Synthesis and reactivity of alpha-thio-beta-chloroacrylamides and alpha-thio-beta-chloroenones

Development of novel synthetic methodology for selective transformation of organic compounds is a central element underpinning organic synthesis with control of chemo-, regio- and stereoselectivity a very high priority. Reactions which can be conducted under mild reaction conditions and, ideally in an environmentally attractive manner, are particularly advantageous. The principal objective of this thesis was to explore the synthesis, reactivity and synthetic utility of a series of α,β-thio-β-chloroenones. The stereochemical features of these transformations and the potential of this novel series of compounds in the synthesis of bioactive compounds were of particular interest. In exploring the reactivity of these compounds, the key transformations included nucleophilic additions and Stille cross-coupling at the β-carbon. Chapter 1 reviews the literature relevant to the research conducted, and focuses in particular on the synthesis of β-chloroenones and related unsaturated carbonyl compounds. The synthesis of chalcone compounds from various precursors is also discussed, with particular emphasis on the use of palladium cross-coupling reactions in the preparation of these compounds. The biological activity of chalcones is also summarised in this chapter. The second chapter delineates the stereoselective synthesis of the novel α-thio-β-chloroenones from the corresponding α-thioketones in a multistep reaction cascade initiated by a NCS-mediated chlorination. A range of both alkyl and aryl β-chloroenones were prepared in this work and the oxidation of these compounds to the corresponding sulfoxides and sulfones is also outlined. The electrophilicity of the β-carbon of the enones was examined in nucleophilic addition/substitution reactions with successful access to a variety of synthetically useful novel adducts including acetals and enaminoketones. Investigation of the synthetic potential of the Stille cross-coupling reaction with the novel α-thio-β-chloroenones was explored and provided an efficient route for the synthesis of a novel series of chalcones. Most importantly this new methodology provided a new and synthetically powerful approach for carbon-carbon bond formation at the β-carbon under mild neutral conditions. A preliminary investigation into the use of these β-chloroenones as dienophiles in Diels-Alder cycloaddition reactions is also discussed in this chapter. Chapter 2 also reports the nucleophilic addition of N, O, S and C nucleophiles to previously described β-chloroacrylamides and their corresponding sulfoxide derivatives. This work builds on previous research carried out in this programme and the reactivity of these β-chloroacrylamides at the sulfide and sulfoxide level is compared. Comparison of the reactivity of the β-chloroacrylamides, in nucleophilic substitution and Stille-coupling, with that of the novel β-chloroenones is of interest. Finally, the biological activity of both the β-chloroenones and the β-chloroacrylamides in terms of cytotoxicity is summarised in Chapter 2. The final chapter, Chapter 3, details the full experimental procedures, including spectroscopic and analytical data for the compounds prepared during this research.

Catalyst, additive and substrate effects in intramolecular aromatic additions of alpha-diazoketones

This thesis is focused on transition metal catalysed reaction of α-diazoketones leading to aromatic addition to form azulenones, with particular emphasis on enantiocontrol through use of chiral copper catalysts. The first chapter provides an overview of the influence of variation of the substituent at the diazo carbon on the outcome of subsequent reaction pathways, focusing in particular on C-H insertion, cyclopropanation, aromatic addition and ylide formation drawing together for the first time input from a range of primary reports. Chapter two describes the synthesis of a range of novel α-diazoketones. Rhodium and copper catalysed cyclisation of these to form a range of azulenones is described. Variation of the transition metal catalyst was undertaken using both copper and rhodium based systems and ligand variation, including the design and synthesis of a novel bisoxazoline ligand. The influence of additives, especially NaBARF, on the enantiocontrol was explored in detail and displayed an interesting impact which was sensitive to substituent effects. Further exploration demonstrated that it is the sodium cation which is critical in the additive effects. For the first time, enantiocontrol in the aromatic addition of terminal diazoketones was demonstrated indicating enantiofacial control in the aromatic addition is feasible in the absence of a bridgehead substituent. Determination of the enantiopurity in these compounds was particularly challenging due to the lability of the products. A substantial portion of the work was focused on determining the stereochemical outcome of the aromatic addition processes, both the absolute stereochemistry and extent of enantiopurity. Formation of PTAD adducts was beneficial in this regard. The third chapter contains the full experimental details and spectral characterisation of all novel compounds synthesised in this project, while details of chiral stationary phase HPLC and 1H NMR analysis are included in the appendix.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Molecular mechanisms underpinning IFN-gamma and TNF-alpha synergy in intestinal epithelial cells

Cytokine-driven signalling shapes immune homeostasis and guides inflammatory responses mainly through induction of specific gene expression programmes both within and outside the immune cell compartment. These transcriptional outputs are often amplified via cytokine synergy, which sets a stimulatory threshold that safeguards from exacerbated inflammation and immunopathology. In this study, we investigated the molecular mechanisms underpinning synergy between two pivotal Th1 cytokines, IFN-γ and TNF-α, in human intestinal epithelial cells. These two proinflammatory mediators induce a unique state of signalling and transcriptional synergy implicated in processes such as antiviral and antitumour immunity, intestinal barrier and pancreatic β-cell dysfunction. Since its discovery more than 30 years ago, this biological phenomenon remains, however, only partially defined. Here, using a functional genomics approach including RNAi perturbation screens and small-molecule inhibitors, we identified two new regulators of IFN-γ/TNF-α-induced chemokine and antiviral gene and protein expression, a Bcl-2 protein BCL-G and a histone demethylase UTX. We also discovered that IFN-γ/TNF-α synergise to trigger a coordinated shutdown of major receptor tyrosine kinases expression in colon cancer cells. Together, these findings extend our current understanding of how IFN-γ/TNF-α synergy elicits qualitatively and quantitatively distinct outputs in the intestinal epithelium. Given the well-documented role of this synergistic state in immunopathology of various disorders, our results may help to inform the identification of high quality and biologically relevant druggable targets for diseases characterised by an IFN-γ/TNF-α high immune signature

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.