Metabolic biofouling of glucose sensors in vivo: role of tissue microhemorrhages

Insertable Biosensors: Combining Implanted Sensing Materials with Wearable Monitors

Insertable biosensor systems are medical diagnostic devices with two primary components: an implantable biosensor within the body and a wearable monitor that can remotely interrogate the biosensor from outside the body. Because the biosensor does not require a physical connection to the electronic monitor, insertable biosensor systems promise improved patient comfort, reduced inflammation and infection risk, and extended operational lifetimes relative to established percutaneous biosensor systems. However, the lack of physical connection also presents technical challenges that have necessitated new innovations in developing sensing chemistries, transduction methods, and communication modalities. In this review, we discuss the key developments that have made insertables a promising option for longitudinal biometric monitoring and highlight the essential needs and existing development challenges to realizing the next generation of insertables for extended-use diagnostic and prognostic devices.

Glucose biosensors based on Michael addition crosslinked poly(ethylene glycol) hydrogels with chemo-optical sensing microdomains

Continuous glucose monitoring (CGM) devices have the potential to lead to better disease management and improved outcomes in patients with diabetes. Chemo-optical glucose sensors offer a promising, accurate, long-term alternative to the current CGMs that require frequent calibration and replacement. Recently, we have proposed glucose sensor designs using phosphorescence lifetime-based measurement of chemo-optical glucose sensing microdomains embedded within alginate hydrogels. Due to the poor long-term stability of calcium-crosslinked alginate, we propose poly(ethylene glycol) (PEG) hydrogels synthesized via thiol-Michael addition chemistry as an alternative hydrogel carrier. The objective of this study was to evaluate the suitability of Michael addition crosslinked PEG hydrogels compared to calcium crosslinked alginate hydrogels for encapsulating glucose-sensing microdomains. PEG hydrogels crosslinked via thiol-vinyl sulfone addition achieved gelation in under 5 minutes, resulting in an even distribution of sensing microdomains. The shear storage modulus of the PEG hydrogels was tunable from 2.2 ± 0.1 kPa to 9.5 ± 1.8 kPa, which was comparable to the alginate hydrogels (10.5 ± 0.8 kPa), and the inclusion of microdomains did not significantly impact stiffness. The high water content of PEG hydrogels resulted in high glucose permeability that closely corresponded to the glucose permeability of alginate (D = 0.09 and 0.12 cm² s^-1, respectively; p = 0.47), but the PEG hydrogels exhibited superior stability. Both PEG and alginate-embedded sensors exhibited a sensing range up to ∼200 mg dL^-1 glucose. The lower limits of detection (LOD) for PEG and alginate-based glucose sensors were 19.8 and 20.6 mg dL^-1 with a difference of just 4.2% variation. The small difference between PEG and alginate embedded sensors indicates that their sensing properties are primarily determined by the glucose sensing microdomains rather than the hydrogel matrix. Overall, the results of this study indicate that Michael addition-crosslinked PEG hydrogels are a promising platform for encapsulation of chemo-optical glucose sensing microdomains.

Should continuous glucose monitoring be used to manage neonates at risk of hypoglycaemia?

The National Institute for Clinical Excellence (NICE) now recommends that continuous glucose monitoring (CGM) be offered to adults and children with diabetes who are at risk from hypoglycaemia. Hypoglycaemia is common in the neonatal period, and is a preventable cause of poor neurodevelopmental outcome, but is CGM helpful in the management of neonates at risk of hypoglycaemia? Neonatal studies have shown that CGM can detect clinically silent hypoglycaemia, which has been associated with reduced executive and visual function in early childhood. Intervention trials have further shown CGM can support the targeting of glucose levels in high-risk extremely preterm neonates. In spite of significant advances in technology, including smaller sensors, better accuracy and factory calibration, further progress and adoption into clinical practice has been limited as current devices are not designed nor have regulatory approval for the specific needs of the newborn. The use of CGM has the potential to support clinical management, and prevention of hypoglycaemia but must be set within its current limitations. The data CGM provides however also provides an important opportunity to improve our understanding of potential risks of hypoglycaemia and the impact of clinical interventions to prevent it.

Continuous glucose monitoring and metrics for clinical trials: an international consensus statement

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management

The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well-poised for the next generation of closed-loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on-tooth tattoos, skin-mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real-time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self-regulating closed-loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management.

Continuous Glucose Monitoring Devices: Past, Present, and Future Focus on the History and Evolution of Technological Innovation

The concept of implantable glucose sensors has been promulgated for more than 40 years. It is now accepted that continuous glucose monitoring (CGM) increases quality of life by allowing informed diabetes management decisions as a result of more optimized glucose control. The focus of this article is to provide a brief overview of the CGM market history, emerging technologies, and the foreseeable challenges for the next CGM generations as well as proposing possible solutions in an effort to advance the next generation of implantable sensor.

Lubricin as a tool for controlling adhesion in vivo and ex vivo

The ability to prevent or minimize the accumulation of unwanted biological materials on implantable medical devices is important in maintaining the long-term function of implants. To address this issue, there has been a focus on materials, both biological and synthetic, that have the potential to prevent device fouling. In this review, we introduce a glycoprotein called lubricin and report on its emergence as an effective antifouling coating material. We outline the versatility of lubricin coatings on different surfaces, describe the physical properties of its monolayer structures, and highlight its antifouling properties in improving implant compatibility as well as its use in treatment of ocular diseases and arthritis. This review further describes synthetic polymers mimicking the lubricin structure and function. We also discuss the potential future use of lubricin and its synthetic mimetics as antiadhesive biomaterials for therapeutic applications.

