New prognostic factors in chronic myeloid leukaemia

The tyrosine kinase inhibitor imatinib mesylate (IM) has proved a major advance in the management of patients with chronic myeloid leukaemia (CML) but about 15% of patients do not achieve complete cytogenetic responses (CCyR) (primary resistance) and a further 15-20% of those who do achieve CCyR eventually lose their response (secondary resistance). The best characterised mechanism of resistance is the expansion of a Ph-positive clone bearing an amino-acid substitution in the BCR-ABL1 kinase domain (KD). We screened over 300 CML patients for KD mutations and demonstrated that detection of a mutation after achieving CCyR is an independent prognostic factor for the loss of response and disease progression regardless of the level of mutant clone. In contrast this study found no difference in the level of IM-induced reduction of phospho-Crkl in diagnostic CD34+ cells from patients who achieved CCyR compared with those who failed to achieve such response. However, I also studied levels of human organic cation transporter 1 (hOCT1), a membrane protein responsible for facilitating entry of IM into cells. I showed that the level of hOCT1 at diagnosis predicted for 3 or more log reduction in BCR-ABL1 transcript level in the patients who achieved CCyR. I investigated the incidence of polymorphisms in the TP53 and MDM2 genes and showed an association between the TP53 P72R SNP and earlier age of onset of CML and an association between the MDM2 309 SNP and Sokal score in CML patients. Finally, I used array comparative genomic hybridisation to compare patterns in blood specimens obtained from 20 CML patients before treatment with patterns obtained from the same patients after induction of CCyR with IM; I showed aberrant patterns in several specific genes, most commonly NAMPT/PBEF1 on chromosome 7. I concluded that the results of these studies provided strong evidence that CML at diagnosis was a heterogeneous disease and that methods could be further refined to develop a model that would predict response to a given dose of IM

Infrared chemical imaging for pathology and forensic biology

University of Technology, Sydney. Faculty of Science.The objective of this research was to explore the capability of Fourier Transform Infrared Chemical Imaging (FTIR CI) for two specific pathology applications: 1) the analysis of human tissues for the diagnosis of melanoma, and 2) incised skin wound age determination for the purpose of forensic investigation. For the melanoma study, thin serial sections were obtained from an archival tissue bank that consisted of pathologist pre-diagnosed (“gold-standard”), paraffin-embedded, human skin and lymph node tissues. Thin sections from each block were mounted on infrared reflective microscope slides and imaged, and a selection of the total images nominated as either training or test samples. Each training sample image was then compared to its corresponding haematoxylin and eosin (H&E)-stained section and reference library spectra extracted. Vertex component analysis (VCA) as a spectral feature extraction method was also explored. Classification of the test sample images was then performed using the spectral angle mapper (SAM) algorithm and the accuracy assessed by comparing the resulting classification images to the H&E-stained tissue sections. The tissue classification model developed produced a range in result quality, and highlighted various critical aspects in the construction of such methodologies. The taking of spectral derivatives improved image classification, as did the removal of paraffin from the tissue (although no data treatment targeting the paraffin was conducted on the non-deparaffinised tissues). Although the accuracy achieved in this study fell short of that required for clinical practice, the results obtained demonstrate that further investigation into the SAM algorithm as a tissue classifying tool is certainly warranted. The second pathology application explored the ageing of wounds, a determination that may be critical in criminal investigations, particularly in homicide investigations, in which the timing of wound infliction may be crucial evidence. For this study, incised wounds were inflicted on rats in a controlled manner and the tissue excised following a known amount of healing time ranging from 5 minutes to 288 hours (12 days). Thin sections of the wounds were mounted on infrared reflective slides, deparaffinised and then imaged using FTIR CI. Although four classification models were attempted, none were capable of producing highly accurate wound age determination. Spectral variation was observed between earlier and later wound ages using some of the classification methods, but the ability to correctly group the test samples into their respective age groups was not achieved. Based on the number of variables which must be taken into consideration when performing such a study, and the number of areas identified as needing further improvement (e.g. spectral data quality), the fact that even a limited form of discrimination was achieved using FTIR CI was encouraging

