ABSTRACT
Hydrogels with adhesive properties have the potential for rapid haemostasis and wound healing in uncontrolled non-pressurized surface bleeding. Herein, a typical hydrogen bond-crosslinked hydrogel with the above functions was constructed by directly mixing solutions of humic acid (HA) and polyvinylpyrrolidone (PVP), in which the HA worked as a crosslinking agent to form hydrogen bonds with the PVP. By altering the concentration of HA, a cluster of stable and uniform hydrogels were prepared within 10 s. The dynamic and reversible nature of the hydrogen bonds gave the HA/PVP complex (HPC) hydrogels injectability and good flexibility, as well as a self-healing ability. Moreover, the numerous functional groups in the hydrogels enhanced the cohesion strength and interaction on the interface between the hydrogel and the substrate, endowing them with good adhesion properties. The unique chemical composition and cross-linking mechanism gave the HPC hydrogel good biocompatibility. Taking advantage of all these features, the HPC hydrogels obtained in this work were broadly applied as haemostatic agents and showed a good therapeutic effect. This work might lead to an improvement in the development of multifunctional non-covalent hydrogels for application to biomaterials.
ABSTRACT
BACKGROUND: With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. OBJECTIVE: To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. METHODS: In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain-specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. RESULTS: OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. CONCLUSIONS: A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.
