Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519261
Title: Human proteomic profiles in latent and active tuberculosis
Author: Sandhu, Gurjinder Singh
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2010
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Abstract:
Distinguishing patients with active tuberculosis (TB) from those with latent TB is an important clinical problem. The SELDI-TOF MS (Surface Enhanced Laser Desorption Ionisation – Time of Flight Mass Spectrometry) platform allows for high throughput detection of multiple proteins in biological fluids. Proteomic patterns reflecting host-pathogen interaction can be used as a tool to aid our understanding of the Natural History of Tuberculosis. Methods: Plasma samples were collected prospectively in a shanty town in Lima, Peru. Latent and active TB status was defined using the Tuberculin Skin Test (TST), Quantiferon (QFN) assay and TB culture. Crude plasma and fractionated plasma samples were analysed on weak cationic CM10 chip surfaces using a Biomek 3000 Laboratory Automation Workstation. Spectra were generated using a ProteinChip System 4000 Mass spectrometer. Data was analysed using a Support Vector Machine. Results: Samples were collected from 154 patients with active TB, 112 patients with respiratory symptoms suggestive of TB and 151 healthy controls. Multiple peaks differed significantly between active TB patients and unhealthy controls. Trained optimal classifiers discriminate between: i) active TB and unhealthy controls with 84% accuracy (87% sensitivity, 79% specificity) in crude plasma and up to 89% accuracy (90% sensitivity, 88% specificity) in fractionated plasma ii) active TB and latent TB with 89% accuracy (90% sensitivity, 89% specificity) iii) latent TB and no TB in healthy controls with 77% accuracy (67% sensitivity, 84% specificity). Conclusions: SELDI-TOF MS proteomic profiles in combination with trained optimal classifiers accurately discriminate active TB from other respiratory disorders. The classifier for latent TB was not as accurate, but active TB could be discriminated from latent TB.
Supervisor: Friedland, Jon ; Agranoff, Daniel Sponsor: Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.519261  DOI: Not available
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