Linear Time Periodic Analysis of Dc-Dc converter

Aim of this thesis is to analyze Dc-Dc converters by using the techniques of Linear Periodic Time varying (LTP) systems to estimate the amount of subharmonics injected in the load. Dc-Dc converters are used to transform a Dc input to a Dc output of different voltage. In this thesis we study in particular the so called "switch mode" converters. In this kind of devices the conversion is obtained by using fast commutations of (at least) two switches. Due to the discrete switch-positions these converters are considered a typical example of hybrid systems. Linear models with fixed coefficients (LTI system) give a description of the system inadequate to predict and to analyze harmonic effects, while linear models with coefficients that vary periodically, namely LTP system, can be used effectively to this aim. We use therefore a Linear Time Periodic (LTP) system to describe the converter. This kind of description in much more accurate but the model and the tools used to study it are more complex. In the thesis we first introduce the LTP system theory and its main results. In particular we introduce the concept of Harmonic Transfer Function (HTF). A LTP model for a Dc-Dc converter is then derived and it is shown that this model accurately describes the response of the converter. Furthermore this LTP model is used to analyze the open and closed loop behavior of the system. It is shown that the linear model estimates correctly the amplitude of the subharmonics in the output. The thesis has been developed at the Automatic Control Department, Lund University, Sweden under the supervision of Andreas Wernrud and Anders Rantzer. The Italian supervisor of this thesis is Giorgio Picci, Dipartimento di Ingegneria dell' Informazione, Università degli studi di Padova, Ital

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

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)

Clinical evaluation of a decision support system for glucose infusion in hypoglycaemic clamp experiments.

AIM To provide a preliminary evaluation of the accuracy and safety of Gluclas decision support system suggestions in a hypoglycaemic clamp study. METHODS This analysis was performed using data from 32 participants (four groups with different glucose-insulin regulation: post Roux-en-Y gastric bypass with and without postprandial hypoglycaemia syndrome, postsleeve gastrectomy and non-operated controls) undergoing Gluclas-assisted hypoglycaemic clamps (target: 2.5 mmol/L for 20 minutes at 150 minutes after oral glucose ingestion). Gluclas provided glucose infusion rate suggestions upon manual entry of blood glucose values (every 5 minutes), which were either followed or overruled by investigators after critical review. Accuracy and safety were evaluated by mean absolute error (MAE), mean absolute percentage error (MAPE), average glucose level, coefficient of variation (CV) and minimal glucose level during the 20-minute hypoglycaemic period. RESULTS Investigators accepted 84% of suggestions, with a mean deviation of 30.33 mg/min. During the hypoglycaemic period, the MAE was 0.16 (0.12-0.24) (median [interquartile range]) mmol/L and the MAPE was 6.12% (4.80%-9.29%). CV was 4.90% (3.58%-7.27%), with 5% considered the threshold for sufficient quality. The minimal glucose level was 2.40 (2.30-2.50) mmol/L. CONCLUSIONS Gluclas achieved sufficiently high accuracy with minimal safety risks in a population with differences in glucose-insulin dynamics, underscoring its applicability to various patient groups

Neem oil nanoemulsions: characterisation and antioxidant activity

The aim of the present work is to develop nanoemulsions (NEs), nanosized emulsions, manufactured for improving the delivery of active pharmaceutical ingredients. In particular, nanoemulsions composed of Neem seed oil, contain rich bioactive components, and Tween 20 as nonionic surfactant were prepared. A mean droplet size ranging from 10 to 100nm was obtained by modulating the oil/surfactant ratio. Physicochemical characterisation was carried out evaluating size, f-potential, microviscosity, polarity and turbidity of the external shell and morphology, along with stability in simulated cerebrospinal fluid (CSF), activity of Neem oil alone and in NEs, HEp-2 cell interaction and cytotoxicity studies. This study confirms the formation of NEs by Tween 20 and Neem oil at different weight ratios with small and homogenous dimensions. The antioxidant activity of Neem oil alone and in NEs was comparable, whereas its cytotoxicity was strongly reduced when loaded in NEs after interaction with HEp-2 cells

