“Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues

The availability of continuous glucose monitoring (CGM) sensors allows development of new strategies for the treatment of diabetes. In particular, from an on-line perspective, CGM sensors can become “smart” by providing them with algorithms able to generate alerts when glucose concentration is predicted to exceed the normal range thresholds. To do so, at least four important aspects have to be considered and dealt with on-line. First, the CGM data must be accurately calibrated. Then, CGM data need to be filtered in order to enhance their signal-to-noise ratio (SNR). Thirdly, predictions of future glucose concentration should be generated with suitable modeling methodologies. Finally, generation of alerts should be done by minimizing the risk of detecting false and missing true events. For these four challenges, several techniques, with various degrees of sophistication, have been proposed in the literature and are critically reviewed in this paper

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

High accuracy of blood glucose prediction over the long term is essential for preventative diabetes management. The emerging closed-loop insulin delivery system such as the artificial pancreas system (APS) provides opportunities for improved glycaemic control for patients with type 1 diabetes. Existing blood glucose studies are proven effective only within 30 minutes but the accuracy deteriorates drastically when the prediction horizon increases to 45 minutes and 60 minutes. Deep learning, especially for long short term memory (LSTM) and its variants have recently been applied in various areas to achieve state-of-the-art results in tasks with complex time series data. In this study, we present deep LSTM based models that are capable of forecasting long term blood glucose levels with improved prediction and clinical accuracy. We evaluate our approach using 20 cases(878,000 glucose values) from Open Source Artificial Pancreas System (OpenAPS). On 30-minutes and 45-minutes prediction, our Stacked-LSTM achieved the best performance with Root-Mean-Square-Error (RMSE) marks 11.96 & 15.81 and Clark-Grid-ZoneA marks 0.887 & 0.784. In terms of 60-minutes prediction, our ConvLSTM has the best performance with RMSE = 19.6 and Clark-Grid-ZoneA=0.714. Our models outperform existing methods in both prediction and clinical accuracy. This research can hopefully support patients with type 1 diabetes to better manage their behavior in a more preventative way and can be used in future real APS context

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System

In this paper, we build a new, simple, and interpretable mathematical model to describe the human glucose-insulin system. Our ultimate goal is the robust control of the blood glucose (BG) level of individuals to a desired healthy range, by means of adjusting the amount of nutrition and/or external insulin appropriately. By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) and intensive care unit (ICU); different choices of appropriate model functions describing uptake of nutrition and removal of glucose differentiate between the models. In both cases, the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted. The model has the form of a linear stochastic differential equation (SDE) to describe the evolution of the BG level. The model includes a term quantifying glucose removal from the bloodstream through the regulation system of the human body, and another two terms representing the effect of nutrition and externally delivered insulin. The parameters entering the equation must be learned in a patient-specific fashion, leading to personalized models. We present numerical results on patient-specific parameter estimation and future BG level forecasting in T2DM and ICU settings. The resulting model leads to the prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in the prediction. Such predictions, then, have the potential for use as part of control systems which are robust to model imperfections and noisy data. Finally, a comparison of the predictive capability of the model with two different models specifically built for T2DM and ICU contexts is also performed.Comment: 47 pages, 9 figures, 7 table

SYNTHESIS AND EVALUATION OF ANTIMICROBIAL ACTIVITY OF PHENYL AND FURAN-2-YL[1,2,4] TRIAZOLO[4,3-a]QUINOXALIN-4(5H)-ONE AND THEIR HYDRAZONE PRECURSORS

A variety of 1-(s-phenyl)-[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one (3a-3h) and 1-(s-furan-2-yl)-[1,2,4]triazolo[4,3- a]quinoxalin-4(5H)-one (5a-d) were synthesized from thermal annelation of corresponding hydrazones (2a-h) and (4a-d) respectively in the presence of ethylene glycol which is a high boiling solvent. The structures of the compounds prepared were confirmed by analytical and spectral data. Also, the newly synthesized compounds were evaluated for possible antimicrobial activity. 3-(2-(4-hydroxylbenzylidene)hydrazinyl)quinoxalin-2(1H)-one (2e) was the most active antibacterial agent while 1-(5-Chlorofuran-2-yl)-[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one (5c) stood out as the most potent antifungal agent

Prediction, Prevention and Treatment of Virally Induced Type 1 Diabetes: A Dissertation

Several viral infections have been associated with human type 1 diabetes (T1D), although it has proven difficult to unequivocally establish them as causative agents. In rodent models, however, viruses have definitely been established to cause T1D. The treatment of weanling BBDR rats with the combination of a TLR3 ligand, pIC, and an ssDNA parvovirus, KRV, precipitates T1D in nearly 100% of rats within a short, predictable timeframe. In this dissertation, we utilized the BBDR rat model to (1) identify early serum biomarkers that could predict T1D precipitated by viral induction and (2) test the efficacy of leptin, a therapeutic agent, which may have the ability to prevent diabetes onset, reverse new onset diabetes and prevent autoimmune recurrence of diabetes in rats transplanted with syngeneic islet grafts. Identification of biomarkers has long served as an invaluable tool for disease prediction. In BBDR rats, we identified an acute phase response protein, haptoglobin, as a potential biomarker for pIC + KRV induced T1D using the global proteomic profiling techniques, 2D gel analysis and iTRAQ. Upon validating this biomarker, we determined that haptoglobin was sensitive in predicting T1D in the pIC + KRV model, in which nearly 100% of the rats become diabetic, but not in models where diabetes expression was variable (KRV only or RCMV only models). However, analysis of the serum kinetics of haptoglobin and its functional capacity in the blood has given us insights into the potential role of early phase reactants in modulating virally mediated T1D. An alternative means of regulating T1D pathogenesis is through leptin. Leptin is a hormone with pleotropic roles in the body, particularly affecting energy metabolism and immune regulation. These characteristics make leptin an intriguing candidate for therapeutic testing in T1D models. Our studies have determined that high doses of leptin delivered via an adenovirus (AdLeptin) or alzet pump delivery system can prevent diabetes in \u3e 90% of rats treated with pIC + KRV. We further showed that serum hyperleptinemia was associated with decreased body weight, decreased non-fasting serum insulin levels and lack of islet insulitis in pIC + KRV treated rats pretreated with AdLeptin compared with those pretreated with PBS. We discovered that hyperleptinemia induced a profound decrease in splenic weight and splenic cellularity, including reductions in CD4+ and CD8+ T cells, DC/MACs and B cells. These findings indicate a potential mechanism whereby hyperleptinemia protects rats from virally induced T1D through the promotion of peripheral immunosuppression. Among pIC + KRV treated rats, we have also found that leptin therapy can reverse hyperglycemia in a subset of new onset diabetics for up to 20 days. In the absence of exogenous insulin, leptin treatment of new onset diabetics prevented the rapid weight loss associated with osmotic diuresis, as well as the ketosis observed in vehicle treated diabetic rats. Overall, these findings point to the therapeutic value of leptin in maintaining glycemic control and preventing ketosis in an insulin deficient state, in the absence of exogenous insulin therapy. Additionally, we have also determined that AdLeptin treatment can prolong the survival of syngeneic islets transplanted into diabetic BBDR rats for up to 50 days post transplant. Although hyperleptinemia generated by AdLeptin was unable to prevent insulitis into islet grafts, this insulitis did not appear to be destructive as islet grafts continued to stain positively for insulin when compared with control rats whose grafts succumbed to recurrent autoimmunity. In the various therapeutic settings in which we have tested leptin treatment, we have found this hormone to have significant beneficial effects. These findings merit further evaluation of leptin as a therapeutic agent in human T1D