Adhesive Composite Hydrogel Patch for Sustained Transdermal Drug Delivery To Treat Atopic Dermatitis

Atopic dermatitis (AD) is a common chronic inflammatory skin disease. Continuous administration of steroids often causes undesired side effects; hence, drug delivery systems with high loading capacities and sustained release profiles are required. Herein, adhesive hydrogels for sustained transdermal delivery of dexamethasone (DEX), a potent corticosteroid, have been suggested for AD treatment. The adhesive composite hydrogels comprise a double network of polyacrylamide (PAM) and polydopamine (PDA) embedded with extra-large-pore mesoporous silica nanoparticles (XL-MSNs). The intrinsic skin adhesiveness of the dopamine-derived PAM/PDA hydrogels is further enhanced by XL-MSN incorporation that contributes to the simultaneous enhancement of cohesion and adhesion of the hydrogel. The resulting adhesive hydrogels exhibit a high water content and strong adhesion to porcine skin. A sustained release of DEX is obtained when DEX is loaded within the pores of XL-MSNs in PAM/PDA hydrogels compared to the rapid release from the direct loading of DEX in hydrogels. Application of DEX-loaded MSN@PAM/PDA hydrogels on an AD mouse model led to the significant suppression of AD symptoms, including the restoration of the thickened epidermal layer, decrease in inflammatory cell infiltration in the skin, recovery of collagen deposition, and decreased levels of immunoglobulin E. XL-MSN-embedded adhesive hydrogels could be a potential platform for topical drug delivery to treat inflammatory skin diseases

When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation

<div><p>Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.</p></div

Summary of crawled data.

<p>Summary of crawled data.</p

Example of deep learning data set.

<p>The z-score (, where and represent the mean and standard deviation for every date, respectively) of data for the previous 12 days (<i>t</i> = 12) was used as the values.</p

Ten topics generated by the Bitcoin forum documents.

<p>Ten topics generated by the Bitcoin forum documents.</p

Concept for building the workflow.

<p>Concept for building the workflow.</p

EVE Online Forum Opinion Analysis Example

<p>EVE Online Forum Opinion Analysis Example</p

Predicting Virtual World User Population Fluctuations with Deep Learning

<div><p>This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.</p></div