Review of the Long-Term Implantable Senseonics Continuous Glucose Monitoring System and Other Continuous Glucose Monitoring Systems

The article published by Kevin Cowart in this issue of the Journal of Diabetes Science and Technology (JDST) is a detailed overview of the clinical trial data and analysis used to demonstrate the safety and effectiveness of the Eversense continuous glucose monitoring (CGM) System for regulatory approval and clinical acceptance. The article describes the published study results for safety, accuracy, reliability, ease of insertion/removal, adverse events, and ease of diabetes patient-use for controlling their glucose levels short and long term. The author nicely compares Eversense CGM System safety and performance with the short-term subcutaneous tissue CGM systems being commercialized by Dexcom, Medtronic Diabetes, and Abbott Diabetes. This comparison may help the clinician define which type of patient with diabetes might benefit the most from the long-term implantable CGM system. The majority of studied patients describe a positive experience managing their diabetes with the Eversense CGM System and request implantation of a new sensor 90 or 180 days later.

Microgel encapsulated nanoparticles for glucose-responsive insulin delivery

An insulin delivery system that self-regulates blood glucose levels has the potential to limit hypoglycemic events and improve glycemic control. Glucose-responsive insulin delivery systems have been developed by coupling glucose oxidase with a stimuli-responsive biomaterial. However, the challenge of achieving desirable release kinetics (i.e., insulin release within minutes after glucose elevation and duration of release on the order of weeks) still remains. Here, we develop a glucose-responsive delivery system using encapsulated glucose-responsive, acetalated-dextran nanoparticles in porous alginate microgels. The nanoparticles respond rapidly to changes in glucose concentrations while the microgels provide them with protection and stability, allowing for extended glucose-responsive insulin release. This system reduces blood sugar in a diabetic mouse model at a rate similar to naked insulin and responds to a glucose challenge 3 days after administration similarly to a healthy animal. With 2 doses of microgels containing 60 IU/kg insulin each, we are able to achieve extended glycemic control in diabetic mice for 22 days.

Tissue Response to Subcutaneous Infusion Catheter

Insulin infusion pump, continuous glucose monitoring (CGM), and insulin infusion set (IIS) have been developed to be increasingly feasible for people with type 1 diabetes (T1D). Several recently approved CGMs are transitioning from 7-day to 10-day wear time without the need for fingerprick recalibration. Nevertheless, studies and improvements on IIS, a critical part of insulin pump therapy, have been limited. In particular, the recommended wear time of IIS is still 2-3 days, which can hardly match the current duration of CGM for potential closed-loop system development. It is generally believed that both the inserted catheter and the subsequent infused insulin drug could induce particular subcutaneous tissue response and skin-related complications at the infusion site. In certain cases, poor glycaemic control, increased risk of hypoglycemia, and serious cosmetic impact on people with diabetes were observed. Skin complication has also been attributed as an important factor resulting users to discontinue insulin pump therapy. This article provides the rare systematic review of IIS induced subcutaneous tissue responses and skin complications, including the impacts from the inserted catheters, the subcutaneous infused insulin, and the adhesive or tape used to immobilize the catheter. The FDA's recommendation for the frequency of IIS change was further discussed. Future studies on this topic are required to further understand the IIS-related problems, and future strategies could be developed accordingly to significantly reduce the incidence of these problems, extend the wear time, and increase the acceptance of insulin pump based therapy.

Factory-Calibrated Continuous Glucose Monitoring: How and Why It Works, and the Dangers of Reuse Beyond Approved Duration of Wear

Continuous glucose monitors (CGM) display real-time glucose values enabling greater glycemic awareness with reduced management burden. Factory-calibrated CGM systems allow for glycemic assessment without the pain and inconvenience of fingerstick glucose testing. Advances in sensor chemistry and CGM algorithms have enabled factory-calibrated systems to have greater accuracy than previous generations of CGM technology. Despite these advances many patients and providers are hesitant about the idea of removing fingerstick testing from their diabetes care. In this commentary, we aim to review the clinical trials on factory-calibrated CGM systems, present the algorithms which facilitate factory-calibrated CGMs to improve accuracy, discuss clinical use of factory-calibrated CGMs, and finally present two cases demonstrating the dangers of utilizing exploits in commercial systems to prolong sensor life.

Bayesian Model Selection Framework to Improve Calibration of Continuous Glucose Monitoring Sensors for Diabetes Management

Minimally-invasive continuous glucose monitoring (CGM) sensors have revolutionized perspectives in the treatment of type 1 diabetes (T1D). Their accuracy relies on an internal calibration function that transforms the raw, physically measured, electrical data into blood glucose concentration values. Usually, a unique, pre-determined, calibration functional is adopted, with parameters periodically updated in individual patients by using "gold standard" references suitably collected by finger prick devices. However, retrospective analysis of CGM data suggests that variability of sensor-subject characteristics is often inefficiently coped with. In the present study, we propose a conceptual Bayesian model- selection framework aimed at guaranteeing wide margins of flexibility for both the determination of the most appropriate calibration functional and the numerical values of its unknown parameters. The calibration model is determined among a finite specified set of candidates, each one depending on a set of unknown model parameters, for which a priori statistical expectations are available. Model selection is based on predictive distributions carrying out asymptotic calculations through Monte Carlo integration methods. Performance of the proposed approach is assessed on synthetic data generated by a well-established T1D simulation model.

