Status de vitamina d y marcadores de metabolismo óseo en diabetes gestacional

RESUMEN Introducción y objetivos: La deficiencia de vitamina D (VD) ha sido asociada con alteración del metabolismo glucémico y síndrome metabólico. Datos actuales sugieren que tendría un rol en la prevención de diabetes gestacional y pre eclampsia. El presente estudio fue diseñado para evaluar los niveles de vitamina D en embarazadas con diabetes mellitus gestacional (DMG) y su relación con los niveles de fructosamina, parathormona, marcadores del metabolismo óseo y control metabólico. Materiales y métodos: Se incluyeron 44 embarazadas con diagnóstico de DMG, entre 15 y 45 años de edad. Las variables analíticas que se determinaron fueron: fructosamina, 25- hidroxivitamina D (25-OH-VD), Parathormona (PTH), calcio total, calcio iónico, fosforo y calcio corregido por albúmina. Resultados: Se analizaron los niveles de 25-OH-VD según las estaciones del año. El valor promedio fue de 37 ng/ml en verano, 26 ng/ml en otoño, 24 ng/ml en primavera y 17 ng/ml en invierno. Se evidenció una relación inversa entre VD y PTH (p<0.014). La correlación entre VD e índice de masa corporal (IMC) revelo un valor promedio de VD de 32.27 ng/ml en grupo con IMC <30 versus 23.63 ng/ml en el grupo con IMC >30 (p<0.027). Se observó relación inversa entre VD y fructosamina (p 0,479). Conclusión: La prevalencia de deficiencia de VD durante el embarazo debe ponerse a consideración en vista de las potenciales implicancias clínicas sobre la salud materna, fetal y postnatal, especialmente cuando se asocia a otros factores de riesgo como sobrepeso, obesidad y diabetes mellitus gestacional.

ABSTRACT Introduction and objectives: Vitamin D deficiency has been associated with altered glycemic metabolism and metabolic syndrome. New data suggest that it might have a role in prevention of gestational diabetes mellitus and preeclampsia. The present study was designed to analyze the relationship between vitamin D with parathormone levels, bone turn over markers and metabolic control. Methodology: The sample consisted of 44 pregnant women with gestational diabetes mellitus, ages 15-45, who attended to endocrinology external consultation. The variables analyzed were 25-hydroxivitan D, parathormone, fructosamine, total calcium, ionic calcium, calcium corrected by albumin and phosphorus, also, the relationship with body mass index, season of the year and age. Results: 25-hydroxivitamin D levels were analyzed regarding the season of the year; the average value was 37 ng/ml in summer, 26 ng/ml in autumn, 24 ng/ml in spring and 17 ng/ml in winter. There was an inverse relationship between vitamin D and parathormone (p<0.014). The analysis regarding vitamin D and BMI revealed the following values, 32.27 ng/ml for BMI <30 and 23.63 ng/ml for BMI >30 (p<0.027). There was an inverse relationship between Vitamin D and fructosamine (p 0.479). Conclusions: The prevalence of Vitamin D deficiency during pregnancy must be taken into account because of the implications in multiple aspects of maternal, fetal and postnatal health, especially when it is associated with other risk factors as overweight, obesity and gestational diabetes mellitus.

Connection topology dependence of synchronization of neural assemblies on class 1 and 2 excitability.

Two main classes of excitable neurons are analyzed in terms of connection topology and strength of the coupling in a network of neurons. In both cases, we measure the degree of synchronization and responsiveness of the neural assembly. Class 2 excitability presents a fast wave-like propagation of the activity pattern, strong frequency dependence on the connection topology and a good level of synchronization regardless of the topology. On the other hand, class 1 excitability shows a strong dependence of the wave propagation speed and the synchronization degree on the connection topology, in addition no frequency adaptation is observed. We conclude that both types of neural excitability endow the neural assembly with very different dynamical properties. Although, for simplicity reasons, no inhibition has been included in our study, the emergent properties described in this paper may help to determine the class of excitability underlying a neural assembly.

