It is highly desired yet challenging to fabricate biocompatible injectable self-healing hydrogels with anti-bacterial adhesion properties for complex wounds that can autonomously adapt to different shapes and depths and can promote angiogenesis and dermal collagen synthesis for rapid wound healing. Herein, an injectable zwitterionic hydrogel with excellent self-healing property, good cytocompatibility, and antibacterial adhesion was developed from a thermoresponsive ABA triblock copolymer poly[(N-isopropyl acrylamide)-co-(butyl acrylate)-co-(sulfobetaine methacrylate)]-b-poly(ethylene glycol)-b-poly[(N-isopropyl acrylamide)-co-(butyl acrylate)-co-(sulfobetaine methacrylate)] (PZOPZ). The prepared PZOPZ hydrogel exhibits a distinct thermal-induced sol−gel transition around physiological temperature and could be easily applied in a sol state and in situ gelled to adapt complex wounds of different shapes and depths for complete coverage. Meanwhile, the hydrogel possesses a rapid selfhealing ability and can recover autonomously from damage to maintain structural and functional integrity. In addition, the CCK-8 and 2D/3D cell culture experiments revealed that the PZOPZ hydrogel dressing shows low cytotoxicity to L929 cells and can effectively prevent the adhesion of Staphylococcus aureus and Escherichia coli. In vivo investigations verified that the PZOPZ hydrogel could increase angiogenesis and dermal collagen synthesis and shorten the transition from the inflammatory to the proliferative stage, thereby providing more favorable conditions for faster wound healing. Overall, this work provides a promising strategy to develop injectable zwitterionic hydrogel dressings with multiple functions for clinic wound management.

Motivation Biological experimental approaches to protein–protein interaction (PPI) site prediction are critical for understanding the mechanisms of biochemical processes but are time-consuming and laborious. With the development of Deep Learning (DL) techniques, the most popular Convolutional Neural Networks (CNN)-based methods have been proposed to address these problems. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in protein sequences. Current methods cannot efficiently explore the nature of Position Specific Scoring Matrix (PSSM), secondary structure and raw protein sequences by processing them all together. For PPI site prediction, how to effectively model the PPI context with attention to prediction remains an open problem. In addition, the long-distance dependencies of PPI features are important, which is very challenging for many CNN-based methods because the innate ability of CNN is difficult to outperform auto-regressive models like Transformers. Results To effectively mine the properties of PPI features, a novel hybrid neural network named HN-PPISP is proposed, which integrates a Multi-layer Perceptron Mixer (MLP-Mixer) module for local feature extraction and a two-stage multi-branch module for global feature capture. The model merits Transformer, TextCNN and Bi-LSTM as a powerful alternative for PPI site prediction. On the one hand, this is the first application of an advanced Transformer (i.e. MLP-Mixer) with a hybrid network for sequence-based PPI prediction. On the other hand, unlike existing methods that treat global features altogether, the proposed two-stage multi-branch hybrid module firstly assigns different attention scores to the input features and then encodes the feature through different branch modules. In the first stage, different improved attention modules are hybridized to extract features from the raw protein sequences, secondary structure and PSSM, respectively. In the second stage, a multi-branch network is designed to aggregate information from both branches in parallel. The two branches encode the features and extract dependencies through several operations such as TextCNN, Bi-LSTM and different activation functions. Experimental results on real-world public datasets show that our model consistently achieves state-of-the-art performance over seven remarkable baselines. Availability The source code of HN-PPISP model is available at https://github.com/ylxu05/HN-PPISP.

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