This paper assesses how to measure the following aspects related to implantable bioartificial kidney. Chronic kidney conditions have led to increased incidence of morbidity and mortality in patients. The end stage renal disease has created a significant interest for critical care physicians, nephrologists and researchers in bioartificial kidney device.
Silicon Nonporous Hemofiltration Membranes
Microelectromechanical systems (MEMS) toolkit, which consists of silicon bulk, surface micromachining and microfabrication methods, offers exceptional management of nanoscale feature size and geometry in a scalable development process (Fissell et al., 2009). It is imperative to understand how controlling pore geometry may possibly offer new chances to exploit molecular transport rates. Relative to polydisperse porous materials, a membrane with similar pores may lower resistance of the flowing fluid and sustain molecular selectivity. It has been established that controlled pore shapes that are extended or slit also offer more reductions in fluid resistance relative uneven or round pores. Hence, MEMS-enabled membranes are significantly enhancing efficiency of bioartificial devices for renal replacement for both wearable and implantable ones. They can serve both bioartificial kidney and traditional interventions. Studies have evaluated biocompatibility of these materials to meet the set global standards, and they have shown that silicon-based MEMS substrates are comparably nontoxic and not reactive (Fissell et al., 2009).
Protein Sieving and Hemofiltration
Measuring protein sieving and hemofiltration involves examining for any defects a collection of membranes under differential conditions of contrast light microscopy (Fissell et al., 2009). Phosphate buffered saline ([PBS) is used to assess membranes hydraulic permeability. The rate of flow is observed by timed collection over varying pressures that range from 3.4 to 13.8 kPa. From these data, the size of the mean pore can be calculated. The mean size of the pore consists of the membrane thickness and long measurement of the slit pore, which are measured through electron microscopy scanning (Fissell et al., 2009). In addition, the measurement must account for the viscosity of the PBS and the slope, which are identified through linear regression on the noted rate of flow against pressure data.
Protein Adsorption
A technique used by Sharma and colleagues can be applied to measure protein adsorption for silicon-based MEMS substrates under tests. The membranes involved in protein adsorption are evaluated by conducting scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) (Fissell et al., 2009).
Protein adsorption requires imaging. An ImagePro software program can be used to analyze data in order to determine the mean pixel intensities based on 12-bit images from every substrate (Fissell et al., 2009). Pixel intensities are evaluated to determine protein binding for every kind of substrate used. It is vital to perform imaging for all controls and experimental membranes on a single day using the same procedures and exposure time. Albumin binding to tissue culture polystyrene can be used to reflect the percentage for albumin binding to bare substrates and PEG-modified substrates (Fissell et al., 2009).
X-ray photoelectron spectroscopy (XPS) is applied when evaluating biofouling tendencies of the membranes. It evaluates the general chemical characteristics of fresh membranes and other membranes “after 90 hours of hemofiltration using anti-coagulated blood” (Fissell et al., 2009). X-ray photoelectron spectra are also collected for protein adsorption measurement.
Blood Coagulation
A plasma recalcification time is used to measure blood coagulation by considering contact activation of the coagulation cascade by both bare and PEG-modified <100>-oriented Si samples and glass cover slips (Fissell et al., 2009). Xu and colleagues have described these techniques (Fissell et al., 2009). These measurements are done on appearance of a noticeable clot of fibrin against an initial strand. Blood samples are required from healthy individuals and then quickly centrifuged in order to get platelet-poor plasma. Other procedures then follow, but the most important part of the measurement process involves collecting data for fibrin clot formation (Fissell et al., 2009).
Solute Transport
To measure solute transport, data are collected using membranes collected on an ultrafiltration cell (Fissell et al., 2009). A driving pressure is used to evaluate solute transport, providing a filtrate flux of a given amount. In addition, the spatial mean velocity within the pore is also approximated to be nearly two orders of scale higher, showing the low porosity of the membrane. The shear rate within the feed area must also be obtained to determine the feed flow rate. Hence, the mass transfer coefficient can be approximated from these data. The protein diffusion coefficient, the wall shear rate within the feed channel and the length of the channel are all covered in the measurement solute transport (Fissell et al., 2009).
Therefore, the measurement should give a sensible approximation of concentration polarization effects by considering the completely retained solute, the solute concentration directly close to the membrane and the solute concentration found in the feed or bulk solution.
Glomerular Filtration Rate (GFR)
GFR measures the quantity of “plasma filtered via glomeruli for a specific time” (Molitoris, 2012). GFR is the clinically most widely adopted measure for evaluating effective kidney function (Molitoris, 2012). In addition, GFR is a vital single parameter that accounts for the complex roles of the kidney. Thus, the GFR estimates are recommended for “the definition, classification, screening and monitoring of kidney diseases” (Rehling, 2012).
Measuring GFR in Bioartificial Kidney Device
Various equations have been developed to estimate GFR (eGFR) for the general population. However, it is imperative to note that currently no specific studies that focus on how to measure GFR in bioartificial kidney device have been conducted. Therefore, the most reliable methods for measuring GFR can be applied for a bioartificial kidney device too.
Previously, the produced creatinine in the urine was used to measure GFR, but this technique was difficult and unreliable. In the recent years, however, creatinine estimation from existing “empirical equations has become a common approach used when measuring GFR” (Rehling, 2012). That is, no actual measuring of creatinine production in an individual patient takes place (Rehling, 2012). Instead, the same values for other patients may be used to estimate GFR. Hence, the Modified Diet in Renal Disease (MDRD) formula is often used to determine GFR. It is also noted that the eGFR is more reliable than the measure for creatinine concentration.
Nevertheless, eGFR is not universal standard and certainly not a superior measurement method for observing fluctuations in GFR to creatinine concentration (Rehling, 2012). The method of estimating GFR is simple, flexible and affordable. Exogenous tracers may be applied to assess GFR because of some merits, particularly evaluating GFR from plasma clearance (Rehling, 2012). Plasma clearance, which is recommended when an accurate value of GFR is required, is “excretion rate relative to the plasma concentration or from the ratio between the injected dose and the area under the plasma concentration curve” (Rehling, 2012). In addition, the method is reliable because no urine is collected to measure GFR.
References
Fissell, W. H., Dubnisheva, A., Eldridge, A. N., Fleischman, A. J., Zydney, A. L., & Roy, S. (2009). High-Performance Silicon Nanopore Hemofiltration Membranes. Journal of Membrane Science, 326(1), 58–63. doi:10.1016/j.memsci.2008.09.039.
Molitoris, B. A. (2012). Measuring glomerular filtration rate in acute kidney injury: Yes, but not yet. Critical Care, 16(5), 158. doi: 10.1186/cc11482.
Rehling, M. (2012). Measuring glomerular filtration rate from plasma clearance of 51Cr- EDTA: quality assurance. European Journal of Nuclear Medicine and Molecular Imaging, 39, 713–714. doi 10.1007/s00259-012-2073-4.