Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay

Determination of regulated and emerging mycotoxins in organic and conventional gluten-free flours by LC-MS/MS

Gluten-free cereal products have grown in popularity in recent years as they are perceived as “healthier” alternatives and can be safely consumed by celiac patients, and people with gluten intolerance or wheat allergies. Molds that produce mycotoxins contaminate cereal crops, posing a threat to global food security. Maximum levels have been set for certain mycotoxins in cereal flours; however, little is known about the levels of emerging mycotoxins in these flours. The aim of this study was to develop an efficient, sensitive, and selective method for the detection of four emerging (beauvericin and enniatins A1, B, and B1) and three regulated (aflatoxin B1, zearalenone, and deoxynivalenol) mycotoxins in gluten-free flours. Ultrasound-assisted matrix solid-phase dispersion was used in the extraction of these mycotoxins from flour samples. The validated method was utilized for the LC-MS/MS analysis of conventional and organic wholegrain oat and rice flours. Six of the seven target mycotoxins were detected in these samples. Multi-mycotoxin contamination was found in all flour types, particularly in conventional wholegrain oat flour. Despite the low detection frequency in rice flour, one sample was found to contain zearalenone at a concentration of 83.2 µg/kg, which was higher than the level set by the European Commission for cereal flours. The emerging mycotoxins had the highest detection frequencies; enniatin B was present in 53% of the samples at a maximum concentration of 56 µg/kg, followed by enniatin B1 and beauvericin, which were detected in 46% of the samples, and at levels reaching 21 µg/kg and 10 µg/kg, respectively. These results highlight the need to improve the current knowledge and regulations on the presence of mycotoxins, particularly emerging ones, in gluten-free flours and cereal-based product

Magnetic transition in nanocrystalline soft magnetic alloys analyzed via ac inductive techniques

The magnetic transition in a FeSiBCuNb nanocrystalline alloy, associated with the decoupling of ferromagnetic crystallites around the Curie point of the residual amorphous matrix, is analyzed in this work through the temperature dependence of the ac axial magnetic permeability and impedance of the samples. The temperature dependence of both complex magnitudes presents a maximum in the irreversible contribution at a certain transition temperature. While for low values of the exciting ac magnetic field the transition temperature lies below the Curie temperature of the amorphous phase, a shift above this Curie point is observed increasing the amplitude of the applied ac magnetic field. The detected field dependence is interpreted taking into account the ac nature of the inductive characterization techniques and the actual temperature dependence of the coercivity of the samples

Design evaluation of a prototype user interface to support a guideline-based decision support system in gestational diabetes

Gestational Diabetes (GD) has increased over the last 20 years, affecting up to 15% of pregnant women worldwide. The complications associated can be reduced with the appropriate glycemic control during the pregnancy

High-temperature anti-Invar behavior of gamma-Fe precipitates in Fe_xCu_(100-x) solid solutions: Ferromagnetic phases

High-temperature magnetization and neutron diffraction measurements on metastable Fe_xCu_(100-x) solid solutions have recently shown to imply that γ-Fe precipitates present ferromagnetic anti-Invar behavior. For this reason, we have studied the ferromagnetic phases of γ-Fe in moment-volume parameter space, using the general potential linearized-augmented plane-wave method and the fixed spin moment procedure in order to calculate the corresponding total energy. We find that only two ferromagnetic phases (one related to a low- spin state and the other to a high-spin state) can exist and even coexist in limited volume ranges (3.55-3.59 Å). Hence, our results provide a "revisited" version of the local spin density calculations used in the early article by Moruzzi [Phys. Rev. B 34, 1784 (1986)]. In addition, the fixed spin moment method-using an energy-moment-volume space representation-allows us to conclude that the high-spin state is the ground state of the gamma-Fe precipitates, as the anti-Invar behavior is an intrinsic property of these states. This simple scenario seems to adequately describe the perplexing phenomenology recently observed on Fe_xCu_(100-x) solid solutions

Proyecto PREDIRCAM 2. Análisis preliminar de uso y valoración de la plataforma

En la actualidad, la prevalencia de las enfermedades no transmisibles (Non-communicable diseases NCD) y la cantidad de muertes causadas por éstas es muy elevada, en su mayoría, consecuencia del envejecimiento de la población, el aumento de la obesidad y los hábitos de vida sedentarios. En este trabajo se describen el funcionamiento y los resultados preliminares del proyecto Predircam 2, destinado al desarrollo y validación de una plataforma inteligente de tecnologías biomédicas para la monitorización, prevención y tratamiento personalizados del sobrepeso, la obesidad y la prevención de enfermedades asociadas como la diabetes, hipertensión arterial o alteraciones del metabolismo lipídico. El objetivo de este trabajo es presentar los resultados preliminares del análisis del uso de la plataforma, la evaluación de la usabilidad y la valoración de la atención recibida por los pacientes en relación a los profesionales sanitarios

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%

Development of a genetic tool for functional screening of anti-malarial bioactive extracts in metagenomic libraries

Ajuts: Departamento Administrativo de Ciencias, Tecnología e Innovación (Colciencias), República de Colombia; Convocatoria 489 - 2009, Código 657048925406, Contrato de financiación RC. 427 - 2009 Colciencias - CorpoGen; Programa de Asistencias Graduadas de Universidad de los Andes, Bogotá, Colombia; i Programa Jóvenes Investigadores de ColcienciasBACKGROUND: The chemical treatment of Plasmodium falciparum for human infections is losing efficacy each year due to the rise of resistance. One possible strategy to find novel anti-malarial drugs is to access the largest reservoir of genomic biodiversity source on earth present in metagenomes of environmental microbial communities. METHODS: A bioluminescent P. falciparum parasite was used to quickly detect shifts in viability of microcultures grown in 96-well plates. A synthetic gene encoding the Dermaseptin 4 peptide was designed and cloned under tight transcriptional control in a large metagenomic insert context (30 kb) to serve as proof-of-principle for the screening platform. RESULTS: Decrease in parasite viability consistently correlated with bioluminescence emitted from parasite microcultures, after their exposure to bacterial extracts containing a plasmid or fosmid engineered to encode the Dermaseptin 4 anti-malarial peptide. Here, a new technical platform to access the anti-malarial potential in microbial environmental metagenomes has been develope