M1 macrophage accumulation and excessive inflammation are commonly encountered issues in diabetic wounds and can fail in the healing process. Hence, hydrogel dressings with immunoregulatory capacity have great promise in the clinical practice of diabetic wound healing. However, current immunoregulatory hydrogels are always needed for complex interventions and high‐cost treatments, such as cytokines and cell therapies. In this study, a novel glycyrrhizic acid (GA)‐based hybrid hydrogel dressing with intrinsic immunoregulatory properties is developed to promote rapid diabetic wound healing. This hybrid hydrogel consists of interpenetrating polymer networks composed of inorganic Zn2+‐induced self‐assembled GA and photo‐crosslinked methyl acrylated silk fibroin (SF), realizing both excellent injectability and mechanical strength. Notably, the SF/GA/Zn hybrid hydrogel can regulate macrophage responses in the inflammatory microenvironment, circumventing the use of any additives. The immunomodulatory properties of the hydrogel can be harnessed for safe and efficient therapeutics that accelerate the three phases of wound repair and serve as a promising dressing for the management of diabetic wounds.

BackgroundCombination of different agents is widely used in clinic to combat complex diseases with improved therapy and reduced side effects. However, the identification of effective drug combinations remains a challenging task due to the huge number of possible combinations among candidate drugs that makes it impractical to screen putative combinations.ResultsIn this work, we construct a 'drug cocktail network' using all the known effective drug combinations extracted from the Drug Combination Database (DCDB), and propose a network-based approach to investigate drug combinations. Our results show that the agents in an effective combination tend to have more similar therapeutic effects and share more interaction partners. Based on our observations, we further develop a statistical approach termed as DCPred (Drug Combination Predictor) to predict possible drug combinations by exploiting the topological features of the drug cocktail network. Validating on the known drug combinations, DCPred achieves the overall AUC (Area Under the receiver operating characteristic Curve) score of 0.92, indicating the predictive power of our proposed approach.ConclusionsThe drug cocktail network constructed in this work provides useful insights into the underlying rules of effective drug combinations and offer important clues to accelerate the future discovery of new drug combinations.

BackgroundDrug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations.ResultsIn this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways.ConclusionsThis study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations.

Lacrimal plug is an effective and widely therapeutic strategy to treat dry eye. However, almost all commercialized plugs are fixed in a certain design and associated with many complications, such as spontaneous plug extrusion, epiphora, and granuloma and cannot be traced in the long‐term. Herein, a simple in situ forming hydrogel is developed as a tracer and degradable lacrimal plug to achieve the best match with the irregular lacrimal passages. In this strategy, methacrylate‐modified silk fibroin (SFMA) is served as a network, and a self‐assembled indocyanine green fluorescence tracer nanoparticle (FTN) is embedded as an indicator to develop the hydrogel plug using visible photo‐crosslinking. This SFMA/FTN hydrogel plug has excellent biocompatibility and biodegradability, which can be noninvasively monitored by near‐infrared light. In vivo tests based on dry eye rabbits show that the SFMA/FTN hydrogel plug can completely block the lacrimal passages and greatly improve the various clinical indicators of dry eye. These results demonstrate that the SFMA/FTN hydrogel is suitable as an injectable and degradable lacrimal plug with a long‐term tracking function. The work offers a new approach to the development of absorbable plugs for the treatment of dry eye.

BackgroundSignal transduction is an essential biological process involved in cell response to environment changes, by which extracellular signaling initiates intracellular signaling. Many computational methods have been generated in mining signal transduction networks with the increasing of high-throughput genomic and proteomic data. However, more effective means are still needed to understand the complex mechanisms of signaling pathways.ResultsWe propose a new approach, namely CASCADE_SCAN, for mining signal transduction networks from high-throughput data based on the steepest descent method using indirect protein-protein interactions (PPIs). This method is useful for actual biological application since the given proteins utilized are no longer confined to membrane receptors or transcription factors as in existing methods. The precision and recall values of CASCADE_SCAN are comparable with those of other existing methods. Moreover, functional enrichment analysis of the network components supported the reliability of the results.ConclusionsCASCADE_SCAN is a more suitable method than existing methods for detecting underlying signaling pathways where the membrane receptors or transcription factors are unknown, providing significant insight into the mechanism of cellular signaling in growth, development and cancer. A new tool based on this method is freely available at http://www.genomescience.com.cn/CASCADE_SCAN/.