A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayes

In many classification models, data is discretized to better estimate its distribution. Existing discretization methods often target at maximizing the discriminant power of discretized data, while overlooking the fact that the primary target of data discretization in classification is to improve the generalization performance. As a result, the data tend to be over-split into many small bins since the data without discretization retain the maximal discriminant information. Thus, we propose a Max-Dependency-Min-Divergence (MDmD) criterion that maximizes both the discriminant information and generalization ability of the discretized data. More specifically, the Max-Dependency criterion maximizes the statistical dependency between the discretized data and the classification variable while the Min-Divergence criterion explicitly minimizes the JS-divergence between the training data and the validation data for a given discretization scheme. The proposed MDmD criterion is technically appealing, but it is difficult to reliably estimate the high-order joint distributions of attributes and the classification variable. We hence further propose a more practical solution, Max-Relevance-Min-Divergence (MRmD) discretization scheme, where each attribute is discretized separately, by simultaneously maximizing the discriminant information and the generalization ability of the discretized data. The proposed MRmD is compared with the state-of-the-art discretization algorithms under the naive Bayes classification framework on 45 machine-learning benchmark datasets. It significantly outperforms all the compared methods on most of the datasets.Comment: Under major revision of Pattern Recognitio

Not all surveillance data are created equal—A multi‐method dynamic occupancy approach to determine rabies elimination from wildlife

1. A necessary component of elimination programmes for wildlife disease is effective surveillance. The ability to distinguish between disease freedom and non‐detection can mean the difference between a successful elimination campaign and new epizootics. Understanding the contribution of different surveillance methods helps to optimize and better allocate effort and develop more effective surveillance programmes. 2. We evaluated the probability of rabies virus elimination (disease freedom) in an enzootic area with active management using dynamic occupancy modelling of 10 years of raccoon rabies virus (RABV) surveillance data (2006–2015) collected from three states in the eastern United States. We estimated detection probability of RABV cases for each surveillance method (e.g. strange acting reports, roadkill, surveillance‐trapped animals, nuisance animals and public health samples) used by the USDA National Rabies Management Program. 3. Strange acting, found dead and public health animals were the most likely to detect RABV when it was present, and generally detectability was higher in fall– winter compared to spring–summer. Found dead animals in fall–winter had the highest detection at 0.33 (95% CI: 0.20, 0.48). Nuisance animals had the lowest detection probabilities (~0.02). 4. Areas with oral rabies vaccination (ORV) management had reduced occurrence probability compared to enzootic areas without ORV management. RABV occurrence was positively associated with deciduous and mixed forests and medium to high developed areas, which are also areas with higher raccoon (Procyon lotor) densities. By combining occupancy and detection estimates we can create a probability of elimination surface that can be updated seasonally to provide guidance on areas managed for wildlife disease. 5. Synthesis and applications. Wildlife disease surveillance is often comprised of a combination of targeted and convenience‐based methods. Using a multi‐method analytical approach allows us to compare the relative strengths of these methods, providing guidance on resource allocation for surveillance actions. Applying this multi‐method approach in conjunction with dynamic occupancy analyses better informs management decisions by understanding ecological drivers of disease occurrence

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning

Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients’ clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients

