Pattern recognition of HER-1 in biological fluids using stochastic sensing.

Stochastic sensing was employed for pattern recognition of HER-1 in biological fluids. Nanostructured materials such as 5,10,15,20-tetraphenyl-21H,23H-porphyrin, maltodextrin and α-cyclodextrin were used to modify diamond paste for stochastic sensing of HER-1. Pattern recognition of HER-1 in biological fluids was performed in a linear concentration range between 5.60 × 10(-11) and 9.72 × 10(-7 )mg ml(-1). The lower limits of determination (10(-12 )mg ml(-1) magnitude order) were recorded when maltodextrin and α-cyclodextrin were used for stochastic sensing. The pattern recognition test of HER-1 in biological fluids samples shows high reliability for both qualitative and quantitative assay.

Molecular recognition of HER-1 in whole-blood samples.

Multimode sensing was proposed for molecular screening and recognition of HER-1 in whole blood. The tools used for molecular recognition were platforms based on nanostructured materials such as the complex of Mn(III) with meso-tetra (4-carboxyphenyl) porphyrin, and maltodextrin (dextrose equivalence between 4 and 7), immobilized in diamond paste, graphite paste or C60 fullerene paste. The identification of HER-1 in whole-blood samples, at molecular level, is performed using stochastic mode and is followed by the quantification of it using stochastic and differential pulse voltammetry modes. HER-1 can be identified in the concentration range between 280 fg/ml and 4.86 ng/ml using stochastic mode, this making possible the early detection of cancers such as gastrointestinal, pancreatic and lung cancers. The recovery tests performed using whole-blood samples proved that the platforms can be used for identification and quantification of HER-1 with high sensitivity and reliability in such samples, these making them good molecular screening tools.

Pattern recognition of neurotransmitters using multimode sensing.

BACKGROUND: Pattern recognition is essential in chemical analysis of biological fluids. Reliable and sensitive methods for neurotransmitters analysis are needed. NEW METHOD: Therefore, we developed for pattern recognition of neurotransmitters: dopamine, epinephrine, norepinephrine a method based on multimode sensing. Multimode sensing was performed using microsensors based on diamond paste modified with 5,10,15,20-tetraphenyl-21H,23H-porphyrine, hemin and protoporphyrin IX in stochastic and differential pulse voltammetry modes. RESULTS: Optimized working conditions: phosphate buffer solution of pH 3.01 and KCl 0.1mol/L (as electrolyte support), were determined using cyclic voltammetry and used in all measurements. The lowest limits of quantification were: 10(-10)mol/L for dopamine and epinephrine, and 10(-11)mol/L for norepinephrine. The multimode microsensors were selective over ascorbic and uric acids and the method facilitated reliable assay of neurotransmitters in urine samples, and therefore, the pattern recognition showed high reliability (RSD<1% for more than 6 months) for the simultaneous determination of dopamine, epinephrine and norepinephrine from urine and whole blood samples. COMPARISON WITH EXISTING METHOD(S): The proposed method can perform pattern recognition of the three neurotransmitters on biological fluids at a lower determination level than chromatographic methods. The sampling of the biological fluids referees only to the buffering (1:1, v/v) with a phosphate buffer pH 3.01, while for chromatographic methods the sampling is laborious. CONCLUSIONS: Accordingly with the statistic evaluation of the results at 99.00% confidence level, both modes can be used for pattern recognition and quantification of neurotransmitters with high reliability. The best multimode microsensor was the one based on diamond paste modified with protoporphyrin IX.

Enantioanalysis of pipecolic acid with stochastic and potentiometric microsensors.

Stochastic and potentiometric microsensors based on porphyrins and polymeric surfactants such as polysodium N-undecanoyl-L-leucylvanilate and polysodium N-undecanoyl-L-vanilate were developed for enantioselective assay of pipecolic acid. The matrices used for the design of the stochastic sensors were diamond paste and graphite paste, while the matrix used for the design of potentiometric sensors was carbon paste. The response characteristics of the microsensors were determined for the enantiomers of pipecolic acid. The response characteristics, selectivity, and enantioselectivity studies proved that the proposed microsensors can be used for clinical enantioanalysis of pipecolic acid in biological fluids, e.g., urine and whole blood.