19 research outputs found
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
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