Effect of sensor location on continuous intraperitoneal glucose sensing in an animal model

Background

In diabetes research, the development of the artificial pancreas has been a major topic since continuous glucose monitoring became available in the early 2000’s. A prerequisite for an artificial pancreas is fast and reliable glucose sensing. However, subcutaneous continuous glucose monitoring carries the disadvantage of slow dynamics. As an alternative, we explored continuous glucose sensing in the peritoneal space, and investigated potential spatial differences in glucose dynamics within the peritoneal cavity. As a secondary outcome, we compared the glucose dynamics in the peritoneal space to the subcutaneous tissue.

Material and methods

Eight-hour experiments were conducted on 12 anesthetised non-diabetic pigs. Four commercially available amperometric glucose sensors (FreeStyle Libre, Abbott Diabetes Care Ltd., Witney, UK) were inserted in four different locations of the peritoneal cavity and two sensors were inserted in the subcutaneous tissue. Meals were simulated by intravenous infusions of glucose, and frequent arterial blood and intraperitoneal fluid samples were collected for glucose reference.

Results

No significant differences were discovered in glucose dynamics between the four quadrants of the peritoneal cavity. The intraperitoneal sensors responded faster to the glucose excursions than the subcutaneous sensors, and the time delay was significantly smaller for the intraperitoneal sensors, but we did not find significant results when comparing the other dynamic parameters.

Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient's discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

A prolonged run-in period of standard subcutaneous microdialysis ameliorates quality of interstitial glucose signal in patients after major cardiac surgery

We evaluated a standard subcutaneous microdialysis technique for glucose monitoring in two critically ill patient populations and tested whether a prolonged run-in period improves the quality of the interstitial glucose signal. 20 surgical patients after major cardiac surgery (APACHE II score: 10.1 ± 3.2) and 10 medical patients with severe sepsis (APACHE II score: 31.1 ± 4.3) were included in this investigation. A microdialysis catheter was inserted in the subcutaneous adipose tissue of the abdominal region. Interstitial fluid and arterial blood were sampled in hourly intervals to analyse glucose concentrations. Subcutaneous adipose tissue glucose was prospectively calibrated to reference arterial blood either at hour 1 or at hour 6. Median absolute relative difference of glucose (MARD), calibrated at hour 6 (6.2 (2.6; 12.4) %) versus hour 1 (9.9 (4.2; 17.9) %) after catheter insertion indicated a significant improvement in signal quality in patients after major cardiac surgery (p < 0.001). Prolonged run-in period revealed no significant improvement in patients with severe sepsis, but the number of extreme deviations from the blood plasma values could be reduced. Improved concurrence of glucose readings via a 6-hour run-in period could only be achieved in patients after major cardiac surgery.

Toward Calibration-Free Continuous Glucose Monitoring Sensors: Bayesian Calibration Approach Applied to Next-Generation Dexcom Technology

BACKGROUND

Continuous glucose monitoring (CGM) sensors need to be calibrated twice/day by using self-monitoring of blood glucose (SMBG) samples. Recently, to reduce the calibration frequency, we developed an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation. When applied to Dexcom G4 Platinum (DG4P) sensor data, the algorithm allowed the frequency of calibrations to be reduced to one-every-four-days without significant worsening of sensor accuracy. The aim of this study is to assess the same methodology on raw CGM data acquired by a next-generation Dexcom CGM sensor prototype and compare the results with that obtained on DG4P.

METHODS

The database consists of 55 diabetic subjects monitored for 10 days by a next-generation Dexcom CGM sensor prototype. The new calibration algorithm is assessed, retrospectively, by simulating an online procedure using progressively fewer SMBG samples until zero. Accuracy is evaluated with mean absolute relative differences (MARD) between blood glucose versus CGM values.

RESULTS

The one-per-day and one-every-two-days calibration scenarios in the next-generation CGM data have an accuracy of 8.5% MARD (vs. 11.59% of DG4P) and 8.4% MARD (vs. 11.63% of DG4P), respectively. Accuracy slightly worsens to 9.2% (vs. 11.62% of DG4P) when calibrations are reduced to one-every-four-days. The calibration-free scenario results in 9.3% MARD (vs. 12.97% of DG4P).

CONCLUSIONS

In next-generation Dexcom CGM sensor data, the use of an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation aids in the shift toward a calibration-free scenario with even better results than those obtained in present-generation sensors.

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.

Future of Automated Insulin Delivery Systems

Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.

Sensor Life and Overnight Closed Loop: A Randomized Clinical Trial

BACKGROUND

Closed-loop (CL) systems direct insulin delivery based on continuous glucose monitor (CGM) sensor values. CGM accuracy varies with sensor life, being least accurate on day 1 of sensor insertion. We evaluated the effect of sensor life (enhanced Enlite, Medtronic MiniMed, Northridge, CA) on overnight CL.

METHODS

In an open-label, randomized, 2-period, inpatient crossover pilot study, 12 adolescents on insulin pump (age 16.7 ± 1.9 years; HbA1c 66 ± 10 mmol/mol) attended a clinical research facility on 2 overnight occasions. In random order, participants received CL on day 1 or on day 3-4 after sensor insertion. During both periods, glucose was automatically controlled by a model predictive control algorithm informed by sensor glucose. Plasma glucose was measured every 30 to 60 min.

