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Raman Spectroscopy for the identification and classification of malignancy in the oesophagus has been demonstrated in this thesis. The potential of Raman spectroscopy in this field is twofold; as a adjunct for the pathologist and as a biopsy targeting tool at endoscopy. This study has demonstrated the feasibility of these potential applications in vitro. Spectral diagnostic models have been developed by correlating spectral information with histopathology. This is the current 'gold standard' diagnostic method for the identification of dysplasia, the established risk factor for the development of oesophageal cancer. Histopathology is a subjective assessment and widely acknowledged to have limitations. A more rigorous gold standard was therefore developed, as part of this study, using the consensus opinion of three independent expert pathologists to train the diagnostic models. Raman spectra have been measured from oesophageal tissue covering the full spectrum of malignant disease in the oesophagus, using a near infrared Raman spectrometer customised for tissue spectral measurements. Two spectral datasets were measured with different volumes of tissue probed using twenty and eighty times magnification ultra long working distance objectives. Multivariate statistical analysis has been used to extract the required spectral information with the greatest discriminative power. Principal component fed linear discriminant spectral models have been tested with leave one out cross validation procedures. Three pathology group models have correctly classified up to 91% of spectra, and eight group models have correctly classified up to 82% of spectra. Optimisation of the spectral models by selection of significant principal components, filtering the data and using staggered models was investigated. Effort has been made to understand the findings in their clinical context, with review of patient history and clinical progress, long term follow up is required. Preliminary work projecting independent data on to the models has been encouraging with 76% of the spectra in the three group model correctly classified, approaching classification levels of the training dataset. Formalin fixed tissue models were demonstrated to perform well, with 80% of the spectra were correctly classified in the seven group model. This further demonstrates the potential of Raman spectroscopy as a pathology tool. If Raman spectroscopy is to be implemented in a clinical setting it must be transferable between different measurement systems. This has been evaluated with oesophageal tissue spectra measured on two systems using three objectives. Simple calibration has demonstrated the use of multiple systems and measurement parameters in the development and application of spectral classification models. Testing of a new design of fibre probe has provided encouraging preliminary results. There is potential for the application of Raman spectroscopy in vivo, however the technology remains immature. Spectral maps of samples taken from across the spectrum of disease have shown clear delineation of the morphological features seen on the H&E images. Furthermore the biochemical information elicited has been analysed. Initial measurements of oesophageal tissue using multiphoton imaging have demonstrated the potential of collagen autofuorescence in the diagnosis of malignant change.

This paper examines the impact of institutional quality on the level of new business formation in 26 of the former socialist states from 2006-2018. Using cross-sectional and panel data, I find that the quality of a nation’s institutions, as measured by the rule of law and control of corruption, has a statistically and economically significant effect on the number of newly registered LLCs. Economies willing to improve their institutional quality can expect more businesses start-ups to register in the formal economy. Specifically, improving the control of corruption by one standard deviation is associated with between 1.5 and 2.4 new businesses registered per 1,000 working age people. Furthermore, I propose that institutional quality also influences an individual’s decision whether to operate a new business start-up in the informal or formal sector.

