Mairpady Anusha, Mourad Abdel-Hamid I, Mozumder Mohammad Sayem
Chemical and Petroleum Engineering Department, UAE University, Al Ain P.O. Box 15551, United Arab Emirates.
Mechanical and Aerospace Engineering Department, UAE University, Al Ain P.O. Box 15551, United Arab Emirates.
Polymers (Basel). 2022 Apr 28;14(9):1802. doi: 10.3390/polym14091802.
In designing successful cartilage substitutes, the selection of scaffold materials plays a central role, among several other important factors. In an empirical approach, the selection of the most appropriate polymer(s) for cartilage repair is an expensive and time-consuming affair, as traditionally it requires numerous trials. Moreover, it is humanly impossible to go through the huge library of literature available on the potential polymer(s) and to correlate the physical, mechanical, and biological properties that might be suitable for cartilage tissue engineering. Hence, the objective of this study is to implement an inverse design approach to predict the best polymer(s)/blend(s) for cartilage repair by using a machine-learning algorithm (i.e., multinomial logistic regression (MNLR)). Initially, a systematic bibliometric analysis on cartilage repair has been performed by using the bibliometrix package in the R program. Then, the database was created by extracting the mechanical properties of the most frequently used polymers/blends from the PoLyInfo library by using data-mining tools. Then, an MNLR algorithm was run by using the mechanical properties of the polymers, which are similar to the cartilages, as the input and the polymer(s)/blends as the predicted output. The MNLR algorithm used in this study predicts polyethylene/polyethylene-graftpoly(maleic anhydride) blend as the best candidate for cartilage repair.
在设计成功的软骨替代物时，支架材料的选择在其他几个重要因素中起着核心作用。采用经验方法，为软骨修复选择最合适的聚合物是一件昂贵且耗时的事情，因为传统上这需要进行大量试验。此外，要浏览潜在聚合物的海量文献库，并关联可能适用于软骨组织工程的物理、机械和生物学特性，这对人类来说是不可能的。因此，本研究的目的是采用逆向设计方法，通过使用机器学习算法（即多项逻辑回归（MNLR））来预测用于软骨修复的最佳聚合物/共混物。首先，使用R程序中的bibliometrix包对软骨修复进行了系统的文献计量分析。然后，通过使用数据挖掘工具从PoLyInfo库中提取最常用聚合物/共混物的机械性能来创建数据库。接着，以与软骨相似的聚合物的机械性能作为输入，聚合物/共混物作为预测输出，运行MNLR算法。本研究中使用的MNLR算法预测聚乙烯/聚乙烯接枝聚（马来酸酐）共混物是软骨修复的最佳候选物。
