Bradford Scholars is the University of Bradford online research archive. Access is free to anyone interested in research being conducted at Bradford. In the repository you will find a range of materials from journal articles and conference papers to research reports and theses.
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Towards the development of an integrated case-finding tool to facilitate the review of anticholinergic prescribing for frail older peopleBackground: The cumulative effect of taking anticholinergic medicines (anticholinergic burden) is associated with adverse outcomes for older people. Prevalence of anticholinergic prescribing is increasing, and there is a need for tools to proactively identify at-risk patients for medication reviews. Aim: To explore the need for, and feasibility of, an integrated case-finding tool that predicts risks using electronic health records (EHRs), facilitating the review of anticholinergic medicines for frail older people. Methods: Mixed methods, adopting a pragmatic approach. A systematic review, prediction modelling of cohort study data, and qualitative interviews were undertaken. Results: The systematic review found anticholinergic exposure was associated with adverse outcomes for the frail; poorer physical function, falls, and mortality, indicating a need for a risk reducing intervention. In the prediction modelling study, predicting risks using composite measures of anticholinergic burden and frailty indicated limited feasibility. Neither enhanced the performance of best subset models using cohort study data. Their predictive utility needs to be investigated using EHR data, to determine their feasibility within primary care. The qualitative study found healthcare professionals needed a proactive tool, supporting risk prediction as a feasible approach. Factors influencing future implementation were; upskilling requirements, deprescribing confidence, patient reluctance, motivation, holistic care, interoperability, trust in risk prediction, remuneration, among other barriers and facilitators. Conclusions: Through identifying a need, and potential feasibility, foundations towards the future developments of a case-finding tool have been provided, informing an early tool prototype (AC-FRAIL). Recommendations for further work suggest a roadmap ahead, to maximise the potential for integrated solutions to proactively reduce anticholinergic risks.
Proteomic Investigation of Endocrine Therapy Resistance in Breast Cancer Investigating the Molecular Mechanisms for SERM Resistant Cell Lines Using SILAC-Based Proteomic ApproachIntroduction: Breast cancer is the second highest cause of cancer mortality in women worldwide. Hormonal therapy is considered one of the most effective therapies and is used against luminal-type malignancies. However, 40-50% of tumour cells can develop resistance, thereby limiting the success in breast cancer treatment. In this study, mechanisms of resistance were investigated using a novel multi-stable isotope labelled amino acids (SILAC) proteomics approach in phenotype-specific breast cancer cell lines resistant to endocrine treatment. Method: In vitro chemo-sensitivity (IC50) was determined for MCF7, T47D, MDA-MB-231, MDA-MB-468, MDA-MB-453, BT-20 and MCF-10A breast cell lines using four endocrine-based therapeutic agents (Tamoxifen, 4-Hydroxytamoxifen OHT, Raloxifene, Anastrozole) to select viable strains for resistance studies. MCF7 (luminal-type A) and MDA-MB-231 (triple negative breast cancer, TNBC) were selected and initially subject to OHT or raloxifene exposure with gradual increments for 10 months. WT cells were grown in the absence of drug in parallel as passage controls. Resistant cell lines were assessed by MTT and IF for comparison with parental cell lines. Resistant cell lines, along with the passage control and a SILAC control, were grown in “light” SILAC medium together with WT strains cultured in “heavy” SILAC medium. Proteins were extracted, concentrations determined and analysed by SDS PAGE for quality control. An aliquot of each “light” cell line (resistant, passage control or SILAC control) was combined with an equal amount of “heavy” WT, trypsin digested and analysed by nano-HPLC Orbitrap Fusion mass spectrometry (2D-LC MS/MS). Proteins were identified by database searching using MascotTM. Relative changes (resistant/WT ratio) in protein levels were determined and bioinformatics tools (STRING and