Vibrational spectroscopic diagnostic techniques have significant potential to improve the care of women with benign, premalignant and malignant vulval diseases by reducing the reliance on traditional biopsy and histopathology. These techniques also have the potential to augment clinicians’ ability to differentiate different types of vulval disease at the time of surgery for neoplastic vulval disease. In addition, vibrational spectroscopic techniques offer the opportunity to assess molecular changes associated with the development of vulval cancer that are not apparent on routine histopathological assessment. The work outlined in this thesis evaluates the role of emerging techniques in vibrational spectroscopy to address this need within three key themes: 1. Developmentofavibrationalspectroscopicdiagnostictechniquetoreducethe reliance on traditional biopsy and histopathological diagnosis. 2. Developmentofavibrationalspectroscopicdiagnostictechniqueforimproving the delineation of disease margins at the time of surgery for pre-malignant and malignant vulval conditions. 3. Evaluation of a vibrational spectroscopic tool for augmenting and automating aspects of vulval histopathology. Raman spectroscopic mapping of 91 fresh frozen vulval tissue sections combined with multivariate spectral analysis was used to demonstrate that malignant vulval disease could be differentiated from non-neoplastic and premalignant vulval disease with a sensitivity of 97% and specificity of 78%. The technique was then tested in experimental conditions closer to in-vivo application, measuring spectra from 91 whole fresh frozen tissue blocks using microscope and probe Raman systems. This demonstrated the technique could differentiate malignant from non-neoplastic and premalignant vulval disease with sensitivities of 84% to 92% and specificities of 84% to 64% respectively. In a separate investigation vulval tissue blocks from 27 women with suspected lichen sclerosus underwent Raman spectroscopic point measurements. Multivariate analysis demonstrated Raman spectroscopy could be used to differentiate lichen sclerosus from other vulval disorders with a similar clinical appearance with a sensitivity of sensitivity of 91% and specificity of 80%. Fourier transform infrared (FTIR) spectroscopic mapping of 93 fixed paraffin embedded tissue sections was used to demonstrate that malignant vulval disease could be differentiated from non-neoplastic and premalignant with vulval disease with an approximate sensitivity of 100% and specificity of 79%. In addition FTIR spectroscopy was used to differentiate molecular changes in vulval intraepithelial neoplasia (VIN) and lichen sclerosus (LS) found in association with vulval squamous cell carcinoma (SCC). Analysis of FTIR spectroscopic tissue maps from 48 patients demonstrated the technique could differentiate LS associated with SCC with a sensitivity of approximately 100% and specificity of 84% and VIN associated with SCC with a sensitivity of approximately 100% and specificity 58%. This thesis demonstrates the considerable potential of vibrational spectroscopy in this clinical setting. The research has made significant progress in each of the three themes outlined above and indicates that further work is warranted to develop the techniques towards routine clinical application.

Epithelial Misplacement (EM) is a benign phenomenon that occurs within polyps most commonly associated with the sigmoid colon. It is brought about because of the colons convulsive nature and this forces a polyps surface epithelium into its submucosa and also causes bleeding. This is problematic as the Bowel Cancer Screening Programme (BCSP) uses positive Faecal Occult Blood (FOB) test results to identify patients that require pathological review. As EM polyps bleed, they get selected for assessment and this results in them being sectioned and stained. In these cross sections, submucosal glandular tissue will be found that looks like it has formed due to metastatic mechanisms. This can lead to ambiguous diagnoses that will cause some patients to undergo unnecessary surgery. It is postulated that this can be prevented if the continuity of the EM samples could be measured. This is because only in the EM cases will the submucosal epithelial tissue remain in continuity with the surface. To test this, volumes representative of 9 samples of cancer and 13 cases of EM were segmented and their number of 26 three dimensional (3D) connected components were recorded. These were used with the 99% confidence limits of the two tailed Mann Whitney U Statistic and tested the null hypothesis that the cancer cases were as connected as the EM samples. In this instance, no significant differences were found and so the benefit of measuring the connectivity of these pathologies is questionable. It was because of this that Immunohistochemical (IHC) alternatives were considered. It was found that Collagen IV antibody staining correctly differentiated nine samples of EM from ten cases of cancer. The Mann Whitney U Statistic found this to be highly significant, p < 0.001, and future investigations should concentrate on automating this analysis. Although, Collagen IV provided a good classification it relied upon the subjective assessment of a pathologist. Therefore, the use of epithelial specific IR spectra was also investigated and this enabled the eleven EM and nine cancer cases that were investigated to be accurately classified 80% of the time upon cross validation. The collection of epithelial specific spectra relied upon a novel digital staining technique that has much application within future research. This study demonstrates that the intermodal registration of complementary modalities is of benefit to the disease classification problem. This technique has potential to be used in the correct identification of EM but more work is required.