RESULTS

During overnight CL (22:30 to 07:30), the proportion of time with plasma glucose readings in the target range (3.9-8.0 mmol/l, primary endpoint) when initiated on day 1 of sensor insertion vs day 3-4 were comparable (58 ± 32% day 1 vs 56 ± 36% day 3-4; P = .34), and there were no significant differences between interventions in terms of mean plasma glucose ( P = .26), percentage time above 8.0 mmol/l ( P = .49), and time spent below 3.9 mmol/l ( P = .93). Sensor accuracy varied with sensor life (mean absolute relative difference 19.8 ± 15.0% on day 1 and 13.7 ± 10.2% on day 3 to 4). Sensor glucose tended to under-read plasma glucose inflating benefits of CL on glucose control.

CONCLUSIONS

In spite of differences in sensor accuracy, overnight CL glucose control informed by sensor glucose on day 1 or day 3-4 after sensor insertion was comparable. The model predictive controller appears to mitigate against sensor inaccuracies.

Tissue-Integrating Oxygen Sensors: Continuous Tracking of Tissue Hypoxia

We describe a simple method of tracking oxygen in real-time with injectable, tissue-integrating microsensors. The sensors are small (500 μm × 500 μm × 5 mm), soft, flexible, tissue-like, biocompatible hydrogel s that have been shown to overcome the foreign body response for long-term sensing. The sensors are engineered to change luminescence in the presence of oxygen or other analytes and function for months to years in the body. A single injection followed by non-invasive monitoring with a hand-held or wearable Bluetooth optical reader enables intermittent or continuous measurements. Proof of concept for applications in high altitude, exercise physiology, vascular disease, stroke, tumors, and other disease states have been shown in mouse, rat and porcine models. Over 90 sensors have been studied to date in humans. These novel tissue-integrating sensors yield real-time insights in tissue oxygen fluctuations for research and clinical applications.

From Two to One Per Day Calibration of Dexcom G4 Platinum by a Time-Varying Day-Specific Bayesian Prior

BACKGROUND

In the DexCom G4 Platinum (DG4P) continuous glucose monitoring (CGM) sensor, the raw current signal generated by glucose-oxidase is transformed to glucose concentration by a calibration function whose parameters are periodically updated by matching self-monitoring of blood glucose references, usually twice a day, to compensate for sensor variability in time. The aim of this work is to reduce DG4P calibration frequency to once a day by a recently proposed Bayesian calibration algorithm, which employs a time-varying calibration function and suitable day-specific priors.

METHODS

The database consists of 57 CGM signals that are collected by the DG4P for 7 days. The Bayesian calibration algorithm is used to calibrate the raw current signal following two different schedules, that is, two and one calibration per day. Calibrated glycemic profiles are compared with those originally acquired by the manufacturer, on days 1, 4, and 7, where frequent blood glucose references were available, by using standard metrics, that is, mean absolute relative difference (MARD), percentage of accurate glucose estimates, and percentage of data in the A-zone of Clarke Error Grid.

RESULTS

The one per day Bayesian calibration algorithm has accuracy similar to that of two per day (11.8% vs. 11.7% MARD, respectively), and it is statistically better (P-value of 0.0411) than the manufacturer calibration algorithm, which requires two calibrations per day (13.1% MARD).

CONCLUSIONS

A Bayesian calibration algorithm employing a time-varying calibration function and suitable priors enables a reduction of the calibrations of DG4P sensor from two to one per day.

Electrochemical biochip for applications to wireless and batteryless monitoring of free-moving mice

A multi-sensing platform for applications in wireless and batteryless monitoring of free-moving small animals is presented in this paper. The proposed platform hosts six sensors: four biosensors for sensing of both disease biomarkers and therapeutic compounds, and two further sensors (T and pH) for biosensor calibration. Electrodeposition of Multi-Walled Carbon Nanotubes (MWCNTs) and the subsequent function-alization with proper enzymes is used to assure sensitivity and specificity in electrochemical biosensing. The realized sensors are demonstrated to be capable of measuring several parameters: lactate with a sensitivity of 77±26 μA/mM· cm(2) and a limit of detection (LOD) of 4±1 μM; glucose with a sensitivity of 63±15 μA/mM· cm(2) and a LOD of 8±2 μM; Etoposide (a well known anti-cancer agent) with a sensitivity of 0.15±0.04 mA/mM· cm(2) and a LOD of 4±1 μM; Open Circuit Potential (OCP) measurements are used on a Pt/IrOx junction to sense pH with a sensitivity of around -75±5mV/pH; while a Pt resistive thermal device is used to measure physiological temperature-range with an average sensitivity of 0.108±0.001 kΩ/°C.

Impact of CCL2 and CCR2 chemokine/receptor deficiencies on macrophage recruitment and continuous glucose monitoring in vivo

The accumulation of macrophages (MΦ) at the sensor-tissue interface is thought to be a major player in controlling tissue reactions and sensor performance in vivo. Nevertheless until recently no direct demonstration of the causal relationship between MΦ aggregation and loss of sensor function existed. Using a Continuous Glucose Monitoring (CGM) murine model we previously demonstrated that genetic deficiencies of MΦ or depletion of MΦ decreased MΦ accumulation at sensor implantation sites, which led to significantly enhanced CGM performance, when compared to normal mice. Additional studies in our laboratories have also demonstrated that MΦ can act as "metabolic sinks" by depleting glucose levels at the implanted sensors in vitro and in vivo. In the present study we extended these observations by demonstrating that MΦ chemokine (CCL2) and receptor (CCR2) knockout mice displayed a decrease in inflammation and MΦ recruitment at sensor implantation sites, when compared to normal mice. This decreased MΦ recruitment significantly enhanced CGM performance when compared to control mice. These studies demonstrated the importance of the CCL2 family of chemokines and related receptors in MΦ recruitment and sensor performance and suggest chemokine targets for enhancing CGM in vivo.

