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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202303253
|2 doi
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|a pubmed25n1209.xml
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|a eng
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|a Fang, Zhou
|e verfasserin
|4 aut
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| 245 |
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|a Marriage of High-Throughput Gradient Surface Generation With Statistical Learning for the Rational Design of Functionalized Biomaterials
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 16.12.2023
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|a Date Revised 16.12.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2023 Wiley-VCH GmbH.
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|a Functional biomaterial is already an important aspect in modern therapeutics; yet, the design of novel multi-functional biomaterial is still a challenging task nowadays. When several biofunctional components are present, the complexity that arises from their combinations and interactions will lead to tedious trial-and-error screening. In this work, a novel strategy of biomaterial rational design through the marriage of gradient surface generation with statistical learning is presented. Not only can parameter combinations be screened in a high-throughput fashion, but also the optimal conditions beyond the experimentally tested range can be extrapolated from the models. The power of the strategy is demonstrated in rationally designing an unprecedented ternary functionalized surface for orthopedic implant, with optimal osteogenic, angiogenic, and neurogenic activities, and its optimality and the best osteointegration promotion are confirmed in vitro and in vivo, respectively. The presented strategy is expected to open up new possibilities in the rational design of biomaterials
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|a Journal Article
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|a bioactive peptides
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|a gradient surface
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|a high-throughput screening
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|a machine learning
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|a surface functionalized
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|a Biocompatible Materials
|2 NLM
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1 |
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|a Zhang, Meng
|e verfasserin
|4 aut
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1 |
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|a Wang, Huaiming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Junjian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yuan, Haipeng
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| 700 |
1 |
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|a Wang, Mengyao
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|4 aut
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1 |
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|a Ye, Silin
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1 |
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|a Jia, Yong-Guang
|e verfasserin
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| 700 |
1 |
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|a Sheong, Fu Kit
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Yingjun
|e verfasserin
|4 aut
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| 700 |
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|a Wang, Lin
|e verfasserin
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| 773 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
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|g 35(2023), 49 vom: 02. Dez., Seite e2303253
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|u http://dx.doi.org/10.1002/adma.202303253
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