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Catalin Nistor

Publications and source records attributed to Catalin Nistor.

3 recordsLinked to original sources

Multivariate analysis to separate the signal given by cross-reactants in immunoassay with sample matrix dilution.

This paper describes a new approach to achieve selectivity in an immunoassay by separating the signals given by two cross-reactive compounds present simultaneously in a complex sample matrix. The method is based on the sequential dilution of the sample containing a mixture of the two analytes, spiking each diluted sample with a reference compound, and the detection by enzyme-linked immunosorbent assay (ELISA). The obtained multivariate response was used for the individual calibrations of the assay for each of the two cross-reactants simultaneously by using principal component analysis (PCA) and partial least squares regression (PLSR) data modeling. The calibration models showed that the signal separation due the analytes 2,4-dinitrophenol (2,4-DNP) and 4-nitrophenol (4-NP) was possible with a prediction concentration error of 1.4 microM and 72 microM, respectively.

2,4-Dinitrophenol↗

A capillary-based amperometric flow immunoassay for 2,4,6-trichlorophenol.

This paper describes the development of two different capillary-based heterogeneous competitive flow immunoassay formats (capillary flow injection immunoassay (CFIIA) and capillary sequential injection immunoassay (CSIIA)) for the determination of 2,4,6-trichlorophenol (2,4,6-TCP). The assays are based on the competition between the analyte and an analyte derivative labelled with the enzyme beta-galactosidase, for an anti-TCP antibody, followed by the injection of the mixture at equilibrium into a flow stream, where separation between the fractions bound and unbound to the antibody is performed in a glass capillary containing immobilised protein A. The antibody-tracer fraction retained inside the protein A capillary was measured by injection of 4-aminophenyl- beta- D-galactoside (4-APG), followed by amperometric detection of the enzymatically generated 4-aminophenol (4-AP), leading to a negative correlation between the signal and the analyte concentration. The two immunoassay formats were compared in terms of sensitivity and speed, giving IC(50) values of 1.41+/-0.03 and 1.64+/-0.07 micro g L(-1), detection limits of 0.2 and 0.4 micro g L(-1), and sample throughputs of 6 and 4 h(-1) for the CFIIA and CSIIA system, respectively. The influence of different interfering chlorophenolic compounds in the assay was minor, with only one exception (i.e. 2,4-dichlorophenol). In addition, different water matrices were tested (surface, tap, and rain water), showing that the matrix influence was negligible, except for rainwater, which resulted in a 30% increase in sensitivity. As a conclusion, the assay is suitable for the fast screening of TCP present at low concentration levels in water samples.

Antibodies, Monoclonal↗

A glucose dehydrogenase biosensor as an additional signal amplification step in an enzyme-flow immunoassay.

Both the antibody affinity and the detectability of the label are essential in deciding the final characteristics of a heterogeneous immunoassay. This paper describes an approach to obtain a supplementary enhancement of the signal generated by using an enzyme label, e.g., by including the product of the enzymatic reaction in an additional amplification cycle during the detection step performed with an amperometric biosensor based on glucose dehydrogenase (GDH). An immunoassay format with a labelled analyte derivative that competes with the analyte present in the sample for a limited amount of antibody binding sites was employed. The beta-galactosidase label hydrolyses the substrate aminophenyl-beta-galactopyranoside, and the generated aminophenol enters then into a bioelectrocatalytic amplification cycle at the GDH biosensor. The principle was applied for determination of 4-nitrophenol, with the best minimal concentration of 1.5 microM and a midpoint of the calibration of 24 microM. The potentials and limitations of such a system are discussed.

Biosensing Techniques↗