Redundancy in Glucose Sensing: Enhanced Accuracy and Reliability of an Electrochemical Redundant Sensor for Continuous Glucose Monitoring

BACKGROUND

Current electrochemical glucose sensors use a single electrode. Multiple electrodes (redundancy) may enhance sensor performance. We evaluated an electrochemical redundant sensor (ERS) incorporating two working electrodes (WE1 and WE2) onto a single subcutaneous insertion platform with a processing algorithm providing a single real-time continuous glucose measure.

METHODS

Twenty-three adults with type 1 diabetes each wore two ERSs concurrently for 168 hours. Post-insertion a frequent sampling test (FST) was performed with ERS benchmarked against a glucose meter (Bayer Contour Link). Day 4 and 7 FSTs were performed with a standard meal and venous blood collected for reference glucose measurements (YSI and meter). Between visits, ERS was worn with capillary blood glucose testing ≥8 times/day. Sensor glucose data were processed prospectively.

RESULTS

Mean absolute relative deviation (MARD) for ERS day 1-7 (3,297 paired points with glucose meter) was (mean [SD]) 10.1 [11.5]% versus 11.4 [11.9]% for WE1 and 12.0 [11.9]% for WE2; P < .0001. ERS Clarke A and A+B were 90.2% and 99.8%, respectively. ERS day 4 plus day 7 MARD (1,237 pairs with YSI) was 9.4 [9.5]% versus 9.6 [9.7]% for WE1 and 9.9 [9.7]% for WE2; P = ns. ERS day 1-7 precision absolute relative deviation (PARD) was 9.9 [3.6]% versus 11.5 [6.2]% for WE1 and 10.1 [4.4]% for WE2; P = ns. ERS sensor display time was 97.8 [6.0]% versus 91.0 [22.3]% for WE1 and 94.1 [14.3]% for WE2; P < .05.

CONCLUSIONS

Electrochemical redundancy enhances glucose sensor accuracy and display time compared with each individual sensing element alone. ERS performance compares favorably with 'best-in-class' of non-redundant sensors.

Continuous Glucose Monitoring, Future Products, and Update on Worldwide Artificial Pancreas Projects

The development of accurate and easy-to-use continuous glucose monitoring (CGM) improved diabetes treatment by providing additional temporal information on glycemia and glucose trends to patient and physician. Although CGM enables users to lower their average glucose level without an increased incidence of hypoglycemia, this comes at the price of additional patient effort. Automation of insulin administration, also known as closed-loop (CL) or artificial pancreas treatment, has the promise to reduce patient effort and improve glycemic control. CGM data serve as the conditional input for insulin automation devices. The first commercial product for partial automation of insulin administration used insulin delivery shutoff at a predefined glucose level. These systems showed a reduction in hypoglycemia. Insulin-only CL devices show increased time spent in euglycemia and a reduction of hypo- and hyperglycemia. Improved glycemic control, coinciding with a minor decrease in hemoglobin A1c level, was confirmed in recent long-term home studies investigating these devices, paving the way for pivotal studies for commercialization of the artificial pancreas. Although the first results from dual-hormone CL systems are promising, because of increased cost of consumables of these systems, long-term head-to-head studies will have to prove superiority over insulin-only approaches. Now CL glucose control for daily use might finally become reality. Improved continuous glucose sensing technology, miniaturization of electrical devices, and development of algorithms were key in making this possible. Clinical adoption challenges, including device usability and reimbursement, need to be addressed. Time will tell for which patient groups CL systems will be reimbursed and whether these devices can deliver the promise that they hold.

Time Delay of CGM Sensors: Relevance, Causes, and Countermeasures

BACKGROUND

Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these.

METHODS

CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay.

RESULTS

Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average.

CONCLUSIONS

Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.

The First-in-Man "Si Se Puede" Study for the use of micro-oxygen sensors (MOXYs) to determine dynamic relative oxygen indices in the feet of patients with limb-threatening ischemia during endovascular therapy

OBJECTIVE

Patients with limb-threatening ischemia exhibit uneven patterns of perfusion in the foot, which makes it challenging to determine adequate topographic perfusion by angiography alone. This study assessed the feasibility of reporting dynamic relative oxygen indices and tissue oxygen concentration from multiple locations on the foot during endovascular therapy using a novel micro-oxygen sensor (MOXY; PROFUSA, Inc, South San Francisco, Calif) approach.

METHODS

A prospective, 28-day, single-arm, observational study was performed in 10 patients who underwent endovascular therapy for limb-threatening ischemia. At least 24 hours before therapy, four microsensors were injected in each patient (one in the arm, three in the treated foot). The optical signal from the microsensors corresponded to tissue oxygen concentration. A custom detector on the surface of the skin was used to continuously and noninvasively measure the signals from the microsensors. The ability to locate and read the signal from each injected microsensor was characterized. Oxygen data from the microsensors were collected throughout the revascularization procedure. The timing of therapy deployment was recorded during the procedure to assess its relationship with the microsensor oxygen data. Oxygen data collection and clinical evaluation were performed immediately postoperatively as well as postoperatively on days 7, 14, 21, and 28.

