The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with in vivo data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.

在过去的几十年中，随着材料科学家试图了解细胞生物学和细胞材料行为，用于组织再生的聚合物支架的工程技术已经有了惊人的发展。统计方法已应用于组织工程学（TE）聚合物支架的理化性质，以指导实验条件的复杂性。我们尝试使用体外实验测试的皮肤TE电纺聚合物支架的数据和理化数据，以使用机器学习（ML）方法对支架性能进行建模。纤维直径，孔径，水接触角和杨氏模量用于查找与支架上L929成纤维细胞的3-（4,5-二甲基噻唑-2-基）-2,5-二苯基四唑溴化物（MTT）测定的相关性7天后。使用Seaborn / Scikit-learn Python库在数据上训练了六种监督学习算法。经过超参数调整后，随机森林回归产生的最高准确度为62.74％。预测模型也与体内数据相关。这是关于ML方法预测纳米纤维支架上细胞与材料相互作用的第一个初步研究。