Wound healing with alginate/chitosan hydrogel containing hesperidin in rat model

Skin damages have always been considered as one of the most common physical injuries. Therefore, many researches have been conducted to find an efficient method for wound healing. Since hydrogels have suitable characteristics, they are widely used for this purpose. In this study, based on the high efficiency of alginate and chitosan hydrogels in the wound healing, different concentrations of hesperidin were loaded to alginate and chitosan hydrogels followed by evaluating their morphology, swelling properties, release, weight loss, hemo- and cytocompatibility, antibacterial and toxicity properties. Finally, the therapeutic function of the prepared hydrogels was evaluated in the full-thickness dermal wound in a rat model. Our results indicated that the hydrogels have appropriate porosity (91.2 ± 5.33) with the interconnected pores. Biodegradability of the prepared hydrogel was confirmed with weight loss assessment (almost 80 after 14 days). Moreover, the time-kill assay showed the antibacterial properties of hydrogels, and MTT assay revealed the positive effect of hydrogels on cell proliferation, and they have no toxicity effect on cells. Also, the in vivo results indicated that the prepared hydrogels had better wound closure than the gauze-treated wound (the control group), and the highest wound closure percentage was observed for the alginate/chitosan/10 hesperidin group. All in all, this study shows that alginate/chitosan hydrogels loaded with 10 of hesperidin can be used to treat skin injuries in humans. © 2019 Elsevier B.V

Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

In this paper, a novel approach for optimizing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows jointly exploiting the wireless and social context of wireless users for optimizing the overall allocation of resources and improving the traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game, in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel self-organizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows offloading a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on wireless links and in the backhaul

Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

In this paper, a novel approach for optimizing and managing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows to jointly exploit both the wireless and social context of wireless users for optimizing the overall allocation of resources and improving traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel, selforganizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows to offload a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on the wireless links and on the backhaul.Comment: Submitted to the IEEE Transaction on Wireless Communication