Burn infection delays wound healing and increases the burn patient mortality. Consequently, a new dressing with antibacterial and anti‐inflammatory dual properties is urgently required for wound healing. In this study, we propose a combination of methacrylate gelatin (GelMA) hydrogel system with silver nanoparticles embed in γ‐cyclodextrin metal–organic frameworks (Ag@MOF) and hyaluronic acid‐epigallocatechin gallate (HA‐E) for the burn wound infection treatment. Ag@MOF is used as an antibacterial agent and epigallocatechin gallate (EGCG) has exhibited biological properties of anti‐inflammation and antibacterial. The GelMA/HA‐E/Ag@MOF hydrogel enjoys suitable physical properties and sustained release of Ag
+
. Meanwhile, the hydrogel has excellent biocompatibility and could promote macrophage polarization from M1 to M2. In vivo wound healing evaluations further demonstrate that the GelMA/HA‐E/Ag@MOF hydrogel reduces the number of the bacterium efficiently, accelerates wound healing, promotes early angiogenesis, and regulates immune reaction. A further evaluation indicates that the noncanonical Wnt signal pathway is significantly activated in the GelMA/HA‐E/Ag@MOF hydrogel treated group. In conclusion, the GelMA/HA‐E/Ag@MOF hydrogel could serve as a promising multifunctional dressing for the burn wound healing.
Recent development of generative pretrained language models has been proven very successful on a wide range of NLP tasks, such as text classification, question answering, textual entailment and so on. In this work, we present a two-phase encoder decoder architecture based on Bidirectional Encoding Representation from Transformers(BERT) for extractive summarization task. We evaluated our model by both automatic metrics and human annotators, and demonstrated that the architecture achieves the stateof-the-art comparable result on large scale corpus-CNN/Daily Mail 1. As the best of our knowledge, this is the first work that applies BERT based architecture to a text summarization task and achieved the stateof-the-art comparable result.
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