Nihat Baysal

A Novel Method to Detect Pressure-Induced Sensor Attenuations (PISA) in an Artificial Pancreas

Background: Continuous glucose monitors (CGMs) provide real-time interstitial glucose concentrations that are essential for automated treatment of individuals with type 1 diabetes. Miscalibration, noise spikes, dropouts, or pressure applied to the site (e.g., lying on the site while sleeping) can cause inaccurate glucose signals, which could lead to inappropriate insulin dosing decisions. These studies focus on the problem of pressure-induced sensor attenuations (PISAs) that occur overnight and can cause undesirable pump shut-offs in a predictive low glucose suspend system. Methods: The algorithm presented here uses real-time CGM readings without knowledge of meals, insulin doses, activity, sensor recalibrations, or fingerstick measurements. The real-time PISA detection technique was tested on outpatient “in-home” data from a predictive low-glucose suspend trial with over 1125 nights of data. A total of 178 sets were created by using different parameters for the PISA detection algorithm to illustrate its range of available performance. Results: The tracings were reviewed via a web-based analysis tool by an engineer with an extensive expertise on analyzing clinical datasets and ~3% of the CGM readings were marked as PISA events which were used as the gold standard. It is shown that 88.34% of the PISAs were successfully detected by the algorithm, and the percentage of false detections could be reduced to 1.70% by altering the algorithm parameters. Conclusions: Use of the proposed PISA detection method can result in a significant decrease in undesirable pump suspensions overnight, and may lead to lower overnight mean glucose levels while still achieving a low risk of hypoglycemia.

Sulfated levan from Halomonas smyrnensis as a bioactive, heparin-mimetic glycan for cardiac tissue engineering applications.

Chemical derivatives of levan from Halomonas smyrnensis AAD6(T) with low, medium and high levels of sulfation were synthesized and characterized by FTIR and 2D-NMR. Sulfated levan samples were found to exhibit anticoagulation activity via the intrinsic pathway like heparin in a dose-dependent manner. Exceptionally high heparin equivalent activity of levan sulfate was shown to proceed via thrombin inhibition where decreased Factor Xa activity with increasing concentration was observed in antithrombin tests and above a certain concentration, levan sulfate showed a better inhibitor activity than heparin. In vitro experimental results were then verified in silico by docking studies using equilibrium structures obtained by molecular dynamic simulations and results suggested a sulfation dependent binding mechanism. With its high biocompatibility and heparin mimetic activity, levan sulfate can be considered as a suitable functional biomaterial to design biologically active, functionalized, thin films and engineered smart scaffolds for cardiac tissue engineering applications.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial

OBJECTIVE As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)–based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively. RESEARCH DESIGN AND METHODS A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms. RESULTS AP improved percent time 70–140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70–180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes. CONCLUSIONS Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.

Detecting sensor and insulin infusion set anomalies in an artificial pancreas

Continuous subcutaneous insulin infusion pumps and continuous glucose monitors enable individuals with type 1 diabetes to achieve tighter blood glucose control, and are critical components in a closed-loop artificial pancreas. Insulin infusion sets can fail and CGM sensor signals can suffer from a variety of anomalies. In this paper algorithms are developed to detect infusion set failures and sensor signal anomalies; both in-patient and out-patient studies are presented. A threshold-based method, based on high glucose concentrations, is shown to be adequate to detect infusion set failures. Pressure-induced sensor attenuation (PISA), which can occur when a subject rolls over and puts pressure on their sensor, is a particularly challenging problem. An algorithm based on non-physiological rates-of-change, coupled with a maximum attenuation time window, is developed to detect and compensate for PISAs. These algorithms can be used either in advisory mode for current open-loop technology, as well as an additional safety/fault detection layer as part of a fully closed-loop artificial pancreas.

A novel integrative network approach to understand the interplay between cardiovascular disease and other complex disorders.

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network can be integrated with bibliomics to reveal association with other complex disorders. In this study, the cardiovascular disease functional linkage network (CFN) containing 1536 nodes and 3345 interactions was constructed using proteins encoded by 234 genes associated with the disease. Integration of CFN with bibliomics showed that 227 out of 566 functional modules are significantly associated with one or more diseases. Analysis of functional modules revealed the possible regulatory roles of SP1 and CXCL12 in the pathogenesis of cardiovascular disease (CVD) and modulation of their activities may be considered as potential therapeutic tools. The integration of CFN with bibliomics also indicated significant relations of CVD with other complex disorders. In a stratified map the members of 227 functional modules and 58 diseases in 15 disease classes were combined. In this map, leprosy, listeria monocytogenes, myasthenia, hemorrhagic diathesis and Protein S deficiency, which were not previously reported to be associated with CVD, showed significant associations. Several cancers arising from epithelial cells were also found to be linked to other diseases through hub proteins, VEGFA and PTGS2.

Complex Disease Interventions from a Network Model for Type 2 Diabetes

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network are informative to reveal significant metabolic processes and disease’s associations with other complex disorders. In the current study, Type 2 diabetes associated functional linkage network (T2DFN) containing 2770 proteins and 15041 linkages was constructed. The functional modules in this network were scored and evaluated in terms of shared pathways, co-localization, co-expression and associations with similar diseases. The assembly of top scoring overlapping members in the functional modules revealed that, along with the well known biological pathways, circadian rhythm, diverse actions of nuclear receptors in steroid and retinoic acid metabolisms have significant occurrence in the pathophysiology of the disease. The disease’s association with other metabolic and neuromuscular disorders was established through shared proteins. Nuclear receptor NRIP1 has a pivotal role in lipid and carbohydrate metabolism, indicating the need to investigate subsequent effects of NRIP1 on Type 2 diabetes. Our study also revealed that CREB binding protein (CREBBP) and cardiotrophin-1 (CTF1) have suggestive roles in linking Type 2 diabetes and neuromuscular diseases.

Steps towards a closed-loop artificial pancreas

A closed-loop artificial pancreas has been the “Holy Grail” of efforts to improve the medical care and lifestyle of individuals with type 1 diabetes. In this talk we provide an overview of research activities related to an artificial pancreas, beginning with a history of glucose sensing technologies, insulin delivery systems, and algorithms to connect sensor signals and insulin delivery rates. Current challenges include sensor calibration errors and signal artifacts, insulin infusion set failure, uncertain meal glucose dynamics, exercise effects, and insulin-glucose sensitivity variability.

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs)

Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis—a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios.

Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.

Background: As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient’s glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures. Methods: An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed. Results: In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed (P = .58). Conclusions: As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875

Interfacial surfactant competition and its impact on poly(ethylene oxide)/Au and poly(ethylene oxide)/Ag nanocomposite properties

The structure and properties of nanocomposites of poly(ethylene oxide), with Ag and Au nanoparticles, surface modified with a 1:1 (by volume) oleylamine/oleic acid mixture, were investigated via transmission electron microscopy, scanning electron microscopy, thermogravimetric analysis, differential scanning calorimetry (DSC), infrared spectroscopy, dynamic mechanical analysis, and static mechanical testing. Results indicated that there was more oleylamine on Ag nanoparticles but more oleic acid on Au nanoparticles. This difference in surfactant populations on each nanoparticle led to different interfacial interactions with poly(ethylene oxide) and drastically influenced the glass transition temperature of these two nanocomposite systems. Almost all other properties were found to correlate strongly with dispersion and distribution state of Au and Ag nanoparticles, such that the property in question changed direction at the onset of agglomeration.