through cell sorting and spatially restricted lineage commitment, similar to organogenesis in vivo. Liver organoids have demonstrated many advantages over conventional in vitro models such as long-term genetic stability, 2D in vivo-like organisation, and maintaining the necessary cellular cross talk and behavioural characteristics of their primary corresponding cells. The focus of this thesis is the application of 3D liver organoids to model and analyse the molecular and cellular effects of liver injury. We established a 3D liver organoid cell culture model from primary mouse tissue and characterised the capacity of these organoids to model liver characteristics in vitro and used this model to define the interactions between organoid hepatocytes and hepatic stellate cells in a co-culture trans-well system. The impact of inflammatory cytokines tumour necrosis factor-α and transformation growth factor-β on this model, as well as other variables such as hypoxia and the anti-fibrosis drug Halofuginone were assessed. Hepatic stellate cell dependent decreases in organoid viability and organoid dependent increases in hepatic stellate cell viability were observed, as well as Halofuginone dependent decreases in hepatic stellate cell viability were also observed. Markers characteristic of liver injury and fibrosis, such as Actn1 and Lamb3 were upregulated in hepatic stellate cells, although collagen expression was downregulated in these cells. Transcriptional profiling revealed a tumour necrosis factor-α mediated apoptotic response in organoids and an inflammatory response in both the organoids and hepatic stellate cells. We concluded that while liver organoids and hepatic stellate cells responded to experimental variables, there were limitations when it came to the cross talk between the cultures in the trans-well system. While apoptotic bodies from the organoids may have stimulated proliferation of hepatic stellate cells, many key genes responsible for liver injury were either not upregulated or were downregulated in co-culture. Electron microscopy analysis of liver organoids showed important ultrastructural changes compared to a whole liver section. Our findings of secreted exosomes, microvilli within the lumen of the organoids, and many ultrastructural features found within liver cells in vivo confirm that our 3D liver organoids closely resemble the liver. We also demonstrated how the use of high-resolution field emission scanning electron microscopy with automated scan resolution can generate a high-resolution ultrastructure map of the whole organoid. This method can also be combined with correlative light electron microscopy for immunofluorescent labelling of proteins of interest using quantum dot nanoparticles. Overall, our 3D organoid model of liver injury had encouraging results and furthering our understanding of pathogenesis of liver fibrogenesis in vitro and the study of novel anti-fibrotic therapeutic agents.

  • (2022) Yunana, Danladi
    Experimental 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.

  • (2022) Li, Bingnan
    With 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.

  • (2022) Kokkinos, John
    Less 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.

  • (2022) Indraratna, Praveen
    Cardiovascular 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.

  • (2022) Shao, Ethan
    The 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.

  • (2022) Gunasekera, Sanjiv
    The 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.

  • (2021) Ng, Olivia
    The arteriovenous fistula (AVF) is a surgically-made vascular structure connecting an artery to a vein. It is the optimal form of vascular access for haemodialysis-dependent end-stage renal disease patients. However, AVF are prone to access dysfunction through the formation of stenoses, which compromise the structure’s utility. To date, a plethora of clinical models are used to predict AVF formation failure based on patient factors and other models predicting late AVF failure by assessing haemodynamics and quantifying disturbed flow behaviours and wall shear stress metrics with stenosis formation. That said, inconsistencies were identified in the correlation between these metrics and diseased AVFs. This thesis aims to assess the suitability of another haemodynamic-related metric, resistance, derived from pressure drop and flow rates through patient-specific CFD modelling, for diagnosing and predicting AVF failure. A three-dimensional ultrasound scanning system was used to obtain patient-specific geometry and flow profiles, used for CFD models which were then analysed, with resistance calculated for each patient. The significance of patient-specific CFD modelling was demonstrated in its usefulness to generate a patient-targeted indicator of diseased AVF. To study the effectiveness of resistance as a metric, the relationship between CFD-derived resistance and the potential for AVF failure was evaluated, starting with classification of resistance results among patients who had undergone treatment for stenosis. An exploratory study into the suitability of CFD-derived resistance and its association with patients’ AVF conditions was further conducted by classifying data from a larger patient dataset and fitting the classified data to a multilevel regression model. CFD-derived resistance was found to be higher at the proximal vein of problematic AVF, however this figure was 76% lower among patients who had undergone stenosis treatment. Meanwhile, no correlation was found between resistance at the proximal artery and patency status. An area under curve of 92.1% was found from the receiver operating characteristic analysis, noting an outstanding discrimination of the classification. CFD-derived resistance appears to be a promising metric in the assessment of a suitable diagnostic marker for AVF failure. This research concludes with aspirations for clinical implementation of a related system, alongside routine surveillance of AVF.

  • (2022) Spooner, Annette
    Clinical 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.