Gradient bimetallic ion–based hydrogels for tissue microstructure reconstruction of tendon-to-bone insertion

Although gradients play an essential role in guiding the function of tissues, achieving synchronous regeneration of gradient tissue injuries remains a challenge. Here, a gradient bimetallic (Cu and Zn) ion–based hydrogel was first constructed via the one-step coordinative crosslinking of sulfhydryl groups with copper and zinc ions for the microstructure reconstruction of the tendon-to-bone insertion. In this bimetallic hydrogel system, zinc and copper ions could not only act as crosslinkers but also provide strong antibacterial effects and induce regenerative capacity in vitro. The capability of hydrogels in simultaneously promoting tenogenesis and osteogenesis was further verified in a rat rotator cuff tear model. It was found that the Cu/Zn gradient layer could induce considerable collagen and fibrocartilage arrangement and ingrowth at the tendon-to-bone interface. Overall, the gradient bimetallic ion–based hydrogel ensures accessibility and provides opportunities to regenerate inhomogeneous tissue with physiological complexity or interface tissue.

A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty

Owing to the increasing use of permeable pavement, there is a growing need for studies that can improve its design and durability. One of the most important factors that can reduce the functionality of permeable pavement is the clogging issue. Field experiments for investigating the clogging potential are relatively expensive owing to the high-cost testing equipment and materials. Moreover, a lot of time is required for conducting real physical experiments to obtain physical properties for permeable pavement. In this paper, to overcome these limitations, we propose a three-dimensional microstructure reconstruction framework based on 3D-IDWGAN with an enhanced gradient penalty, which is an image-based computational system for clogging analysis in permeable pavement. Our proposed system first takes a two-dimensional image as an input and extracts latent features from the 2D image. Then, it generates a 3D microstructure image through the generative adversarial network part of our model with the enhanced gradient penalty. For checking the effectiveness of our system, we utilize the reconstructed 3D image combined with the numerical method for pavement microstructure analysis. Our results show improvements in the three-dimensional image generation of the microstructure, compared with other generative adversarial network methods, and the values of physical properties extracted from our model are similar to those obtained via real pavement samples.