RESUMEN
A new polymeric adsorbent to improve portable dialysis treatment by simplifying urea removal at the dialysate regeneration step is proposed. An adsorbent to remove urea was synthesized by molecular imprinting technology that can potentially overcome drawbacks existing in urease enzyme-based dialysate regeneration technology. Molecularly imprinted polymer (MIP) for urea generates cavities both in shape and in size targeted for urea. Using the synthesized MIP, we have shown removal of urea present in the dialysate buffer. Various experimental conditions such as choice of monomers, porogen, and template to monomer ratios were optimized to achieve highest binding capacity on a column flow through system monitored using high-performance liquid chromatography (HPLC). Taking advantage of the basicity of urea molecule, monomers having Brønsted acidic groups such as acrylic acid, methacrylic acid and itaconic acid were screened. The MIP synthesized using urea:acrylic acid:EGDMA (1:4:12) in acetonitrile:ethylene dichloride (1:1) as porogen gave highest binding capacity of 24.5 g/kg of urea in the dialysate buffer matrix.
Asunto(s)
Fallo Renal Crónico/terapia , Impresión Molecular , Diálisis Renal/métodos , Urea/metabolismo , Acrilatos , Cromatografía Líquida de Alta Presión , Humanos , Metacrilatos , Polímeros/químicaRESUMEN
To maximize utilization of expensive laboratory instruments and to make most effective use of skilled human resources, the entire chain of data processing, calculation, and reporting that is needed to transform raw NMR data into meaningful results was automated. The LEAN process improvement tools were used to identify non-value-added steps in the existing process. These steps were eliminated using an in-house developed software package, which allowed us to meet the key requirement of improving quality and reliability compared with the existing process while freeing up valuable human resources and increasing productivity. Reliability and quality were improved by the consistent data treatment as performed by the software and the uniform administration of results. Automating a single NMR spectrophotometer led to a reduction in operator time of 35%, doubling of the annual sample throughput from 1400 to 2800, and reducing the turn around time from 6 days to less than 2.
Asunto(s)
Automatización de Laboratorios/métodos , Procesamiento Automatizado de Datos/métodos , Espectroscopía de Resonancia Magnética/métodos , Polímeros/análisis , Automatización de Laboratorios/normas , Procesamiento Automatizado de Datos/normas , Humanos , Programas Informáticos , Factores de TiempoRESUMEN
In our attempt to fully automate the data acquisition and processing of NMR analysis of dissolved synthetic polymers, phase correction was found to be the most challenging aspect. Several approaches in literature were evaluated but none of these was found to be capable of phasing NMR spectra with sufficient robustness and high enough accuracy to fully eliminate intervention by a human operator. Step by step, aspects from the process of manual/visual phase correction were translated into mathematical concepts and evaluated. This included area minimization, peak height maximization, negative peak minimization and baseline correction. It was found that not one single approach would lead to acceptable results but that a combination of aspects was required, in line again with the process of manual phase correction. The combination of baseline correction, area minimization and negative area penalization was found to give the desired results. The robustness was found to be 100% which means that the correct zeroth order and first order phasing parameters are returned independent of the position of the starting point of the search in this parameter space. When applied to high signal-to-noise proton spectra, the accuracy was such that the returned phasing parameters were within a distance of 0.1-0.4 degrees in the two dimensional parameter space which resulted in an average error of 0.1% in calculated properties such as copolymer composition and end groups.