Infected wounds caused by persistent inflammation exhibit poor vascularization and cellular infiltration. In order to rapidly control the inflammatory effect and accelerate wound healing, it is necessary to develop a novel drug vehicle addressing the need for infected wounds. Herein, we developed a novel dual-drug delivery system with micrometer-scale alginate fibers encapsulated in instant self-assembly peptide hydrogel. Short peptides with the sequence of Nap-Gly-Phe-Phe-Lys-His (Nap-GFFKH) could self-assemble outside the microfluidic-based alginate microfibers in weak acidic solution (pH ≈ 6.0) within 5 s. The gelation condition is close to the pH environment of the human skin. We further constructed recombinant bovine basic fibroblast growth factor (FGF-2) in fibrous alginate, which was encapsulated in antibiotic-loaded peptide hydrogel. The dual-drug delivery system exhibited good mechanical property and sustained release profiles, where antibiotic could be rapidly released from the peptide hydrogel, while the growth factor could be gradually released within 7 days. Both in vitro antibacterial experiments and in vivo animal experiments confirmed that such a dual-drug delivery system has good antibacterial activity and enhances wound healing property. We suggested that the dual-drug delivery system could be potentially applied for controlled drug release in infected wound healing, drug combination for melanoma therapy, and tissue engineering.

Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of other tokens on each specific token. While beneficial, the quadratic complexity of self-attention on the input sequence length has limited its application to longer sequences - a topic being actively studied in the community. To address this limitation, we propose Nyströmformer - a model that exhibits favorable scalability as a function of sequence length. Our idea is based on adapting the Nyström method to approximate standard self-attention with O(n) complexity. The scalability of Nyströmformer enables application to longer sequences with thousands of tokens. We perform evaluations on multiple downstream tasks on the GLUE benchmark and IMDB reviews with standard sequence length, and find that our Nyströmformer performs comparably, or in a few cases, even slightly better, than standard self-attention. On longer sequence tasks in the Long Range Arena (LRA) benchmark, Nyströmformer performs favorably relative to other efficient self-attention methods. Our code is available at https://github.com/mlpen/Nystromformer.

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