211 resultados para Serological diagnosis

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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Screening for Helicobacter pylori in dyspeptic patients may improve selectivity for gastroscopy. Rapid serological tests based on ELISA technique are cheap, readily available and simple to use in the clinical setting. However local evaluation is essential in order to validate these techniques. Fifty-six dyspeptic patients (aged less than 45 yr) had a rapid serological test (Helisal) performed prior to gastroscopy. At gastroscopy H. pylori status was assessed using culture and histology. The Helisal sensitivity was 80 per cent, specificity 82 per cent. Screening patients with the Helisal test would have missed 6 patients with peptic ulcer disease and 2 with oesophagitis. The Helisal test did not perform satisfactorily as a screening test in selection of patients for gastroscopy.

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Quantitative point-of-care (POC) devices are the next generation for serological disease diagnosis. Whilst pathogen serology is typically performed by centralized laboratories using Enzyme-Linked ImmunoSorbent Assay (ELISA), faster on-site diagnosis would infer improved disease management and treatment decisions. Using the model pathogen Bovine Herpes Virus-1 (BHV-1) this study employs an extended-gate field-effect transistor (FET) for direct potentiometric serological diagnosis. BHV-1 is a major viral pathogen of Bovine Respiratory Disease (BRD), the leading cause of economic loss ($2 billion annually in the US only) to the cattle and dairy industry. To demonstrate the sensor capabilities as a diagnostic tool, BHV-1 viral protein gE was expressed and immobilized on the sensor surface to serve as a capture antigen for a BHV-1-specific antibody (anti-gE), produced in cattle in response to viral infection. The gE-coated immunosensor was shown to be highly sensitive and selective to anti-gE present in commercially available anti-BHV-1 antiserum and in real serum samples from cattle with results being in excellent agreement with Surface Plasmon Resonance (SPR) and ELISA. The FET sensor is significantly faster than ELISA (<10 min), a crucial factor for successful disease intervention. This sensor technology is versatile, amenable to multiplexing, easily integrated to POC devices, and has the potential to impact a wide range of human and animal diseases.

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This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.