Objective: The systematic review aimed to determine the potential side effects of antibacterial coatings in orthopaedic implants.Methods: Publications were searched in the databases of Embase, PubMed, Web of Science and Cochrane Library using predetermined keywords up to 31 October 2022. Clinical studies reporting side effects of the surface or coating materials were included.Results: A total of 23 studies (20 cohort studies and three case reports) reporting the concerns about the side effects of antibacterial coatings were identified. Three types of coating materials, silver, iodine and gentamicin were included. All of studies raised the concerns regarding safety of antibacterial coatings, and the occurrence of adverse events was observed in seven studies. The main side effect of silver coatings was the development of argyria. For iodine coatings, only one anaphylactic case was reported as an adverse event. No systemic or other general side effects were reported for gentamicin.Conclusion: Clinical studies on the side effects of antibacterial coatings were limited. Based on the available outcomes, the most reported side effects of antibacterial coatings in clinical use were argyria with silver coatings. However, researchers should always pay attention to the potential side effects of antibacterial materials, such as systematic or local toxicity and allergy.
In this paper, a novel multitask healthcare management recommendation system leveraging the knowledge graph is proposed, which is based on deep neural network and 5G network, and it can be applied in mobile and terminal device to free up medical resources and provide treatment programs. The technique we applied is referred to as KG-based recommendation system. When several experiments have been carried out, it is demonstrated that it is more intelligent and precise in disease prediction and treatment recommendation, similar to the state of the art. Also, it works well in the accuracy and comprehension, which is much higher and highly consistent with the predictions of the theoretical model. The fact that our work involves studies of multitask healthcare management recommendation system, which can contribute to the smart healthcare development, proves to be promising and encouraging.
In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been made in the medical knowledge graph in recent studies, the issue of comprehensive medical knowledge graph in Chinese appropriate for telemedicine or mobile devices have been ignored. In our study, it is a working theory which is based on semantic mobile computing and deep learning. When several experiments have been carried out, it is demonstrated that it has better performance in generating various types of medical knowledge graph in Chinese, which is similar to that of the state-of-the-art. Also, it works well in the accuracy and comprehensive, which is much higher and highly consisted with the predictions of the theoretical model. It proves to be inspiring and encouraging that our work involving studies of medical knowledge graph in Chinese, which can stimulate the smart healthcare development.
The current visualization has been difficult to adapt to the growing of big data. The paper presents method to use binned aggregation to reduce big data for visualize a variety of data types. Givens a data storage scheme of the dimensionality reduction block. Then for interactive visualization in binned plots through multivariate data tiles. For a large range of data, we cut into a lot of small pieces. When carrying out a range query, just use the divided pieces of data for this range be spliced. The big data through reduction into data block and be interactive checking among binned plots through many multivariate data tiles.
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