An Immunomodulatory Hydrogel by Hyperthermia-Assisted Self-Cascade Glucose Depletion and ROS Scavenging for Diabetic Foot Ulcer Wound Therapeutics.

Current therapeutic protocols for diabetic foot ulcers (DFUs), a severe and rapidly growing chronic complication in diabetic patients, remain nonspecific. Hyperglycemia-caused inflammation and excessive reactive oxygen species (ROS) are common obstacles encountered in DFU wound healing, often leading to impaired recovery. These two effects reinforce each other, forming an endless loop. However, adequate and inclusive methods are still lacking to target these two aspects and break the vicious cycle. This study proposes a novel approach for treating DFU wounds, utilizing an immunomodulatory hydrogel to achieve self-cascade glucose depletion and ROS scavenging to regulate the diabetic microenvironment. Specifically, AuPt@melanin-incorporated (GHM3) hydrogel dressing is developed to facilitate efficient hyperthermia-enhanced local glucose depletion and ROS scavenging. Mechanistically, in vitro/vivo experiments and RNA sequencing analysis demonstrate that GHM3 disrupts the ROS-inflammation cascade cycle and downregulates the ratio of M1/M2 macrophages, consequently improving the therapeutic outcomes for dorsal skin and DFU wounds in diabetic rats. In conclusion, this proposed approach offers a facile, safe, and highly efficient treatment modality for DFUs.

Multi-channel feature fusion attention Dehazing network.

Haze is a typical weather phenomena that has a significant negative impact on transportation safety, particularly in the port, highways, and airport runway areas. A multi-scale U-shaped dehazing network is proposed in this research, which is based on our multi-channel feature fusion attention structure. With the help of the feature fusion attention techniques, the model can focus on the intriguing locations with higher haze concentration area. In conjunction with UNet, it can achieve multi-scale feature reuse and residual learning, allowing it to fully utilize the feature information of each layer for image restoration. Experimental resulsts show that our technique performs well on a variety of test datasets. On highway data sets, the PSNR / SSIM / L∞ error performance over the novel technique is increased by 0.52% / 0.5% / 30.84%, 4.68% / 0.78% / 26.19% and 13.84% / 9.05% / 55.57% respectively, when compared to DehazeFormer, MIRNetv2, and FSDGN methods. The findings suggest that our proposed method performs better on image dehazing, especially in terms of L∞ error performance.