Literature DB >> 16802027

Needle enzyme electrode based glucose diffusive transport measurement in a collagen gel and validation of a simulation model.

Zimei Rong1, Umber Cheema, Pankaj Vadgama.   

Abstract

Rapid response needle enzyme electrodes were fabricated to measure the glucose concentration at the centre of a cylindrical spiralled collagen gel, which is a relevant constituent for tissue engineering scaffolds. The experimental data were based on a low consumption glucose sensor which minimised the distorting effect of enzymatic degradation. As the measurement was carried out within a collagen gel the stirring independence was compulsory for the biosensor. Glucose concentration changes were derived from a model based on the solution to Fick's Second Law. This had two different expressions for different dimensionless time (T) domains. The expression for large T and a first order approximation for small T were known. The expression for high order approximation for small T was then derived. An analytical expression consisting of fast convergent parts of these two expressions is proposed, which operates for the entire time region. A computational model for glucose concentration evolution where an electrode is located is proposed to operate for extended time periods. The model was confirmed by agreement between the simulated and observed data. An experimental technique is developed here to determine glucose diffusion coefficient by fitting the simulated concentration profile to the observed one. The glucose diffusion coefficient within the collagen gel was estimated to be 1.3 x 10(-6) cm(2) s(-1); higher accuracy is achieved here because errors due to noise, baseline and zero time determination are minimised with best fit.

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Year:  2006        PMID: 16802027     DOI: 10.1039/b600334f

Source DB:  PubMed          Journal:  Analyst        ISSN: 0003-2654            Impact factor:   4.616


  10 in total

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9.  Role of extracellular matrix and microenvironment in regulation of tumor growth and LAR-mediated invasion in glioblastoma.

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Journal:  Biomacromolecules       Date:  2014-10-02       Impact factor: 6.988

  10 in total

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