Functional polyaniline nanofibre mats for human adipose-derived stem cell proliferation and adhesion

Conductive polymer poly(aniline-co-m-aminobenzoic acid) (P(ANI-co-m-ABA)) and polyaniline (PANI) were blended with a biodegradable, biocompatible polymer, poly(l-lactic acid) and were electrospun into nanofibres to investigate their potential application as a scaffold for human adipose-derived stem cells (hASCs). These polymers, in both conductive and non-conductive form, were electrospun with average fibre diameters of less than 400 nm. Novel nanoindentation results obtained on the individual nanofibres revealed that the elastic moduli of the nanofibres are much higher at the surface (4–10 GPa, hmax 75 nm). The composite nanofibres showed great promise as a scaffold for hASCs as they supported the cell adhesion and proliferation. After 1 week of cell culture hASCs were well spread on the substrates with abundant focal adhesions. The electrospun mats provide the cells with comparably stiff, sub-micron sized fibres as anchoring points on a substrate of high porosity. The conductive nature of these composite nanofibres offers exciting opportunities for electrical stimulation of the cells

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals

Neural Predictors of Gait Stability When Walking Freely in the Real-World.

Background: Gait impairments during real-world locomotion are common in neurological diseases. However, very little is currently known about the neural correlates of walking in the real world and on which regions of the brain are involved in regulating gait stability and performance. As a first step to understanding how neural control of gait may be impaired in neurological conditions such as Parkinson’s disease, we investigated how regional brain activation might predict walking performance in the urban environment and whilst engaging with secondary tasks in healthy subjects. Methods: We recorded gait characteristics including trunk acceleration and brain activation in fourteen healthy young subjects whilst they walked around the university campus freely (single task), while conversing with the experimenter and while texting with their smartphone. Neural spectral power density (PSD) was evaluated in three brain regions of interest, namely the pre-frontal cortex (PFC) and bilateral posterior parietal cortex (right/left PPC). We hypothesized that specific regional neural activation would predict trunk acceleration data obtained during the different walking conditions. Results: Vertical trunk acceleration was predicted by gait velocity and left PPC theta (4-7 Hz) band PSD in single-task walking (R-squared = 0.725, p = 0.001) and by gait velocity and left PPC alpha (8-12 Hz) band PSD in walking while conversing (R-squared = 0.727, p = 0.001). Medio-lateral trunk acceleration was predicted by left PPC beta (15-25 Hz) band PSD when walking while texting (R-squared = 0.434, p = 0.010). Conclusions: We suggest that the left PPC may be involved in the processes of sensorimotor integration and gait control during walking in real-world conditions. Frequency-specific coding was operative in different dual tasks and may be developed as biomarkers of gait deficits in neurological conditions during performance of these types of, now commonly undertaken, dual tasks