Recent Advancement of Functional Hydrogels toward Diabetic Wound Management

Wound healing is a dynamic, orchestrated process comprising partially overlapping phases of hemostasis, inflammation, proliferation, and remodeling. This programmed process, dysregulated in diabetic individuals, results in chronic diabetic wounds. The normal process of healing halts at the inflammatory stage, and this prolonged inflammatory phase is characteristic of diabetic wounds. There are a few U.S. Food & Drug Administration approved skin substitutes; dermal matrixes are commercially available to manage diabetic wounds. However, expensiveness and nonresponsiveness in a few instances are the major limitations of such modalities. To address the issues, several treatment strategies have been exploited to treat chronic wounds; among them hydrogel-based systems showed promise due to favorable properties such as excellent absorption capabilities, porous structure, tunable mechanical strength, and biocompatibility. In the past two decades, hydrogels have become one of the most acceptable systems in the field of wound dressing material, offering single functionality to multifunctionality. This review focuses on the advancement of functional hydrogels explored for diabetic wound management. The process of diabetic wound healing is discussed in the light of the normal healing process, and the role of macrophages in the process is explained. This review also discusses the different approaches to treat diabetic wounds using functional hydrogels, along with their future opportunities.

A filter based encoding model for mouse retinal ganglion cells

We adopt a system theoretic approach and explore the model of retinal ganglion cells as linear filters followed by a maximum-likelihood Bayesian predictor. We evaluate the model by using cross-validation, i.e., first the model parameters are estimated using a training set, and then the prediction error is computed (by comparing the stochastic rate predicted by the model with the rate code of the response) for a test set. As in system identification theory, we present spatially uniform stimuli to the retina, whose temporal intensity is drawn independently from a Gaussian distribution, and we simultaneously record the spike trains from multiple neurons. The optimal linear filter for each cell is obtained by maximizing the mutual information between the filtered stimulus values and the output of the cell (as measured in terms of a stochastic rate code). Our results show that the model presented in this paper performs well on the test set, and it outperforms the identity Bayesian model and the traditional linear model. Moreover, in order to reduce the number of optimal filters needed for prediction, we cluster the cells based on the filters' shapes, and use the cluster consensus filters to predict the firing rates of all neurons in the same class. We obtain almost the same performance with these cluster filters. These results provide hope that filter-based retinal prosthetics might be an effective and feasible idea.

Non-Poisson Fluctuation Statistics In Neuronal Inter-Spike Intervals (ISI): Hurst parameter Estimates of Mouse Retinal Ganglion Signals

There is considerable recent interest in both (i) modelling the retinal ganglion cells, so that the models can generate output that approximates the actual response of the retina (such models will help design retinal prosthetics); and (ii) understanding how relevant information is encoded in the spike patterns generated by the ganglion cells (these neuronal codes will help understand how the brain analyzes visual scenes). Since the signals (as captured by ISI) are fundamentally stochastic, any modelling or analysis tool will have to track, and make assumptions about, the fluctuations or noise inherently present in these signals. Even though there have been recent work claiming that the fluctuations are fractal in nature, showing long-range dependencies, almost all modelling and analysis work continue to assume Poisson fluctuations. The widespread use of the Poisson model is partly for the sake of convenience, and partly due to the fact that those claiming on fractal nature of ISI are contradictory: In [1] a long-range dependency (i.e., Hurst parameter [2], H > 0.5) is claimed in cat's retina, and in [3] an H < 0.5 and a long-range anti-correlation are claimed for paddlefish electroreceptors. We resolve this issue by studying the ISI of more than 50 ganglion cells recorded from two different mouse retinas, and (i) Conclusively show that the Hurst parameter is less than 0.5; we also show why the results presented in [1] are erroneous: methods that do not detrend the data were used.