Abstract A fast, reproducible, and sensitive method is proposed for the kinetic determination of carbocisteine (CCys). The method depends on the inhibitory property of carbocisteine, which reduces the Hg2+ catalyzed substitution rate of cyanide from [Ru(CN)6]4- with N-R-salt (1-Nitroso-2-naphthol-3,6-disulfonic acid disodium salt) via forming a stable complex with Hg2+. Spectrophotometric measurements were carried out by recording the absorbance at 525 nm (λmax of [Ru(CN)5 Nitroso-R-Salt]3- complex) at a fixed time of 10 and 15 min under the optimized reaction conditions with [N-R-salt] = 4.5 × 10-4 M, I = 0.05 M (KNO3), Temp = 45.0 ± 0.2 o C, pH = 7.0 ± 0.03, [Hg2+] = 8.0 × 10-5 M and [Ru(CN)6 4-] = 4.25 × 10-5 M. With the proposed method, CCys can be determined quantitatively down to 3.0 × 10-6 M. This methodology can be effectively used for the rapid quantitative estimation of CCys in the pharmaceutical samples with good accuracy and reproducibility. The addition of common excipients in pharmaceuticals even up to 1000 times with [CCys] does not interfere significantly in the estimation of CCys.

Resumen Se propone un método rápido, reproducibley sensible para la determinación cinética de la carbocisteina (CCys). El método depende de la propiedad inhibitoria de la carbocisteina que reduce la tasa de sustitución catalizada por Hg2+ del cianuro de [Ru(CN)6]4- con la sal N-R (sal disódica del ácido 1-Nitroso-2-naftol-3,6-disulfónico) mediante la formación de un complejo estable con Hg2+. Las mediciones espectrofotométricas se llevaron a cabo registrando la absorbancia a 525 nm (λmax del complejo [Ru(CN)5 Sal-Nitroso-R]3-) en un tiempo fijo de 10 y 15 min en las condiciones de reacción optimizadas con [sal-NR] = 4.5 × 10-4 M, I = 0.05 M (KNO3), Temp = 45.0 ± 0.2 o C, pH = 7.0 ± 0.03, [Hg2+] = 8.0 × 10-5 M y [Ru(CN)6 4-] = 4.25 × 10-5 M. Con el método propuesto, CCys se puede determinar cuantitativamente hasta 3,0 × 10-6 M. Esta metodología se puede utilizar eficazmente para la estimación cuantitativa rápida de CCys en las muestras farmacéuticas con buena precisión y reproducibilidad. La adición de excipientes comunes en productos farmacéuticos incluso hasta 1000 veces con [CCys] no interfiere significativamente en la estimación de CCys.

ABSTRACT With the rapid rise in popularity of ecommerce application, Recommender Systems are being widely used by them to predict the response that a user will give to a given item. This prediction helps in cross selling, upselling and to increase the loyalty of their customers. However due to lack of sufficient feedback data these systems suffer from sparsity problem which leads to decline in their prediction efficiency. In this work, we have proposed and empirically demonstrated how the Transfer Learning approach using five dimensions of basic human values can be successfully used to alleviate the sparsity problem and increase the efficiency of recommender system algorithms.