UniProt) used to explore significantly changed pathways associated with resistance. Western blotting was used to verify selected target proteins. Results: Four consistently resistant sublines were generated MCF7 OHT Res (2.00-fold more resistant), MCF7 Ralx Res (2.00-fold), MDA-MB-231 OHT Res (1.90-fold change) and MDA-MB-231 Ralx Res (2.00-fold), in addition to two high passage controls. ER expression by IF was decreased in MCF7 OHT Res compared to the WT and MCF7 Ralx Res, whereas CD44 was increased. Proteomic analysis revealed 2247 and 2880 total proteins in MCF7 OHT Res and MCF7 Ralx Res whilst 3471 and 3495 total proteins were identified in MDA-MB-231 OHT Res and MDA-MB-231 Ralx Res, respectively. Bioinformatics tools identified significantly changed pathways included apoptosis, cytoskeleton, cell motility and redox cell homeostasis. Components of the MAPK-signalling cascade were consistently found to be upregulated in resistant cell lines. MAPK1 (ERK2), previously associated with tamoxifen resistance was increased in MDA-MB-231 Ralx Res cell lines by 4.45-fold and confirmed by Western blotting. Sorcin, which contributes to calcium homeostasis and is also linked to multidrug resistance was increased 4.11- and 2.35-fold in MCF7 OHT Res and Ralx Res sub cell lines, respectively. Some results, such as those for c-Jun, were inconsistent between proteomic analysis and Western blotting and require further investigation. Conclusion: The unique resistant cell lines generated here, as well the MCF7 OHT resistant line, provided novel data that give insights into the biological pathways involved in mechanisms of endocrine drug resistance in breast cancer. Proteomics analysis provided extensive data on common functionality and pathways across the resistant cell lines independent of phenotype or SERM. Overall, the results provided interesting targets for re-sensitising resistant breast cancer and the potential to investigate novel combination therapies in the future.
Proteomic profiling of matched normal and tumour tongue biopsies from smokers and non-smokers. Oncoproteomic applications for oral tongue squamous cell carcinoma biomarker discoveryDespite considerable development in the therapeutic repertoire for managing cancer-related malignancies, head and neck cancer mortality has not significantly improved. The burden of HNSCC fluctuates across countries and has been associated with exposure to tobacco-derived carcinogens, excessive alcohol consumption or combinations. Due to late detection, patients often present with oral pre-malignant lesions which have progressed to an advanced stage of HNSCC. In this study, the samples were from a male cohort as generally, men are at two to four-fold higher risk than women with over 90% of HNSCCs arising in the upper aerodigestive tract. Therefore, the purpose of this thesis was to identify HNSCC biomarkers in males associated within defined anatomical region (tongue) and causative agents, specific to smoking. An iTRAQ proteomic approach was used to profile protein changes in matched normal and tumour samples from male non-smoking (n=6) and smoking patients (n=6) with tongue carcinomas revealing identification of potential targets specific to cancer. Samples were subjected to liquid nitrogen cryo-pulverisation and protein determination. Protein extracts from the same category were pooled, trypsin digested and iTRAQ 4-plex labelled. Data was generated by 2D-LC/MS on an Orbitrap Fusion and significantly changed proteins (median ± SD) were subject to bioinformatics appraisal. A total of 3426 proteins were identified and quantified by proteomic analysis. Comparison of non-smoker tumour (NS:T) with smoker tumour (S:T) distinguished 64 proteins that were upregulated and 62 downregulated, S:T vs S:N categorised 349 proteins up- and 395 down-regulated respectively and NS:T vs NS:N identified 469 proteins up- and 431 down-regulated, respectively. Arginase-1 (ARG1), Keratin Type-2 Cytoskeletal 8 (KRT8), Lipocalin-1 (LCN1) and DNA replication licensing factor MCM2 (MCM2) were identified as biologically associated with smoking compared to non-smoking, providing viable targets for verification by immunochemical methods which further supported the proteomic data. Overall, the project demonstrated the importance of using matched biopsies with good clinicopathological data for experimental design and provided a set of unique targets for a more expanded verification study.
