The Biostator makes it possible to perform glucose clamp experiments almost automatically. Thus, blood glucose concentrations can be maintained at (or close to) a target level with substantially less effort than with the manual clamp technique. The automatisation also avoids a potential bias on the part of the investigator. However, as with the non-automated manual clamp technique, blood glucose concentrations do not remain exactly at the target value, as they show a considerable scatter around the target value. This scatter is generated by the time constants of the Biostator, i.e. the whole closed-loop system, and the autoregressive properties of the glucose clamp algorithm used. In order to describe the quality of glucose clamps over time more precisely, "cumulative sums" as an alternative to the usual coefficient of variation can be used. Practical work with the Biostator is burdened with technical difficulties and considerable costs in comparison to the manual clamp technique. Deficits concerning data storage and presentation capability of the Biostator have been overcome by an appropriate programme for an external computer. The use of the Biostator for the glucose clamp technique is not mandatory, but, the use of this machine makes it possible to perform glucose clamp studies under standardised and reproducible conditions.
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"The baseline plasma glucose concentration at the start of SHC was 5.6 mmol/L (100 mg/dL). An automated SHC technique was performed with the Biostator and standard infusion pumps to infuse glucose (20% volume for volume), acutely raise plasma glucose to consecutive target levels (50 mg/dL [2.8 mmol/L] increments), and constantly maintain these levels for 40 min up to a maximum target glucose concentration of 550 mg/dL (30.5 mmol/L) (21,23). "
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE
To examine the effect of dapagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, on the major components of renal glucose reabsorption (decreased TmG, increased splay, and reduced threshold), using the pancreatic/stepped hyperglycemic clamp (SHC) technique.RESEARCH DESIGN AND METHODS
Subjects with type 2 diabetes (n = 12) and matched healthy subjects (n = 12) underwent pancreatic/SHC (plasma glucose range 5.5-30.5 mmol/L) at baseline and after 7 days of dapagliflozin treatment. A pharmacodynamic model was developed to describe the major components of renal glucose reabsorption for both groups and then used to estimate these parameters from individual glucose titration curves.RESULTSAt baseline, type 2 diabetic subjects had elevated maximum renal glucose reabsorptive capacity (TmG), splay, and threshold compared with controls. Dapagliflozin treatment reduced the TmG and splay in both groups. However, the most significant effect of dapagliflozin was a reduction of the renal threshold for glucose excretion in type 2 diabetic and control subjects.CONCLUSION
The SGLT2 inhibitor dapagliflozin improves glycemic control in diabetic patients by reducing the TmG and threshold at which glucose is excreted in the urine.
"It is be able to sample, filter, and interpret the glucose sensor data, compare the reading with allowable normals, and accurately order just enough insulin to maintain normal glycemia. Some problems arise with designing and implementing such an AP regarding to its construction and its actual clinical efficiency, for example, instability of glucose concentration observed in the glucose clamp . Engelborghs et al.  shows that some of encountered questions including the above instability are a dynamical nature. "
[Show abstract][Hide abstract] ABSTRACT: In this paper, a physiological model of invasive blood-glucose (BG) measurement is employed to consider the diabetic treatment by the external auxiliary system, i.e., artificial pancreas (AP). For such system, there are two time delays, i.e., technological and liver's physiological delay, where the former comes from external auxiliary system with the active pancreas inputting. The technological delay and the infection degree of patients are considered as two controlled parameters to regulate the BG level of patients. This two parameters can also lead to the non-resonant double Hopf bifurcations. The classification and unfolding for the non-resonant double Hopf bifurcation are performed in terms of non-linear dynamics. The results show that such controlled parameters are very important. They can determine the efficiency for the diabetic treatment. It implies whether the diabetic patients recover or are still tormented by the simple or complex glucose fluctuation. The results have also been promising applications on analyzing, predicting and optimizing the medical outcome, evaluating the medical risk and feasibility. The physiological meaning in this paper is that one is able to achieve the better medical outcomes for the different patients by controlling the technological delay qualitatively.
Full-text · Article · Jul 2010 · International Journal of Non-Linear Mechanics
"Using an intravenous glucose clamp technique , the blood glucose concentration of a subject was varied during a ten hour study day according to a predetermined profile including two hyperglycemic events (Figure 1). "
[Show abstract][Hide abstract] ABSTRACT: Measurements of impedance spectra used for non-invasive glucose monitoring are affected by a variety of perturbing factors such as temperature and sweat/moisture fluctuations, changes in perfusion, and body movements. In order to quantify and compensate for these perturbing effects, a multi-sensor approach was suggested. Different sensors are used, measuring signals correlated with blood glucose and perturbing factors, respectively. Here, we investigate how the multiple sensor data can be transformed into meaningful information about changes in the concentration of blood glucose. Linear regression models and variable selection (stepwise for/back-ward and lasso) techniques are used to derive generally valid models allowing for the estimation of blood glucose concentration. We find that over-fitting is best avoided by using a special version of cross-validated prediction error as the model selection criterion. Indeed, the resulting models are reasonably small, plausible, and comprise an additive adjustment for the experimental run.