Novel investigations and treatment outcomes in systemic AL amyloidosis

Background Systemic AL amyloidosis (AL) is a potentially fatal disorder characterised by fibrillary deposition of monoclonal immunoglobulin light chains within organs. Outcomes in AL have improved, but ~20% of patients die within six months of diagnosis. Aims Since upfront bortezomib-based therapy is the mainstay of contemporary AL management, I aim to assess outcomes with this therapy in a large UK cohort. Within this cohort, I shall explore the impact of achieving a ‘stringent’ light chain response. I also aim to explore outcomes in AL patients with advanced cardiac involvement, and patients who do not achieve early haematological responses to upfront therapy. Few AL patients are eligible for upfront autologous stem cell transplantation (ASCT) due to advanced cardiac involvement, and I shall explore the role of deferred ASCT in initially transplant-ineligible patients. I shall examine treatment outcomes with rituximab-bendamustine in IgM-associated AL. AL patients with severe neuropathic involvement have limited chemotherapeutic options, and I shall assess outcomes with the second-generation proteasome inhibitor, carfilzomib, in such patients. The final aim of this thesis is to perform a pilot study of 18F-florbetapir, a novel imaging tracer, in cardiac AL. Results and conclusion Bortezomib-based therapy results in good overall survival and time-to-next-treatment in AL. A ‘stringent’ light chain response predicts prolonged time-to-next-treatment and excellent organ responses. Patients who do not achieve early haematological responses with bortezomib can still go on to achieve excellent haematological responses. AL patients with advanced cardiac involvement who achieve rapid, deep haematological responses have survival outcomes that are better than previously reported. Deferred ASCT is a potential treatment option in initially transplant-ineligible AL patients. Rituximab-bendamustine induces good haematological responses in IgM-associated AL. Carfilzomib is an effective treatment option in AL patients with significant neuropathic involvement. 18F-florbetapir PET is associated with cardiac uptake in AL patients with cardiac involvement

Raman spectroscopy for point of care urinary tract infection diagnosis

Urinary tract infections (UTIs) are one of the most common bacterial infections experience by humans, with 150 million people suffering one or more UTIs each year. The massive scale at which UTIs occurs translates to a tremendous health burden comprising of patient morbidity and mortality, massive societal costs and a recognised contribution to expanding antimicrobial resistance. The considerable disease burden caused by UTIs is severely exacerbated by an outdated diagnostic paradigm characterised by inaccuracy and delay. Poor accuracy of screening tests, such as urinalysis, lead to misdiagnosis which in turn result in delayed recognition or overtreatment. Additionally, these screening tests fail to identify the causative pathogen, causing an overreliance on broad-spectrum antimicrobials which exacerbate burgeoning antimicrobial resistance. While diagnosis may be accurately confirmed though culture and sensitivity testing, the prolonged delay incurred negates the value of the information provided doing so. A novel diagnostic paradigm is required that that targets rapid and accurate diagnosis of UTIs, while providing real-time identification of the causative pathogen. Achieving this precision management is contingent on the development of novel diagnostic technologies that bring accurate diagnosis and pathogen classification to the point of care. The purpose of this thesis is to develop a technology that may form the core of a point-of-care diagnostic capable of delivering rapid and accurate pathogen identification direct from urine sample. Raman spectroscopy is identified as a technology with the potential to fulfil this role, primarily mediated though its ability to provide rapid biochemical phenotyping without requiring prior biomass expansion. Raman spectroscopy has demonstrated an ability to achieve pathogen classification through the analysis of inelastically scattered light arising from pathogens. The central challenge to developing a Raman-based diagnostic for UTIs is enhancing the weak bacterial Raman signal while limiting the substantial background noise. Developing a technology using Raman spectroscopy able to provide UTI diagnosis with uropathogen classification is contingent on developing a robust experimental methodology that harnesses the multitude of experimental and analytical parameters. The refined methodology is applied in a series of experimental works that demonstrate the unique Raman spectra of pathogens has the potential for accurate classification. Achieving this at a clinically relevant pathogen load and in a clinically relevant timeframe is, however, dependent on overcoming weak bacterial signal to improve signal-to-noise ratio. Surface-enhanced Raman spectroscopy (SERS) provides massive Raman signal enhancement of pathogens held in close apposition to noble metal nanostructures. Additionally, vacuum filtration is identified as a means of rapidly capturing pathogens directly from urine. SERS-active filters are developed by applying a gold nanolayer to commercially available membrane filters through physical vapour deposition. These SERS-active membrane filter perform multiple roles of capturing pathogens, separating them from urine, while providing Raman signal enhancement through SERS. The diagnostic and classification performance of SERS-active filters for UTIs is demonstrated to achieve rapid and accurate diagnosis of infected samples, with real-time uropathogen classification, using phantom urine samples, before piloting the technology using clinical urine samples. The Raman technology developed in this thesis will be further developed toward a clinically implementable technology capable of ameliorating the substantial burden of disease caused by UTIs.Open Acces