Electroactive calcium-alginate/polycaprolactone/reduced graphene oxide nanohybrid hydrogels for skeletal muscle tissue engineering.

Graphene derivatives such as reduced graphene oxide (rGO) are used as components of novel biomaterials for their unique electrical properties. Electrical conductivity is a crucial factor for muscle cells, which are electrically active. This study reports the development of a new type of semi-interpenetrated polymer network based on two biodegradable FDA-approved biomaterials, sodium alginate (SA) and polycaprolactone (PCL), with Ca2+ ions as SA crosslinker. Several drawbacks such as the low cell adhesion of SA and weak structural stability can be improved with the incorporation of PCL. Furthermore, this study demonstrates how this semi-IPN can be engineered with rGO nanosheets (0.5% and 2% wt/wt rGO nanosheets) to produce electroactive nanohybrid composite biomaterials. The study focuses on the microstructure and the enhancement of physical and biological properties of these advanced materials, including water sorption, surface wettability, thermal behavior and thermal degradation, mechanical properties, electrical conductivity, cell adhesion and myogenic differentiation. The results suggest the formation of a complex nano-network with different interactions between the components: bonds between SA chains induced by Ca2+ ions (egg-box model), links between rGO nanosheets and SA chains as well as between rGO nanosheets themselves through Ca2+ ions, and strong hydrogen bonding between rGO nanosheets and SA chains. The incorporation of rGO significantly increases the electrical conductivity of the nanohybrid hydrogels, with values in the range of muscle tissue. In vitro cultures with C2C12 murine myoblasts revealed that the conductive nanohybrid hydrogels are not cytotoxic and can greatly enhance myoblast adhesion and myogenic differentiation. These results indicate that these novel electroactive nanohybrid hydrogels have great potential for biomedical applications related to the regeneration of electroactive tissues, particularly in skeletal muscle tissue engineering.

Fluctuations of conformational mobility of macromolecules around the glass transition.

The heterogeneity of local dynamics in disordered systems is behind some key features of glass transition. In order to improve our understanding of the molecular dynamics in disordered systems in the vicinity of the glass transition, different parameters have been proposed to quantitatively describe dynamical heterogeneity. In the case of polymers, free volume models relate the macromolecular mobility to the free or accessible volume. The relationship between dynamic heterogeneity and fluctuations of accessible volume seems straightforward. In the present work, the heterogeneity of local dynamics in polymeric systems is analyzed by computer simulation with the bond fluctuation model. The value of the accessible volume around each polymer chain is evaluated from a snapshot or static structure at each system state, resulting in a distribution of accessible volume that reflects system heterogeneity. The relationship between the average value and the standard deviation of free volume distributions at different temperatures fits a master curve for different systems, regardless of the specific inter- and intramolecular interaction potentials that define each material. The dynamic slowdown around the glass transition is accompanied by a clear evolution of the mean value and shape of the accessible free volume distribution. The relative fluctuation of the dynamically accessible volume has been used as a parameter to quantitatively describe heterogeneity. The fluctuation varies with temperature with remarkable differences between the liquid and glassy states of the systems studied, presenting a peak at the glass transition temperature, which can be interpreted as a reflection of the distribution of local glass transition temperatures.

Técnicas de análisis de posproceso en resonancia magnetica para el estudio de la conectividad cerebral / Magnetic resonance imaging postprocessing techniques in the study of brain connectivity

La noción de conectividad cerebral es un aspecto clave para entender el funcionamiento cerebral. Las metodologías para detectar y cuantificar los diferentes tipos de conectividad con técnicas de neuroimagen son en la actualidad un área de estudio fundamental en la comprensión de la fisiopatología de muchos trastornos, tanto neurológicos como psiquiátricos. Con este artículo se pretende realizar una revisión crítica de las técnicas con resonancia magnética para medir la conectividad cerebral dentro del actual contexto del proyecto Conectoma. Las técnicas revisadas se dividen en: a) conectividad estructural b) conectividad funcional (análisis de componentes principales, análisis de componentes independientes, vóxel semilla, meta-análisis) y c) conectividad efectiva (interacciones psicofisiológicas, modelo dinámico causal, modelos autorregresivos multivariantes y modelo estructural de ecuaciones). Estos tres enfoques permiten combinar y utilizar distintas técnicas matemático-estadísticas cuyos resultados proporcionan modelos para intentar predecir la funcionalidad cerebral. Es necesario validar los hallazgos de estas técnicas con otras formas de análisis de la conectividad estructural y funcional. Esta información se integra dentro del proyecto Conectoma donde este conjunto de técnicas convergen para ofrecer una representación de todos los modelos de conectividad (AU)