RESULTS

The study enrolled 10 patients (50% male) with ischemia (30% Rutherford class 4, 70% Rutherford class 5). Patients were a mean age of 70.7 years (range, 46-90 years), and all were Hispanic of varying origin. Microsensors were successfully read 206 of 212 times (97.2%) in all patients during the course of the study. Microsensors were compatible with intraoperative use in the interventional suite and postoperatively in an office setting. In nine of 10 revascularization procedures, at least one of the three MOXYs showed an immediate change in the dynamic relative oxygen index, correlating to deployed therapy. Moreover, there was a statistically significant increase in the concentration of oxygen in the foot in preoperative levels compared with postoperative levels. No adverse events occurred related to the microsensor materials.

CONCLUSIONS

This MOXY approach appears to be safe when implanted in patients with limb-threatening ischemia undergoing endovascular recanalization and is effective in reporting local tissue oxygen concentrations over a course of 28 days. Further testing is needed to determine its potential effect on clinical decision making, both acutely on-table and chronically as a surveillance modality, which ultimately can lead to improved healing and limb salvage.

Modeling the Physiological Factors Affecting Glucose Sensor Function in Vivo

For implantable sensors to become a more viable option for continuous glucose monitoring strategies, they must be able to persist in vivo for periods longer than the 3- to 7-day window that is the current industry standard. Recent studies have attributed such limited performance to tissue reactions resulting from implantation. While in vivo biocompatibility studies have provided much in the way of understanding histology surrounding an implanted sensor, little is known about how each constituent of the foreign body response affects sensor function. Due to the ordered composition and geometry of implant-associated tissue reactions, their effects on sensor function may be computationally modeled and analyzed in a way that would be prohibitive using in vivo studies. This review both explains how physiologically accurate computational models of implant-associated tissue reaction can be designed and shows how they have been utilized thus far. Going forward, these in silico models of implanted sensor behavior may soon complement in vivo studies to provide valuable information for improved sensor designs.

A novel online recalibration strategy for continuous glucose measurement sensors employing LMI techniques

This paper considers the problem of online calibration and recalibration of continuous glucose monitoring devices. A parametric relation between interstitial and blood glucose is investigated and a constructive algorithm to adaptively estimate the parameters within this relation is proposed. The algorithm explicitly considers measurement uncertainty of the device used to collect the calibration measurements and enables automatic detection of measurements which are not suitable to be used for calibration. The method was assessed on clinical data from 17 diabetic patients and the improvements with respect to the current state of the art is shown.

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors

GOAL

Minimally invasive continuous glucose monitoring (CGM) sensors measure in the subcutis a current signal, which is converted into interstitial glucose (IG) concentration by a calibration process periodically updated using fingerstick blood glucose (BG) references. Though important in diabetes management, CGM sensors still suffer from accuracy problems. Here, we propose a new online calibration method improving accuracy of CGM glucose profiles with respect to manufacturer calibration.

METHOD

The proposed method fits CGM current signal against the BG references collected twice a day for calibration purposes, by a time-varying calibration function whose parameters are identified in a Bayesian framework using a priori second-order statistical knowledge. The distortion introduced by BG-to-IG kinetics is compensated before parameter identification via nonparametric deconvolution.

RESULTS

The method was tested on a database where 108 CGM signals were collected for 7 days by the Dexcom G4 Platinum sensor. Results show the new method drives to a statistically significant accuracy improvement as measured by three commonly used metrics: mean absolute relative difference reduced from 12.73% to 11.47%; percentage of accurate glucose estimates increased from 82.00% to 89.19%; and percentage of values falling in the "A" zone of the Clark error grid increased from 82.22% to 88.86%.

CONCLUSION

The new calibration method significantly improves CGM glucose profiles accuracy with respect to manufacturer calibration.

SIGNIFICANCE

The proposed algorithm provides a real-time improvement of CGM accuracy, which can be crucial in several CGM-based applications, including the artificial pancreas, thus providing a potential great impact in the diabetes technology research community.

Advances in management of type 1 diabetes mellitus

Treatment of type 1 diabetes mellitus has always posed a challenge to balance hyperglycemia control with hypoglycemia episodes. The quest for newer therapies is continuing and this review attempts to outline the recent developments. The insulin molecule itself has got moulded into different analogues by minor changes in its structure to ensure well controlled delivery, stable half-lives and lesser side effects. Insulin delivery systems have also consistently undergone advances from subcutaneous injections to continuous infusion to trials of inhalational delivery. Continuous glucose monitoring systems are also becoming more accurate and user friendly. Smartphones have also made their entry into therapy of diabetes by integrating blood glucose levels and food intake with calculated adequate insulin required. Artificial pancreas has enabled to a certain extent to close the loop between blood glucose level and insulin delivery with devices armed with meal and exercise announcements, dual hormone delivery and pramlintide infusion. Islet, pancreas-kidney and stem cells transplants are also being attempted though complete success is still a far way off. Incorporating insulin gene and secretary apparatus is another ambitious leap to achieve insulin independence though the search for the ideal vector and target cell is still continuing. Finally to stand up to the statement, prevention is better than cure, immunological methods are being investigated to be used as vaccine to prevent the onset of diabetes mellitus.