Time will tell: Material surface cues for the visual perception of material ageing Insights from psychophysics, online experiments, image processing and a science festivalThis thesis explores the visual perception of material change over time, a novel topic that has received little attention so far. We aimed to understand the material surface features and mental representations associated with material change over time by the human visual system, and possibly wider cognitive systems. To this end, we performed a series of experiments with varying methodologies. These included a psychophysics experiment, online experiments, and data collection during a science festival. The latter showed that the general public mentioned “Faded (colour)” most often to describe material change over time and that specific material surface change features clustered around specific materials. In another experiment, material type, but not colour or the geometrical distribution, had a significant effect on perceived material change. Other experiments partially contradicted this finding. It was found that perceived material type showed a significant, non-linear association with perceived material change, replicating earlier findings on the effect of material type. In contrast, material surface lightness, a constituent of colour, was associated with perceived material change. The same held for components of the geometrical distribution. They showed a minor contribution to the perception of material change, but a major one to perceived material type. Together, our findings suggest that the human visual system seems to use constituents of material surface colour as a cue to material change over time. The geometrical distribution seems to play a minor role. Although these contributions may vary with material type, as our findings showed that material type affected the perception of material change over time.
Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly DetectionTime series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep learning algorithms. The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminating features and time-series temporal nature. However, their performance is affected by the speed at which the time series arrives, the use of a fixed threshold, and the assumption of Gaussian distribution on the prediction error to identify anomalous values. An exact parametric distribution is often not directly relevant in many applications and it’s often difficult to select an appropriate threshold that will differentiate anomalies with noise. Thus, implementations need the Prediction Interval (PI) that quantifies the level of uncertainty associated with the Deep Neural Network (DNN) point forecasts, which helps in making a better-informed decision and mitigates against false anomaly alerts. To achieve this, a new anomaly detection method is proposed that computes the uncertainty in estimates using quantile regression and used the quantile interval to identify anomalies. Similarly, to handle the speed at which the data arrives, an online anomaly detection method is proposed where a model is trained incrementally to adapt to the concept drift that improves prediction. This is implemented using a window-based strategy, in which a time series is broken into sliding windows of sub-sequences as input to the model. To adapt to concept drift, the model is updated when changes occur in the new arrival instances. This is achieved by using anomaly likelihood which is computed using the Q-function to define the abnormal degree of the current data point based on the previous data points. Specifically, when concept drift occurs, the proposed method will mark the current data point as anomalous. However, when the abnormal behavior continues for a longer period of time, the abnormal degree of the current data point will be low compared to the previous data points using the likelihood. As such, the current data point is added to the previous data to retrain the model which will allow the model to learn the new characteristics of the data and hence adapt to the concept changes thereby redefining the abnormal behavior. The proposed method also incorporates feature extraction to capture structural patterns in the time series. This is especially significant for multivariate time-series data, for which there is a need to capture the complex temporal dependencies that may exist between the variables. In summary, this thesis contributes to the theory, design, and development of algorithms and models for the detection of anomalies in both static and evolving time series data. Several experiments were conducted, and the results obtained indicate the significance of this research on offline and online anomaly detection in both static and evolving time-series data. In chapter 3, the newly proposed method (Deep Quantile Regression Anomaly Detection Method) is evaluated and compared with six other prediction-based anomaly detection methods that assume a normal distribution of prediction or reconstruction error for the identification of anomalies. Results in the first part of the experiment indicate that DQR-AD obtained relatively better precision than all other methods which demonstrates the capability of the method in detecting a higher number of anomalous points with low false positive rates. Also, the results show that DQR-AD is approximately 2 – 3 times better than the DeepAnT which performs better than all the remaining methods on all domains in the NAB dataset. In the second part of the experiment, sMAP dataset is used with 4-dimensional features to demonstrate the method on multivariate time-series data. Experimental result shows DQR-AD have 10% better performance than AE on three datasets (SMAP1, SMAP3, and SMAP5) and equal performance on the remaining two datasets. In chapter 5, two levels of experiments were conducted basis of false-positive rate and concept drift adaptation. In the first level of the experiment, the result shows that online DQR-AD is 18% better than both DQR-AD and VAE-LSTM on five NAB datasets. Similarly, results in the second level of the experiment show that the online DQR-AD method has better performance than five counterpart methods with a relatively 10% margin on six out of the seven NAB datasets. This result demonstrates how concept drift adaptation strategies adopted in the proposed online DQR-AD improve the performance of anomaly detection in time series.