Building trajectories through clinical data to model disease progression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modeling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This thesis describes the application of intelligent data analysis techniques for extracting information from time series generated by different diseases. The aim of this thesis is to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms. It explores the use of a bootstrap technique that fits trajectories through the data generating “pseudo time-series”. It addresses issues including: how clinical variables interact as a disease progresses along the trajectories in the data; and how to automatically identify different disease states along these trajectories, as well as the transitions between them. The thesis documents how reliable time-series models can be created from large amounts of historical cross-sectional data and a novel relabling/latent variable approach has enabled the exploration of the temporal nature of disease progression. The proposed algorithms are tested extensively on simulated data and on three real clinical datasets. Finally, a study is carried out to explore whether we can “calibrate” pseudo time-series models with real longitudinal data in order to improve them. Plausible directions for future research are discussed at the end of the thesis

Development of Techniques for Analysis of the Human Retinal Ganglion Cell Transcriptome: Application to the Role of Calcium in RGC Death in Glaucoma

Purpose: Irreversible retinal ganglion cell (RGC) death is the reason for visual loss in glaucoma. However, the mechanisms of RGC death remain unclear. The aim of this research was to develop methods to study mRNA expression profiles in human RGCs, then to use the data to investigate the role of calcium in RGC death. Methods: A planar sectioning technique was developed to isolate mRNA from serial sections of the human retina. QRT-PCR of neuronal markers validated the technique. Global gene expression analysis, using Illumina arrays, compared expression in the retina ganglion cell layer (RGCL) and entire macula (Mac). Immunohistochemistry and QRT-PCR validated gene array data. RGC death was investigated using a simulated ischemia (oxygen glucose deprivation, OGD) model in human organotypic retinal cultures (HORCs). Cell survival was measured by LDH, and RGC loss by immunohistochemistry and QRT-PCR. Western blot assessed proteases. Results: The sectioning technique developed enabled isolation of relatively large quantites of high quality mRNA from 20μm retinal sections from the macular region of the human retina. Marker genes for retinal neurons verified accurate profiling of gene expression across the retina. Gene arrays provided a list of genes that were most enriched in the RGCL. AHNAK2 and HSPA1B were the two most enriched genes in the RGCL. CAPN1 (calpain 1), a calciumdependent cysteine proteases, was in the gene list. Its expression was confirmed to be mainly in the inner retina. OGD caused calpain activation and induced RGC death. Two TRP channels, TRPM-2 and TRPC-3, which mediate Ca2+ influx, were found that predominantly expressed in the RGCL. Involvement in RGC death in the OGD model using the TRP inhibitor ACA could not be confirmed. Conclusions: The technique developed has enabled determination of the human RGCL transcriptome and has allowed expression profiling of gene of interest across the retina. This could prove to be a powerful tool in the investigation of pathways involved in neurodegeneration in the retina

Clinical decision support system for early detection and diagnosis of dementia

Dementia is a syndrome caused by a chronic or progressive disease of the brain, which affects memory, orientation, thinking, calculation, learning ability and language. Until recently, early diagnosis of dementia was not a high priority, since the related diseases were considered untreatable and irreversible. However, more effective treatments are becoming available, which can slow the progress of dementia if they are used in the early stages of the disease. Therefore, early diagnosis is becoming more important. The Clock Drawing Test (CDT) and Mini Mental State Examination (MMSE) are well-known cognitive assessment tests. A known obstacle to the wider usage of the CDT assessments is the scoring and interpretation of the results. This thesis introduces a novel diagnostic Clinical Decision Support System (CDSS) based on CDT which can help in the diagnosis of three stages of dementia. It also introduces the advanced methods developed for the interpretation and analysis of CDTs. The data used in this research consist of 604 clock drawings produced by dementia patients and healthy individuals. A comprehensive catalogue of 47 visual features within CDT drawings is proposed to enhance the sensitivity of the CDT in diagnosing the early stages of dementia. These features are selected following a comprehensive analysis of the available data and the most common CDT scoring systems reported in the medical literature. These features are used to build a new digitised dataset necessary for training and validating the proposed CDSS. In this thesis, a novel feature selection method is proposed for the study of CDT feature significance and to define the most important features in diagnosing dementia. iii A new framework is also introduced to analyse the temporal changes in the CDT features corresponding to the progress of dementia over time, and to define the first onset symptoms. The proposed CDSS is designed to differentiate between four cognitive function statuses: (i) normal; (ii) mild cognitive impairment or mild dementia; (iii) moderate or severe dementia; and (vi) functional. This represents a new application of the CDT, as it was previously used only to detect the positive dementia cases. Diagnosing mild cognitive impairment or early stage dementia using CDT as a standalone tool is a very challenging task. To address this, a novel cascade classifier is proposed, which benefits from combining CDT and MMSE to enhance the overall performance of the system. The proposed CDSS diagnoses the CDT drawings and places them into one of three cognitive statuses (normal or functional, mild cognitive impairment or mild dementia, and moderate or severe dementia) with an accuracy of 78.34 %. Moreover, the proposed CDSS can distinguish between the normal and the abnormal cases with accuracy of 89.54 %. The achieved results are good and outperform most of CDT scoring systems in discriminating between normal and abnormal cases as reported in existing literature. Moreover, the system shows a good performance in diagnosing the CDT drawings into one of the three cognitive statuses, even comparing well with the performance of dementia specialists. The research has been granted ethical approval from the South East Wales Research Ethics Committee to employ anonymised copies of clock drawings and copies of Mini Mental State Examination made by patients during their examination by the memory team in Llandough hospital, Cardif

