New insights into food hydrogels with reinforced mechanical properties: A review on innovative strategies

Enhancement on the mechanical properties of hydrogels leads to a wider range of their applications in various fields. Therefore, there has been a great interest recently for developing new strategies to reinforce hydrogels. Moreover, food gels must be edible in terms of both raw materials and production. This paper reviews innovative techniques such as particle/fiber-reinforced hydrogel, double network, dual crosslinking, freeze-thaw cycles, physical conditioning and soaking methods to improve the mechanical properties of hydrogels. Additionally, their fundamental mechanisms, advantages and disadvantages have been discussed. Important biopolymers that have been employed for these strategies and also their potentials in food applications have been summarized. The general mechanism of these strategies is based on increasing the degree of crosslinking between interacting polymers in hydrogels. These links can be formed by adding fillers (oil droplets or fibers in filled gels) or cross-linkers (regarding double network and soaking method) and also by condensation or alignment of the biopolymers (freeze-thaw cycle and physical conditioning) in the gel network. The properties of particle/fiber-reinforced hydrogels extremely depend on the filler, gel matrix and the interaction between them. In freeze-thaw cycles and physical conditioning methods, it is possible to form new links in the gel network without adding any cross-linkers or fillers. It is expected that the utilization of gels will get broader and more varied in food industries by using these strategies.

Drug repurposing with network reinforcement

Background

Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data.

Results

The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations.

Conclusions

The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2858-6) contains supplementary material, which is available to authorized users.