Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms
Version: 1,
Uploaded by: Administrator,
Date Uploaded:
25 November 2022
Warning
You are about to be redirected to a website not operated by the Mauritius Research and Innovation Council. Kindly note that we are not responsible for the availability or content of the linked site. Are you sure you want to leave this page?
Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms
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