Implants and prostheses are widely used to replace damaged tissues or to treat various diseases. However, besides the risk of bacterial or fungal infection, an inflammatory response usually occurs. Here, recent progress in the field of anti‐inflammatory biomaterials is described. Different materials and approaches are used to decrease the inflammatory response, including hydrogels, nanoparticles, implant surface coating by polymers, and a variety of systems for anti‐inflammatory drug delivery. Complex multifunctional systems dealing with inflammation, microbial infection, bone regeneration, or angiogenesis are also described. New promising stimuli‐responsive systems, such as pH‐ and temperature‐responsive materials, are also being developed that would enable an “intelligent” antiinflammatory response when the inflammation occurs. Together, different approaches hold promise for creation of novel multifunctional smart materials allowing better implant integration and tissue regeneration.
Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we first analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufficient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the first time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast first response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-inflammatory properties.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.