Bayesian denoising algorithm dealing with colored, non-stationary noise in continuous glucose monitoring timeseries

Introduction: The retrospective analysis of continuous glucose monitoring (CGM) timeseries can be hampered by colored and non-stationary measurement noise. Here, we introduce a Bayesian denoising (BD) algorithm to address both autocorrelation of measurement noise and temporal variability of its variance.Methods: BD utilizes adaptive, a-priori models of signal and noise, whose unknown variances are derived on partially-overlapped CGM windows, via smoothing approach based on linear mean square estimation. The CGM signal and noise variability profiles are then reconstructed using a kernel smoother. BD is first assessed on two simulated datasets, DS1 and DS2. On DS1, the effectiveness of accounting for colored noise is evaluated by comparison against a literature algorithm; on DS2, the effectiveness of accounting for the noise variance temporal variability is evaluated by comparison against a Butterworth filter. BD is then evaluated on 15 CGM timeseries measured by the Dexcom G6 (DR).Results: On DS1, BD allows reducing the root-mean-square-error (RMSE) from 8.10 [6.79–9.24] mg/dL to 6.28 [5.47–7.27] mg/dL (median [IQR]); on DS2, RMSE decreases from 6.85 [5.50–8.72] mg/dL to 5.35 [4.48–6.49] mg/dL. On DR, BD performs a reasonable tracking of noise variance variability and a satisfactory denoising.Discussion: The new algorithm effectively addresses the nature of CGM measurement error, outperforming existing denoising algorithms

First Use of Model Predictive Control in Outpatient Wearable Artificial Pancreas

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Counter-regulatory responses to postprandial hypoglycaemia in patients with post-bariatric hypoglycaemia vs surgical and non-surgical control individuals

Aims/hypothesis Post-bariatric hypoglycaemia is an increasingly recognised complication of bariatric surgery, manifesting particularly after Roux-en-Y gastric bypass. While hyperinsulinaemia is an established pathophysiological feature, the role of counter-regulation remains unclear. We aimed to assess counter-regulatory hormones and glucose fluxes during insulin-induced postprandial hypoglycaemia in patients with post-bariatric hypoglycaemia after Roux-en-Y gastric bypass vs surgical and non-surgical control individuals. Methods In this case–control study, 32 adults belonging to four groups with comparable age, sex and BMI (patients with post-bariatric hypoglycaemia, Roux-en-Y gastric bypass, sleeve gastrectomy and non-surgical control individuals) underwent a postprandial hypoglycaemic clamp in our clinical research unit to reach the glycaemic target of 2.5 mmol/l 150–170 min after ingesting 15 g of glucose. Glucose fluxes were assessed during the postprandial and hypoglycaemic period using a dual-tracer approach. The primary outcome was the incremental AUC of glucagon during hypoglycaemia. Catecholamines, cortisol, growth hormone, pancreatic polypeptide and endogenous glucose production were also analysed during hypoglycaemia. Results The rate of glucose appearance after oral administration, as well as the rates of total glucose appearance and glucose disappearance, were higher in both Roux-en-Y gastric bypass groups vs the non-surgical control group in the early postprandial period (all p<0.05). During hypoglycaemia, glucagon exposure was significantly lower in all surgical groups vs the non-surgical control group (all p<0.01). Pancreatic polypeptide levels were significantly lower in patients with post-bariatric hypoglycaemia vs the non-surgical control group (median [IQR]: 24.7 [10.9, 38.7] pmol/l vs 238.7 [186.3, 288.9] pmol/l) (p=0.005). Other hormonal responses to hypoglycaemia and endogenous glucose production did not significantly differ between the groups. Conclusions/interpretation The glucagon response to insulin-induced postprandial hypoglycaemia is lower in post-bariatric surgery individuals compared with non-surgical control individuals, irrespective of the surgical modality. No significant differences were found between patients with post-bariatric hypoglycaemia and surgical control individuals, suggesting that impaired counter-regulation is not a root cause of post-bariatric hypoglycaemia

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P &lt; 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P &lt; 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder