Electrospun nanofibers that contain silver nanoparticles (AgNPs) have a strong antibacterial activity that is beneficial to wound healing. However, most of the literature available on the bactericidal effects of this material is based on the use of AgNPs with uncontrolled size, shape, surface properties, and degree of aggregation. In this study, we report the first versatile synthesis of novel catechol moieties presenting electrospun nanofibers functionalized with AgNPs through catechol redox chemistry. The synthetic strategy allows control of the size and amount of AgNPs on the surface of nanofibers with the minimum degree of aggregation. We also evaluated the rate of release of the AgNPs, the biocompatibility of the nanofibers, the antibacterial activity in vitro, and the wound healing capacity in vivo. Our results suggest that these silver-releasing nanofibers have great potential for use in wound healing applications.

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Administration of B1 receptor antagonist BI113823 significantly reduced endotoxin-induced direct lung injury and also reduced sepsis-induced lung inflammatory response. Most importantly, BI113823 improved survival following severe polymicrobial sepsis.

Deep learning (DL) is a branch of machine learning (ML) capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across a range of domains and applications. Single-cell (SC) omics are often high-dimensional, sparse, and complex, making DL techniques ideal for analyzing and processing such data. We examine DL applications in a variety of single-cell omics (genomics, transcriptomics, proteomics, metabolomics and multi-omics integration) and address whether DL techniques will prove to be advantageous or if the SC omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized or addressed the most pressing challenges of the SC omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) but lacking the needed biological interpretability in many cases. Although such developments have generally been gradual, recent advances reveal that DL methods can offer valuable resources in fast-tracking and advancing research in SC.Abstract Figure

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