Response to Comment on "Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials"

We thank Eichenlaub and coworkers for their interesting letter to the editor entitled Comment on “Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials.”1 When developing the original model,2 we acknowledged that the need of fixing SG (fractional glucose effectiveness) to a population value was an important limitation of the method. To test the implication of this assumption on the model-derived insulin sensitivity, SI , we performed an extensive validation work against independent techniques, including glucose clamp3 and multiple tracer experiment,4 and we were able to prove that SI is well correlated with model-independent indices. Eichenlaub and coworkers’ overlook the SI validation studies3,4 that fully address their concerns: the correlation was 0.81 P < .001 with clamp in 10 normal and 11 impaired glucose tolerant subjects3 and 0.86, P < .0001 with the multiple tracer experiment in 88 healthy individuals.4 Therefore, their critiques cannot be sustained in the face of such a large body of validation evidence. [...

A nonparametric approach for model individualization in an artificial pancreas

The identification of patient-tailored linear time invariant glucose-insulin models is investigated for type 1 diabetic patients, that are characterized by a substantial inter-subject variability. The individualized linear models are identified by considering a novel kernel-based nonparametric approach and are compared with a linear time invariant average model in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean squared error. Model identification and validation are based on in-silico data collected from the adult virtual population of the UVA/Padova simulator. The data generation involves a protocol designed to produce a sufficient input excitation without compromising patient safety, compatible also with real life scenarios. The identified models are exploited to synthesize an individualized Model Predictive Controller (MPC) for each patient, which is used in an Artificial Pancreas to maintain the blood glucose concentration within an euglycemic range. The MPC used in several clinical studies, synthesized on the basis of a non-individualized average linear time invariant model, is also considered as reference. The closed-loop control performance is evaluated in an in-silico study on the adult virtual population of the UVA/Padova simulator in a perturbed scenario, in which the MPC is blind to random variations of insulin sensitivity in each virtual patient. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

PRINCIPAL COMPONENT ANALYSIS OF KNEE ANGLE WAVEFORMS DURING RACE WALKING

This study aimed at understanding whether principal component analysis (PCA) may be useful to characterize race-walkers abilities at different performance levels. Seven young race-walkers of national and international rank were recruited. PCA was applied for classifying and detecting the structure of knee sagittal angle. This statistical technique allowed extracting multidimensional features that capture the greatest variation in race walking data. The scores, i.e. the projections of the original data on the components, revealed to be good discriminative factors for performance level detection. Finally, the underlying linear structure of the principal components provided a biomechanical interpretation of motor skill. The best athletes were able to correctly lock the knee during the mid-stance; the worst ones tended to bend the knee prematurely

BIOMECHANICAL ANALYSIS OF THREE DIFFERENT BLOCKING FOOTWORK TECHNIQUES IN VOLLEYBALL: A PILOT STUDY

The purpose of this study was to analyse three different blocking footwork techniques in volleyball. In particular the attention was focused on the correlation between anthropometric and kinematic parameters. Three female athletes playing in the first national league were recruited for a pilot study. Bosco tests were executed to have a morphological classification. A stereophotogrammetric system was used to acquire three blocking footwork techniques: slide step, running and jab cross over patterns. Parameters of interest included the blocking time, the jump height, the horizontal and vertical speed of the centre of mass, the frontal position of the body with respect to the net and the invasion angle of the hands over the net. A correlation between jump height and blocking time was observed only in the running step technique. The time of centre of mass maximum speed was significantly less for the jab cross-over step technique. The most effective blocking technique for every athlete was finally obtained

Interstitial fluid glucose is not just a shifted-in-time but a distorted mirror of blood glucose: Insight from an in Silico study

Glucose sensors measure glucose concentration in the interstitial fluid (ISF), remote from blood. ISF glucose is well known to be "delayed" with respect to blood glucose (BG). However, ISF glucose is not simply a shifted-in-time version of BG but exhibits a more complex pattern. METHODS: To gain insight into this problem, one can use linear systems theory. However, this may lose a more clinical readership, thus we use simulation and two case studies to convey our thinking in an easier way. In particular, we consider BG concentration measured after meal and exercise in 12 healthy volunteers, whereas ISF glucose is simulated using a well-accepted model of blood-ISF glucose kinetics, which permits calculation of the equilibration time, a parameter characterizing the system. Two metrics are defined: blood and ISF glucose difference at each time point and time to reach the same glucose value in blood and ISF. RESULTS: The simulation performed and the two metrics show that the relationship between blood-ISF glucose profiles is more complex than a pure shift in time and that the pattern depends on both equilibration time and BG. CONCLUSIONS: In this in silico study, we have illustrated, with simple case studies, the meaning of the of ISF glucose with respect to BG. Understanding that ISF glucose is not just a shifted-in-time version but a distorted mirror of BG is important for a correct use of continuous glucose monitoring for diabetes management

Accuracy of a CGM Sensor in Pediatric Subjects With Type 1 Diabetes. Comparison of Three Insertion Sites: Arm, Abdomen, and Gluteus

Patients with diabetes, especially pediatric ones, sometimes use continuous glucose monitoring (CGM) sensor in different positions from the approved ones. Here we compare the accuracy of Dexcom\uae G5 CGM sensor in three different sites: abdomen, gluteus (both approved) and arm (off-label)

Significance analysis of microarray transcript levels in time series experiments

Background: Microarray time series studies are essential to understand the dynamics of molecular events. In order to limit the analysis to those genes that change expression over time, a first necessary step is to select differentially expressed transcripts. A variety of methods have been proposed to this purpose; however, these methods are seldom applicable in practice since they require a large number of replicates, often available only for a limited number of samples. In this data-poor context, we evaluate the performance of three selection methods, using synthetic data, over a range of experimental conditions. Application to real data is also discussed. Results: Three methods are considered, to assess differentially expressed genes in data-poor conditions. Method 1 uses a threshold on individual samples based on a model of the experimental error. Method 2 calculates the area of the region bounded by the time series expression profiles, and considers the gene differentially expressed if the area exceeds a threshold based on a model of the experimental error. These two methods are compared to Method 3, recently proposed in the literature, which exploits splines fit to compare time series profiles. Application of the three methods to synthetic data indicates that Method 2 outperforms the other two both in Precision and Recall when short time series are analyzed, while Method 3 outperforms the other two for long time series. Conclusion: These results help to address the choice of the algorithm to be used in data-poor time series expression study, depending on the length of the time series

Regularised Model Identification Improves Accuracy of Multisensor Systems for Noninvasive Continuous Glucose Monitoring in Diabetes Management

Continuous glucose monitoring (CGM) by suitable portable sensors plays a central role in the treatment of diabetes, a disease currently affecting more than 350 million people worldwide. Noninvasive CGM (NI-CGM), in particular, is appealing for reasons related to patient comfort (no needles are used) but challenging. NI-CGM prototypes exploiting multisensor approaches have been recently proposed to deal with physiological and environmental disturbances. In these prototypes, signals measured noninvasively (e.g., skin impedance, temperature, optical skin properties, etc.) are combined through a static multivariate linear model for estimating glucose levels. In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO), Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. More specifically, the Elastic-Net model (i.e., the model identified using a combination of ${l}_{1}$ and ${l}_{2}$ norms) has the best results, according to the metrics widely accepted in the diabetes community. This model represents an important incremental step toward the development of NI-CGM devices effectively usable by patients