Oxygen Supplementing, Biocompatible Outer Membranes for Enhanced Performance of Implantable Glucose Sensors

Lack of linearity and sensitivity, oxygen dependence, biofouling and tissue inflammation hinder the development of implantable biosensors for continuous monitoring of glucose. Herein, we report the development of stacked outer membranes based on LBL/PVA hydrogels that improve sensor sensitivity, linearity, oxygen independence and counter biofouling and inflammation. While the inner LBL membrane affords tunable diffusivity, the outer PVA is capable of releasing anti-inflammatory drugs/tissue response modifying agents to counter acute and chronic inflammation, and to induce neo-angiogenesis at the implant site. Sensors were fabricated by immobilizing GOx enzyme on top of 50 μm platinum wires, followed by deposition of stacked LBL/PVA hydrogel membranes. The response of the sensors at 0.7V to various glucose concentrations was studied. Michelis-Menten analysis was performed to quantify sensor performance in terms of linearity and oxygen dependence. The interplay between sensor performance and inward glucose diffusivity was elucidated using (i) various LBL membranes and (ii) various freeze-thaw (FT) cycles of PVA. Incorporation of LBL/PVA stacked membranes resulted in an 8 fold increase in sensor linearity and a 9 fold decrease in oxygen dependence compared to controls. The enhancement in the sensor performance is attributed to (i) the oxygen storing capability of PVA hydrogel due to the formation of hydrophobic domains during its freezing/ thawing employed for its physical crosslinking and (ii) regulation of glucose flux by the inner LBL membrane. Such membranes offer significant advantages over presently available outer membranes in lieu of (i) their ability to control inflammation, (ii) their modulus that closely matches that of subcutaneous human tissue, (iii) non-necessity of reactive chemical crosslinking agents, (iv) tunable sensitivity and (v) supplemental storage of oxygen.

Validation of a Spatially Continuous EDEN Water-Surface Model for the Everglades, Florida

The Everglades Depth Estimation Network (EDEN) is an integrated network of realtime water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (2000-present), online water-stage and water-depth information for the entire freshwater portion of the Greater Everglades. Continuous daily spatial interpolations of the EDEN network stage data are presented on grid with 400-square-meter spacing. EDEN offers a consistent and documented dataset that can be used by scientists and managers to: (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan (CERP) (U.S. Army Corps of Engineers, 1999). The target users are biologists and ecologists examining trophic level responses to hydrodynamic changes in the Everglades. The first objective of this report is to validate the spatially continuous EDEN water-surface model for the Everglades, Florida developed by Pearlstine et al. (2007) by using an independent field-measured data-set. The second objective is to demonstrate two applications of the EDEN water-surface model: to estimate site-specific ground elevation by using the validated EDEN water-surface model and observed water depth data; and to create water-depth hydrographs for tree islands. We found that there are no statistically significant differences between model-predicted and field-observed water-stage data in both southern Water Conservation Area (WCA) 3A and WCA 3B. Tree island elevations were derived by subtracting field water-depth measurements from the predicted EDEN water-surface. Water-depth hydrographs were then computed by subtracting tree island elevations from the EDEN water stage. Overall, the model is reliable by a root mean square error (RMSE) of 3.31 cm. By region, the RMSE is 2.49 cm and 7.77 cm in WCA 3A and 3B, respectively. This new landscape-scale hydrological model has wide applications for ongoing research and management efforts that are vital to restoration of the Florida Everglades. The accurate, high-resolution hydrological data, generated over broad spatial and temporal scales by the EDEN model, provides a previously missing key to understanding the habitat requirements and linkages among native and invasive populations, including fish, wildlife, wading birds, and plants. The EDEN model is a powerful tool that could be adapted for other ecosystem-scale restoration and management programs worldwide.