Antimalarial drug design: targeting the plasmodium falciparum cytochrome bc1 complex through computational modelling, chemical synthesis and biological testing

Malaria is a life-threatening disease which is responsible for roughly one million deaths annually. Previous successes in attempting to eradicate the disease have only been short lived, owing to the increased development of resistance in the parasite. There is a continued need for novel compounds which act at novel therapeutic targets, with the Plasmodium falciparum cytochrome bc1 complex (Pfbc1) representing one such target. Its inhibition halts the biochemical generation of ATP, thus resulting in parasite cell death. Work described in this thesis was concerned with utilising molecular modelling, synthesis and biological testing to develop novel antimalarial compounds, which selectively inhibit this target. The structural details of a number of compounds known to be active or inactive against Pfbc1 were used in combination with six different ligand based virtual screening techniques, and applied to the ZINC lead like library of compounds to identify potential chemotypes active against malaria. These methods included fingerprint similarity searching, principal component analysis, and naïve Bayesian classification. The hits from each of these methods were merged and formed part of a consensus analysis in which compounds identified across several methods were deemed of more interest than those which appeared less frequently. Each molecule was given a score based on its occurrence in the virtual screening methods and also its physicochemical properties. Compounds were filtered to remove those with unfavourable chemical properties, or which contained known toxicophores. 19 compounds were ultimately purchased and tested in vitro against the 3D7 strain of the malaria parasite. 5 of the compounds reported single digit µM IC50 values, with each containing novel structural chemotypes. The lead candidate contained a benzothiazole core, and reported an IC50 value against 3D7 of 4.53 ± 1.86 µM. Additional testing showed the compounds to be inactive against bovine bc1, which is promising as strong bovine bc1 inhibition has been shown to be indicative of cardiotoxicity in humans. Molecular docking was extensively employed to rationalise the activity of Pfbc1 inhibitors such as atovaquone and HDQ. A number of quinolone containing compounds were also subject to docking, with key observations made with regard to interactions thought to be crucial to their antimalarial activity. The hits from LBVS were also the focus of docking, further supporting their potential as Pfbc1 inhibitors. QSARs were developed for a series of 4-aminoquinoline compounds which had been tested against both the NF54 and K1 strains of malaria. MLR, PLS and kNN machine learning methods were investigated, with molecular descriptors contained within valid models interpreted. Significant models were identified and shown to have strong predictive abilities for both strains. QSAR models were similarly developed for a series of thiazolide compounds with activity against hepatitis C. SVM was found to give a significant model which was able to predict the cell safety of the thiazolide derivatives. The rational design of the novel pyrroloquinolone chemotype led to the synthesis of 7 synthetic analogues to investigate its SAR, via alkylation and Winterfeldt oxidation reactions. The compounds reported 3D7 activity values between 75 nM and 1.02 µM, with molecular docking supporting their potential for Qo binding and thus Pfbc1 inhibition

Molecular mechanisms of drug resistance and invasion in a human lung carcinoma cell line

Metastasis and drug resistance present as major problems to patients during cancer chemotherapy. The research outlined in this thesis aims to further our knowledge about the molecular mechanisms involved in these processes and the relationship between drug resistance and cancer invasion and/or metastasis. One explanation for the link between resistance and metastasis is that resistance facilitates tumour progression and invasion into both surrounding and distal tissues. Investigations were conducted on a clonal sub-population of poorly differentiated human lung squamous carcinoma cells (DLKP). These were pulsed with mitoxantrone and the resulting cell populations extensively characterised. Two sub-lines emerged: SQ-Mitox-BCRP and SQ-Mitox-MDR cell lines. These two cell lines typically exhibited resistance to the selecting agent (ranging approx. 210 to 320-fold). This occurred as an early event during the pulsing process. The two sublines differed in their morphology and pattern of gene expression. In addition, BCRP was significantly increased in one population (SQ-Mitox-BCRP) while P-gp was significantly increased in the other population (SQ-Mitox-MDR). A crucial step in human lung cancer progression appears to be the acquisition of invasiveness. The population of cells arising from the 4th drug pulse remained noninvasive but had acquired a high level of drug resistance. However, after two additional drug pulses, all cell lines acquired invasiveness. The invasive, drug resistant BCRP and MDR cell line variants were characterised in depth and microarray analysis was used to find functionally significant changes in the transition from the preinvasive to the invasive phenotype. Functional and cellular signaling analyses were performed on the cell lines using pharmacological inhibitors, function-blocking antibodies, and gene silencing by RNA interference. The DLKP cell line appears to contain at least three morphologically distinct sub-populations of cells with different levels of invasiveness. Microarray analysis generated gene lists that were specific to an invasive phenotype, identifying possible genetic markers for invasion. Proteins (including the cell adhesion molecules, N-cadherin and ALCAM and the axon guidance molecule SLIT2) were also identified as possible and alternative markers capable of distinguishing between the different cell line clones

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe