Detection of hepatitis B virus markers using a biosensor based on imaging ellipsometry

A biosensor based on imaging ellipsometry (BIE) has been developed and validated in 169 patients for detecting five markers of hepatitis B virus (HBV) infection. The methodology has been established to pave the way for clinical diagnosis, including ligand screening, determination of the sensitivity, set-up of cut-off values (CoVs) and comparison with other clinical methods. A matrix assay method was established for ligand screening. The CoVs of HBV markers were derived with the help of receiver operating characteristic curves. Enzyme-linked immunosorbent assay (ELISA) was the reference method. Ligands with high bioactivity were selected and sensitivities of 1 ng/mL and 1 IU/mL for hepatitis B surface antigen (HBsAg) and surface antibody (anti-HBs) were obtained respectively. The CoVs of HBsAg, anti-HBs, hepatitis B e antigen, hepatitis B e antibody and core antibody were as follows: 15%, 18%, 15%, 20% and 15%, respectively, which were the percentages over the values of corresponding ligand controls. BIE can simultaneously detect up to five markers within 1 h with results in acceptable agreement with ELISA, and thus shows a potential for diagnosing hepatitis B with high throughput.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.