Machine learning as a tool to design glasses with controlled dissolution for healthcare applications
Machine learning as a tool to design glasses with controlled dissolution for healthcare applications
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DOI：
10.1016/j.actbio.2020.02.037
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发表时间：
2020-04-15
影响因子：
9.7
通讯作者：
Kumar, Aditya
中科院分区：
文献类型：
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作者：
Han, Taihao;Stone-Weiss, Nicholas;Kumar, Aditya
玻璃科学的进步对提高人类的生活质量和寿命起着举足轻重的作用。然而，随着各种医疗保健应用对玻璃的需求不断增加，特别是在控制降解率的情况下，使用传统方法设计新的玻璃组合物变得越来越困难。例如，设计新的基因激活生物活性眼镜是很困难的，如果不是不可能的话，使用基于试错的方法来控制为特定患者状态量身定制的功能离子的释放。尽管如此，通过使用基于人工智能的方法，例如监督机器学习（ML），可以设计出控制功能离子释放的新眼镜。在本文中，我们提出了一个集成ML模型，用于可靠地预测与各种生物医学应用相关的各种氧化物玻璃的时间和成分依赖的溶解行为。一个综合数据库，包括1300多个从原始玻璃溶解实验中整合的数据记录，已用于ML模型的预测性能的训练和后续测试。结果表明，集合ML模型可以预测玻璃在水溶液中的化学降解行为，其pH值范围很广，与人体使用相关，其中环境可能是高酸性（例如，pH = 3），例如，由于破骨细胞分泌柠檬酸，或高碱性（pH值接近10），由于生物活性玻璃释放碱阳离子。这项研究的结果可以用来设计在各种生物环境中具有控制溶解行为的玻璃。在本文中，我们提出了一个集成机器学习（ML）模型，用于预测与各种生物医学应用相关的各种氧化物玻璃的溶解行为。结果表明，ML模型可以预测玻璃在水溶液中的化学降解行为，其pH值范围很广，与人体使用相关，其中环境可能是高酸性（例如，pH = 3），例如，由于破骨细胞分泌柠檬酸，或高碱性（pH值接近10），由于生物活性玻璃释放碱阳离子。这项研究的结果可以用来设计在各种生物环境中具有控制（期望）溶解行为的新型生物医学眼镜。(C) 2020材料学报Elsevier Ltd.出版。版权所有。
The advancement of glass science has played a pivotal role in enhancing the quality and length of human life. However, with an ever-increasing demand for glasses in a variety of healthcare applications - especially with controlled degradation rates - it is becoming difficult to design new glass compositions using conventional approaches. For example, it is difficult, if not impossible, to design new gene-activation bioactive glasses, with controlled release of functional ions tailored for specific patient states, using trial-and-error based approaches. Notwithstanding, it is possible to design new glasses with controlled release of functional ions by using artificial intelligence-based methods, for example, supervised machine learning (ML). In this paper, we present an ensemble ML model for reliable prediction of time-and composition-dependent dissolution behavior of a wide variety of oxide glasses relevant for various biomedical applications. A comprehensive database, comprising of over 1300 data-records consolidated from original glass dissolution experiments, has been used for training and subsequent testing of prediction performance of the ML model. Results demonstrate that the ensemble ML model can predict chemical degradation behavior of glasses in aqueous solutions over a wide range of pH relevant for their usage in a human body where the environment can be highly acidic (for example, pH = 3), for example, due to secretion of citric acid by osteoclasts, or highly alkaline (pH approximate to 10) due to the release of alkali cations from bioactive glasses. Outcomes of this study can be leveraged to design glasses with controlled dissolution behavior in various biological environments.Statement of SignificanceIn this paper, we present an ensemble machine learning (ML) model for prediction of dissolution behavior of a wide variety of oxide glasses relevant for various biomedical applications. The results demonstrate that the ML model can predict the chemical degradation behavior of glasses in aqueous solutions over a wide range of pH relevant for their usage in a human body where the environment can be highly acidic (for example, pH = 3), for example, due to secretion of citric acid by osteoclasts, or highly alkaline (pH approximate to 10) due to the release of alkali cations from bioactive glasses. Outcomes of this study can be leveraged to design new biomedical glasses with controlled (desired) dissolution behavior in various biological environments. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.