Inhibition of transglutaminase activity reduces extracellular matrix accumulation induced by high glucose levels in proximal tubular epithelial cells

Diabetic nephropathy affects 30-40% of diabetics leading to end-stage kidney failure through progressive scarring and fibrosis. Previous evidence suggests that tissue transglutaminase (tTg) and its protein cross-link product epsilon(gamma-glutamyl)lysine contribute to the expanding renal tubulointerstitial and glomerular basement membranes in this disease. Using an in vitro cell culture model of renal proximal tubular epithelial cells we determined the link between elevated glucose levels with changes in expression and activity of tTg and then, by using a highly specific site directed inhibitor of tTg (1,3-dimethyl-2[(oxopropyl)thio]imidazolium), determined the contribution of tTg to glucose-induced matrix accumulation. Exposure of cells to 36 mm glucose over 96 h caused an mRNA-dependent increase in tTg activity with a 25% increase in extracellular matrix (ECM)-associated tTg and a 150% increase in ECM epsilon(gamma-glutamyl)lysine cross-linking. This was paralleled by an elevation in total deposited ECM resulting from higher levels of deposited collagen and fibronectin. These were associated with raised mRNA for collagens III, IV, and fibronectin. The specific site-directed inhibitor of tTg normalized both tTg activity and ECM-associated epsilon(gamma-glutamyl)lysine. Levels of ECM per cell returned to near control levels with non-transcriptional reductions in deposited collagen and fibronectin. No changes in transforming growth factor beta1 (expression or biological activity) occurred that could account for our observations, whereas incubation of tTg with collagen III indicated that cross-linking could directly increase the rate of collagen fibril/gel formation. We conclude that Tg inhibition reduces glucose-induced deposition of ECM proteins independently of changes in ECM and transforming growth factor beta1 synthesis thus opening up its possible application in the treatment other fibrotic and scarring diseases where tTg has been implicated.

Leveraging product adopter information from online reviews for product recommendation

The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are the ncategorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterize both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed frame work for product recommendation.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.