This translational research evaluates a novel, custom-built Raman probe for potential application as an in vivo diagnostic tool in the oesophagus. Raman spectroscopy (RS) is a well established analytical technique which is capable of probing the biochemical changes associated with neoplastic progression in oesophageal tissue. RS relies on measurement of subtle inelastic scattering signals following monochromatic laser excitation. Clinical utilisation of RS within hollow organs requires accurate collection and transmission of signal through fibre-optic cables. This research aims to evaluate the ability of a custom built fibre-optic Raman probe, in conjunction with multivariate classification models, to differentiate between benign and neoplastic oesophageal pathologies ex vivo. The need for spectral stability and reproducibility are addressed, as are difficulties associated with multisystem reliability. In addition, the biochemical basis of spectral classification is evaluated and discussed. Cont/d.

This study has involved the analysis of volatile fingerprints using a hybrid electronic nose (e-nose) to discriminate between and diagnose the microorganisms which cause ventilator–associated pneumonia (VAP), one of the most important infections in the hospital environment. This infection occurs in hospitalised patients with 48-72 hrs of mechanical ventilation. VAP diagnostics still remains a problem due to the lack of a precise diagnostic tool. The current tests are mostly based on quantitative cultures of samples from the lower lung airways with clinical findings, which do not often result in accurate diagnoses of the disease. Cont/d.

Ventilator associated pneumonia (VAP) is a significant challenge for the Intensive Care doctors worldwide. It is both difficult to diagnose accurately and quickly and to treat effectively once the diagnosis has been established. Current diagnostic microbiological methods of diagnosis can take up to 48 hours to yield results. Early diagnosis and treatment remain the best way of improving outcome for patients with VAP. In this study we look at novel diagnostic techniques for VAP. Electronic nose (Enose) technology was used to identify to identify the presence of microorganisms in bronchoalveolar lavage (BAL) fluid samples taken from the respiratory tracts of ventilated patients. The results were compared with standard microbiological culture and sensitivities. The Enose was able to discriminate 83% of samples into growth or no growth groups on samples grown in the lab. When the technique was employed to samples taken directly from patients the accuracy fell to 68.2%. This suggests that patient related factors may be reducing the accuracy of the Enose classification. The use of antimicrobial drugs prior to patient sampling is likely to have played a major role. The second part of this study used Gas Chromatography-Mass Spectrometry (GC-MS) analysis of patient’s breath in an attempt to identify patients with VAP. Breath samples were taken at the same time as the bronchoalveolar lavage samples described above. The use of this technique did show differences between the breath samples of patients who did not have any microbiological growth from their BAL samples and those that did. Leave one out cross validation of a PC fed LDA model showed 84% correct classification between healthy volunteers, no growth and growth groups. Finally, we evaluated the Breathotron, which is a breath analysis device designed and built at Cranfield Health. It allows for analysis of breath samples using a single sensor system as opposed to a sensor array employed in traditional Enose devices. This allows it to be more portable and cheaper to build. The Breathotron also allows collection of breath onto sorbent cartridges for GC-MS analysis. Its single sensor did not allow for accurate discrimination between samples.

Raman spectroscopy is a technique that utilises inelastic scattering processes to provide a biochemical fingerprint that has been shown to successfully discriminate oesophageal pathologies. The aim of this study was to develop Raman spectroscopy as a clinical tool; both in vivo for ‘targeted biopsy’, and in ex vivo for ‘automated histopathology’. Two different Raman probes were evaluated and compared and tissue classification models generated ex vivo. A preliminary classification model of a novel single collection fibre probe demonstrated potential for the probe design. Both probes were shown to discriminate three different oesophageal pathology groups. A cross-validated tissue classification model (88 samples) discriminated normal, Barrett’s and neoplasia with an overall accuracy of 86.5% with a sensitivity of 83.3-89.5% and specificity of 89.2-97.1%. A novel rapid Raman mapping technique was evaluated. It was shown that sufficient biochemical information for pathology diagnosis could be extracted from low signal to noise ratio data using multivariate analysis providing the dataset was sufficiently large, thus demonstrating the feasibility of automated histopathology in a clinically realistic time frame. Furthermore, it was demonstrated that high spatial resolution imaging was not necessarily required for automated histopathology using novel interpretation of multivariate techniques. A tissue classification model generated from two rapid Raman maps containing separated substrate, normal, HGD, luminescence and fibrous connective tissue with an overall training performance of 97.5% Problems limiting clinical implementation of Raman techniques were investigated and methods of overcoming devised.