Reclassification as supervised clustering.

In some branches of science, such as molecular biology, classes may be defined but not completely trusted. Sometimes posterior analysis proves them to be partially incorrect. Despite its relevance, this phenomenon has not received much attention within the neural computation community. We define reclassification as the task of redefining some given classes by maximum likelihood learning in a model that contains both supervised and unsupervised information. This approach leads to supervised clustering with an additional complexity penalizing term on the number of new classes. As a proof of concept, a simple reclassification algorithm is designed and applied to a data set of gene sequences. To test the performance of the algorithm, two of the original classes are merged. The algorithm is capable of unraveling the original three-class hidden structure, in contrast to the unsupervised version (K-means); moreover, it predicts the subdivision of one of the original classes into two different ones.

Fast response and temporal coherent oscillations in small-world networks.

We have investigated the role that different connectivity regimes play in the dynamics of a network of Hodgkin-Huxley neurons by computer simulations. The different connectivity topologies exhibit the following features: random topologies give rise to fast system response yet are unable to produce coherent oscillations in the average activity of the network; on the other hand, regular topologies give rise to coherent oscillations, but in a temporal scale that is not in accordance with fast signal processing. Finally, small-world topologies, which fall between random and regular ones, take advantage of the best features of both, giving rise to fast system response with coherent oscillations.

A central pattern generator to control a pyloric-based system.

A central pattern generator (CPG) is built to control a mechanical device (plant) inspired by the pyloric chamber of the lobster. Conductance-based models are used to construct the neurons of the CPG. The plant has an associated function that measures the amount of food flowing through it per unit of time. We search for the best set of solutions that give a high positive flow of food in the maximization function. The plant is symmetric and the model neurons are identical to avoid any bias in the space of solutions. We find that the solution is not unique and that three neurons are sufficient to produce positive flow. We propose an effective principle for CPGs (effective on-off connectivity) and a few predictions to be corroborated in the pyloric system of the lobster.

Modeling the physiological responses of anuran R3 ganglion cells.

Teeters and Arbib (Bio Cybernet 1991;64:197-207) presented a model of the anuran retina which qualitatively accounts for some of the characteristic response properties used to distinguish ganglion cell type in anurans. Teeters et al. (Vis Res 1993;33:2361-2379) tested the model's ability to reproduce data of Ewert and Hock (Exp Brain Res 1972;16:41-59) relating toad R2, R3 and R4 ganglion cell responses to moving worm, antiworm and square-shaped stimuli of various edge lengths for stimulus shape and size dependency. In this paper we provide an exhaustive analysis of the performance of the modeled R3 cells with respect to most of the known qualitative and quantitative physiological properties of natural R3 ganglion cells. We also introduce several relevant predictions of the model relating different responses of R3 cells under the effect of changes in different model components. In some cases the predictions have been tested in neurophysiological experiments.

Quantitative modeling of responses of anuran retina: stimulus shape and size dependency.

Teeters and Arbib presented a model of the anuran retina which qualitatively accounts for the characteristic response properties used to distinguish ganglion cell type in anurans. In this paper we test the model's ability to reproduce quantitatively tabulated data on the dependency on stimulus shape and size, with a new implementation of the model in the neural simulation language NSL. Data of Ewert and Hock relating toad R2, R3, and R4 ganglion cell responses to moving worm, antiworm, and square-shaped stimuli of various edge lengths are used to test stimulus shape and size dependency. A close match to the data can be achieved by tuning some of the model parameters while still retaining the characteristic responses to the typical stimulus types. We stress here the importance of a populational approach to the models. We place more emphasis on the variation of response properties in a population of neurons of the same class, rather than questing for the neuron of a given type. As an example of the populational approach we offer a model for the respiratory R3 response following researchers who argue that a subclass of R3 neurons are activated by stationary boundaries owing to the anuran's self induced respiratory eye movement.