Role of vascular networks in extending glucose sensor function: Impact of angiogenesis and lymphangiogenesis on continuous glucose monitoring in vivo

The concept of increased blood vessel (BV) density proximal to glucose sensors implanted in the interstitial tissue increases the accuracy and lifespan of sensors is accepted, despite limited existing experimental data. Interestingly, there is no previous data or even conjecture in the literature on the role of lymphatic vessels (LV) alone, or in combination with BV, in enhancing continuous glucose monitoring (CGM) in vivo. To investigate the impact of inducing vascular networks (BV and LV) at sites of glucose sensor implantation, we utilized adenovirus based local gene therapy of vascular endothelial cell growth factor-A (VEGF-A) to induce vessels at sensor implantation sites. The results of these studies demonstrated that (1) VEGF-A based local gene therapy increases vascular networks (blood vessels and lymphatic vessels) at sites of glucose sensor implantation; and (2) this local increase of vascular networks enhances glucose sensor function in vivo from 7 days to greater than 28 days postsensor implantation. This data provides "proof of concept" for the effective usage of local angiogenic factor (AF) gene therapy in mammalian models in an effort to extend CGM in vivo. It also supports the practice of a variety of viral and nonviral vectors as well as gene products (e.g. anti-inflammatory and anti-fibrosis genes) to engineer "implant friendly tissues" for the usage with implantable glucose sensors as well as other implantable devices.

Macrophage embedded fibrin gels: an in vitro platform for assessing inflammation effects on implantable glucose sensors

The erroneous and unpredictable behavior of percutaneous glucose sensors just days following implantation has limited their clinical utility for diabetes management. Recent research has implicated the presence of adherent inflammatory cells as the key mitigating factor limiting sensor functionality in this period of days post-implantation. Here we present a novel in vitro platform to mimic the cell-embedded provisional matrix that forms adjacent to the sensor immediately after implantation for the focused investigation of the effects of early stage tissue response on sensor function. This biomimetic surrogate is formed by imbibing fibrin-based gels with physiological densities of inflammatory RAW 264.7 macrophages. When surrounding functional sensors, macrophage-embedded fibrin gels contribute to sensor signal declines that are similar in both shape and magnitude to those observed in previous whole blood and small animal studies. Signal decline in the presence of gels is both metabolically-mediated and sensitive to cell type and activation. Computational modeling of the experimental setup is also presented to validate the design by showing that the cellular glucose uptake parameters necessary to achieve such experimental declines align well with literature values. Together, these data suggest this in vitro provisional matrix surrogate may serve as an effective screening tool for testing the biocompatibility of future glucose sensor designs.

Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring

Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.

LMI-Based Approaches for the Calibration of Continuous Glucose Measurement Sensors

The problem of online calibration and recalibration of continuous glucose monitoring (CGM) devices is considered. Two different parametric relations between interstitial and blood glucose are investigated and constructive algorithms to adaptively estimate the parameters within those relations are proposed. One characteristic is the explicit consideration of measurement uncertainty of the device used to collect the calibration measurements. Another feature is the automatic detection of fingerstick measurements that are not suitable to be used for calibration. Since the methods rely on the solution of linear matrix inequalities resulting in convex optimization problems, the algorithms are of moderate computational complexity and could be implemented on a CGM device. The methods were assessed on clinical data from 17 diabetic patients and the improvements with respect to the current state of the art is shown.

Cell based metabolic barriers to glucose diffusion: macrophages and continuous glucose monitoring

It is assumed that MQ are central to glucose sensor bio-fouling and therefore have a major negative impact on continuous glucose monitoring (CGM) performance in vivo. However to our knowledge there is no data in the literature to directly support or refute this assumption. Since glucose and oxygen (O2) are key to glucose sensor function in vivo, understanding and controlling glucose and O2 metabolic activity of MQ is likely key to successful glucose sensor performance. We hypothesized that the accumulation of MQ at the glucose sensor-tissue interface will act as "Cell Based Metabolic Barriers" (CBMB) to glucose diffusing from the interstitial tissue compartment to the implanted glucose sensor and as such creating an artificially low sensor output, thereby compromising sensor function and CGM. Our studies demonstrated that 1) direct injections of MQ at in vivo sensor implantation sites dramatically decreased sensor output (measured in nA), 2) addition of MQ to glucose sensors in vitro resulted in a rapid and dramatic fall in sensor output and 3) lymphocytes did not affect sensor function in vitro or in vivo. These data support our hypothesis that MQ can act as metabolic barriers to glucose and O2 diffusion in vivo and in vitro.

Impact of macrophage deficiency and depletion on continuous glucose monitoring in vivo

Although it is assumed that macrophages (MQ) have a major negative impact on continuous glucose monitoring (CGM), surprisingly there is no data in the literature to directly support or refute the role of MQ or related foreign body giant cells in the bio-fouling of glucose sensors in vivo. As such, we developed the hypothesis that MQ are key in controlling glucose sensor performance and CGM in vivo and MQ deficiencies or depletion would enhance CGM. To test this hypothesis we determined the presence/distribution of MQ at the sensor tissue interface over a 28-day time period using F4/80 antibody and immunohistochemical analysis. We also evaluated the impact of spontaneous MQ deficiency (op/op mice) and induced-transgenic MQ depletions (Diphtheria Toxin Receptor (DTR) mice) on sensor function and CGM utilizing our murine CGM system. The results of these studies demonstrated: 1) a time dependent increase in MQ accumulation (F4/80 positive cells) at the sensor tissue interface; and 2) MQ deficient mice and MQ depleted C57BL/6 mice demonstrated improved sensor performance (MARD) when compared to normal mice (C57BL/6). These studies directly demonstrate the importance of MQ in sensor function and CGM in vivo.