Brain connectivity is a key concept for understanding brain function. Current methods to detect and quantify different types of connectivity with neuroimaging techniques are fundamental for understanding the pathophysiology of many neurologic and psychiatric disorders. This article aims to present a critical review of the magnetic resonance imaging techniques used to measure brain connectivity within the context of the Human Connectome Project. We review techniques used to measure: a) structural connectivity b) functional connectivity (main component analysis, independent component analysis, seed voxel, meta-analysis), and c) effective connectivity (psychophysiological interactions, causal dynamic models, multivariate autoregressive models, and structural equation models). These three approaches make it possible to combine and use different statistical techniques to elaborate mathematical models in the attempt to understand the functioning of the brain. The findings obtained with these techniques must be validated by other techniques for analyzing structural and functional connectivity. This information is integrated in the Human Connectome Project where all these approaches converge to provide a representation of all the different models of connectivity (AU)

[Magnetic resonance imaging postprocessing techniques in the study of brain connectivity]. / Técnicas de análisis de posproceso en resonancia magnetica para el estudio de la conectividad cerebral.

Brain connectivity is a key concept for understanding brain function. Current methods to detect and quantify different types of connectivity with neuroimaging techniques are fundamental for understanding the pathophysiology of many neurologic and psychiatric disorders. This article aims to present a critical review of the magnetic resonance imaging techniques used to measure brain connectivity within the context of the Human Connectome Project. We review techniques used to measure: a) structural connectivity b) functional connectivity (main component analysis, independent component analysis, seed voxel, meta-analysis), and c) effective connectivity (psychophysiological interactions, causal dynamic models, multivariate autoregressive models, and structural equation models). These three approaches make it possible to combine and use different statistical techniques to elaborate mathematical models in the attempt to understand the functioning of the brain. The findings obtained with these techniques must be validated by other techniques for analyzing structural and functional connectivity. This information is integrated in the Human Connectome Project where all these approaches converge to provide a representation of all the different models of connectivity.

Bond fluctuation model to describe physical aging in polymeric materials.

The thermal behavior of a polymeric material during a cooling ramp was simulated by means of the bond fluctuation model. By introducing both an intramolecular and an intermolecular potential, if the cooling rate is fast enough, the glass transition occurs, and the states attained at low temperatures can be characterized as disordered glasses. The evolution of the resulting amorphous systems was then studied during isothermal periods both for systems starting as an amorphous liquid and as an amorphous glass. The results show that after a very long annealing time at temperatures above the glass transition, an excess of energy loss appears in the system when compared to the usual glass theory. The Monte Carlo method was used to simulate the physical aging phenomena at long time scales.

Influence of low-temperature nucleation on the crystallization process of poly(L-lactide).

The crystallization kinetics of poly(l-lactide), PLLA, is slow enough to allow a quasi-amorphous polymer to be obtained at low temperature simply by quenching from the melt. The PLLA crystallization process was followed by differential scanning calorimetry and optical microscopy after nucleation isothermal treatments at temperatures just below (53 degrees C) and just above (73 degrees C) the glass transition temperature. The crystallization exotherm shown in the heating thermograms shifts toward lower temperatures as the annealing time at 73 degrees C increases. The same effect is shown to a lesser extent when the sample nucleates at 53 degrees C, showing the ability to nucleate in the glassy state, already shown in other polymers. The shape of the DSC thermograms is modeled by using Avrami's theory and allows an estimation of the number of crystallization germs formed. The results of optical microscopy are converted to thermograms by evaluating the average gray level of the image recorded in transmission mode as a function of temperature and calculating its temperature derivative. The shape of such optical thermograms is quite similar to that of the DSC traces but shows some peculiarities after long nucleation treatments. Atomic force microscopy was used to analyze the crystal morphology and is an additional proof of the effect of nucleation in the glassy state. The crystalline morphology observed in samples crystallized after nucleation in the glassy state is qualitatively different from that of samples nucleated above the glass transition temperature, and the number of crystals seems to be much greater than what could be expected from the crystallization kinetics.