An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models

Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

N-terminal fusion of a toll-like receptor 2-ligand to a Neospora caninum chimeric antigen efficiently modifies the properties of the specific immune response

Immunoprophylactic products against neosporosis during pregnancy should induce an appropriately balanced immune response. In this respect, OprI, a bacterial lipoprotein targeting toll like receptor (TLR)2, provides promising adjuvant properties. We report on the manipulation of the innate and the T-cell immune response through the fusion of OprI with the Neospora caninum chimeric protein Mic3-1-R. In contrast to Mic3-1-R, OprI-MIC3-1-R significantly activated bone-marrow dendritic cells from naïve mice. Mice immunized with OprI-Mic3-1-R induced an immune response with mixed T helper (Th)1 and Th2 properties (high levels of both immunoglobulin (Ig)G1 and IgG2a and of interleukin (IL)-10, IL-12(p70) and interferon-γ responses) whereas Mic3-1-R+saponin induced a clear Th2-biased response (low IgG2a and high IL-4 and IL-10). After mating and challenge with N. caninum, increased expression of interferon-γ was only found in placentas from OprI-Mic3-1-R immunized dams. However, no protection against vertical transmission and neonatal mortality was observed in either of the two groups. These results indicated that more exhaustive studies must be done to elucidate the immune mechanisms associated with transplacental transmission. Antigen linkage to TLR2-ligands, such as OprI, is a useful tool to investigate this enigma by reorienting the innate and adaptive immune responses against other candidate antigens in future studies.

Urinary metabolomics in Fxr-null mice reveals activated adaptive metabolic pathways upon bile acid challenge

Farnesoid X receptor (FXR) is a nuclear receptor that regulates genes involved in synthesis, metabolism, and transport of bile acids and thus plays a major role in maintaining bile acid homeostasis. In this study, metabolomic responses were investigated in urine of wild-type and Fxr-null mice fed cholic acid, an FXR ligand, using ultra-performance liquid chromatography (UPLC) coupled with electrospray time-of-flight mass spectrometry (TOFMS). Multivariate data analysis between wild-type and Fxr-null mice on a cholic acid diet revealed that the most increased ions were metabolites of p-cresol (4-methylphenol), corticosterone, and cholic acid in Fxr-null mice. The structural identities of the above metabolites were confirmed by chemical synthesis and by comparing retention time (RT) and/or tandem mass fragmentation patterns of the urinary metabolites with the authentic standards. Tauro-3alpha,6,7alpha,12alpha-tetrol (3alpha,6,7alpha,12alpha-tetrahydroxy-5beta-cholestan-26-oyltaurine), one of the most increased metabolites in Fxr-null mice on a CA diet, is a marker for efficient hydroxylation of toxic bile acids possibly through induction of Cyp3a11. A cholestatic model induced by lithocholic acid revealed that enhanced expression of Cyp3a11 is the major defense mechanism to detoxify cholestatic bile acids in Fxr-null mice. These results will be useful for identification of biomarkers for cholestasis and for determination of adaptive molecular mechanisms in cholestasis.

A novel fusion toxin derived from an EpCAM-specific designed ankyrin repeat protein has potent antitumor activity

Designed ankyrin repeat proteins (DARPins) hold great promise as a new class of binding molecules to overcome the limitations of antibodies for biomedical applications. Here, we assessed the potential of an epithelial cell adhesion molecule (EpCAM)-specific DARPin (Ec4) for tumor targeting as a fusion toxin with Pseudomonas aeruginosa exotoxin A.