Predicting glucose sensor behavior in blood using transport modeling: relative impacts of protein biofouling and cellular metabolic effects

BACKGROUND

Tissue response to indwelling glucose sensors remains a confounding barrier to clinical application. While the effects of fully formed capsular tissue on sensor response have been studied, little has been done to understand how tissue interactions occurring before capsule formation hinder sensor performance. Upon insertion in subcutaneous tissue, the sensor is initially exposed to blood, blood borne constituents, and interstitial fluid. Using human whole blood as a simple ex vivo experimental system, the effects of protein accumulation at the sensor surface (biofouling effects) and cellular consumption of glucose in both the biofouling layer and in the bulk (metabolic effects) on sensor response were assessed.

METHODS

Medtronic MiniMed SofSensor glucose sensors were incubated in whole blood, plasma-diluted whole blood, and cell-free platelet-poor plasma (PPP) to analyze the impact of different blood constituents on sensor function. Experimental conditions were then simulated using MATLAB to predict the relative impacts of biofouling and metabolic effects on the observed sensor responses.

RESULTS

Protein biofouling in PPP in both the experiments and the simulations was found to have no interfering effect upon sensor response. Experimental results obtained with whole and dilute blood showed that the sensor response was markedly affected by blood borne glucose-consuming cells accumulated in the biofouling layer and in the surrounding bulk.

CONCLUSIONS

The physical barrier to glucose transport presented by protein biofouling does not hinder glucose movement to the sensor surface, and the consumption of glucose by inflammatory cells, and not erythrocytes, proximal to the sensor surface has a substantial effect on sensor response and may be the main culprit for anomalous sensor behavior immediately following implantation.

Continuous glucose monitoring: current use and future directions

Continuous glucose monitoring (CGM) is an emerging technology that provides a continuous measure of interstitial glucose levels. In addition to providing a more complete pattern of glucose excursions, CGMs utilize real-time alarms for thresholds and predictions of hypo- and hyperglycemia, as well as rate of change alarms for rapid glycemic excursions. CGM users have been able to improve glycemic control without increasing their risk of hypoglycemia. Sensor accuracy, reliability, and wearability are important challenges to CGM success and are critical to the development of an artificial pancreas (or closed-loop system).

Analysis: on the path to overcoming glucose-sensor-induced foreign body reactions

It is generally accepted that unreliable in vivo performance of implantable glucose sensors originates, in large part, from tissue reactions to the implanted sensor, including foreign body reactions (i.e., inflammation, fibrosis, and vessel regression). Development of glucose sensor coatings with increased biocompatibility would contribute to the development of a reliable long-term glucose sensor. In this issue of Journal of Diabetes Science and Technology, Van den Bosch and coauthors report on their initial in vitro results on a candidate biocompatibility coating for sensors (silica nanoparticle- polyethylene-glycol-based coating). Although the initial standard testing is encouraging, it is important that sensor-specific testing protocol be utilized to more accurately predict sensor performance in vivo. The development and application of sensor-specific testing standards will likely speed the development of biocompatible coatings that will increase sensor accuracy and lifespan in the future.

A promising solution to enhance the sensocompatibility of biosensors in continuous glucose monitoring systems

BACKGROUND

Continuous glucose monitors (CGMs) measure glucose in real time, making it possible to improve glycemic control. A promising technique involves glucose sensors implanted in subcutaneous tissue measuring glucose concentration in interstitial fluid. A major drawback of this technique is sensor bioinstability, which can lead to unpredictable drift and reproducibility. The bioinstability is partly due to sensor design but is also affected by naturally occurring subcutaneous inflammations. Applying a nonbiofouling coating to the sensor membrane could be a means to enhancing sensocompatibility.

METHODS

This study evaluates the suitability of a polyethylene-glycol-based coating on sensors in CGMs. Methods used include cross hatch, wet paper rub, paper double rub, bending, hydrophilicity, protein adsorption, bio-compatibility, hemocompatibility, and glucose/oxygen permeability testing.

RESULTS

Results demonstrate that coating homogeneity, adhesion, integrity, and scratch resistance are good. The coating repels lysozyme and bovine serum albumin, and only a low level of fibrin and blood platelet adsorption to the coating was recorded when testing in whole human blood. Cytotoxicity, irritation, sensitization, and hemolysis were assessed, and levels suggested good biocompatibility of the coating in subcutaneous tissue. Finally, it was shown that the coating can be applied to cellulose acetate membranes of different porosity without changing their permeability for glucose and oxygen.

CONCLUSIONS

These results suggest that the mechanical properties of the coating are sufficient for the given application, that the coating is effective in preventing protein adsorption and blood clot formation on the sensor surface, and that the coating can be applied to membranes without hindering their glucose and oxygen transport.

Interstitial fluid physiology as it relates to glucose monitoring technologies: symposium introduction

Nearly all commercially available glucose sensors share the subcutaneous interstitial fluid (ISF) compartment as their preferred implantation site. However, ISF physiology as it relates to glucose sensors is not well understood. This special symposium titled "Interstitial Fluid Physiology as It Relates to Glucose Monitoring Technologies" is intended to help to bridge the gap in our understanding. This symposium is intended to foster a greater understanding of biological factors that impact the success of implantable glucose monitors and to inspire additional research in the area of ISF physiology as it relates to glucose sensing. Recognition that sensor designers need to have an intimate understanding of the biological environment in which their sensor will reside is emphasized. The symposium is published in two parts, with part I published in September 2010 and part II published in May 2011. All articles published in this symposium are summarized herein.