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Human milk contains biologically important amounts of transforming growth factor-β2 isoform (TGF- β2), which is presumed to protect against inflammatory gut mucosal injury in the neonate. In preclinical models, enterally administered TGF-β2 can protect against experimental necrotizing enterocolitis, an inflammatory bowel necrosis of premature infants. In this study, we investigated whether TGF-β bioactivity in human preterm milk could be enhanced for therapeutic purposes by adding recombinant TGF-β2 (rTGF-β2) to milk prior to feeding. Milk-borne TGF-β bioactivity was measured by established luciferase reporter assays. Molecular interactions of TGF-β2 were investigated by nondenaturing gel electrophoresis and immunoblots, computational molecular modeling, and affinity capillary electrophoresis. Addition of rTGF-β2 (20-40 nM) to human preterm milk samples failed to increase TGF-β bioactivity in milk. Milk-borne TGF-β2 was bound to chondroitin sulfate (CS) containing proteoglycan(s) such as biglycan, which are expressed in high concentrations in milk. Chondroitinase treatment of milk increased the bioactivity of both endogenous and rTGF-β2, and consequently, enhanced the ability of preterm milk to suppress LPS-induced NF-κB activation in macrophages. These findings provide a mechanism for the normally low bioavailability of milk-borne TGF-β2 and identify chondroitinase digestion of milk as a potential therapeutic strategy to enhance the anti-inflammatory effects of preterm milk. [ABSTRACT FROM AUTHOR]

Purpose - Social relations play an important role in a real community. Interaction patterns reveal relations among actors (such as persons, groups, firms), which can be merged to produce valuable information such as a network structure. This paper aims to present a new approach to extract inter-firm networks from the web for further analysis. Design/methodology/approach - In this study extraction of relations between a pair of firms is obtained by using a search engine and text processing. Because names of firms co-appear coincidentally on the web, an advanced algorithm is proposed, which is characterised by the addition of keywords ("relation keywords") to a query. The relation keywords are obtained from the web using a Jaccard coefficient. Findings - As an application, a network of 60 firms in Japan is extracted including IT, communication, broadcasting, and electronics firms from the web and comprehensive evaluations of this approach are shown. The alliance and lawsuit relations are easily obtainable from the web using the algorithm. By adding relation keywords to named pairs of firms as a query, It is possible to collect target pages from the top of web pages more precisely than by only using the named pairs as a query. Practical implications - This study proposes a new approach for extracting inter-firm networks from the web. The obtained network is useful in several ways. It is possible to find a cluster of firms and characterise a firm by its cluster. Business experts often make such inferences based on firm relations and firm groups. For that reason the firm network might enhance inferential abilities on the business domain. Also we might use obtained networks to recommend business partners based on structural advantages. The authors'' intuition is that extracting a social network might provide information that is only recognisable from the network point of view. For example, the centrality of each firm is identified only after generating a social network. Originality/value - This study is a first attempt to extract inter-firm networks from the web using a search engine. The approach is also applicable to other actors, such as famous persons, organisations or other multiple relational entities. [ABSTRACT FROM AUTHOR]