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On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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The use of machine learning to predict the effects of cryoprotective agents on the GelMA-based bioinks used in extrusion cryobioprinting
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Qian Qiao, Xiang Zhang, Zhenhao Yan, Chuanyu Hou, Juanli Zhang, Yong He, Na Zhao, Shujie Yan, Youping Gong & Qian Li . The use of machine learning to predict the effects of cryoprotective agents on the GelMA-based bioinks used in extrusion cryobioprinting[J]. Journal of Zhejiang University Science D, 2023, 6(4): 464-477.
@article{title="The use of machine learning to predict the effects of cryoprotective
agents on the GelMA-based bioinks used in extrusion cryobioprinting",
author="Qian Qiao, Xiang Zhang, Zhenhao Yan, Chuanyu Hou, Juanli Zhang, Yong He, Na Zhao, Shujie Yan, Youping Gong & Qian Li ",
journal="Journal of Zhejiang University Science D",
volume="6",
number="4",
pages="464-477",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1007/s42242-023-00244-4"
}
%0 Journal Article
%T The use of machine learning to predict the effects of cryoprotective
agents on the GelMA-based bioinks used in extrusion cryobioprinting
%A Qian Qiao
%A Xiang Zhang
%A Zhenhao Yan
%A Chuanyu Hou
%A Juanli Zhang
%A Yong He
%A Na Zhao
%A Shujie Yan
%A Youping Gong & Qian Li
%J Journal of Zhejiang University SCIENCE D
%V 6
%N 4
%P 464-477
%@ 1869-1951
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1007/s42242-023-00244-4
TY - JOUR
T1 - The use of machine learning to predict the effects of cryoprotective
agents on the GelMA-based bioinks used in extrusion cryobioprinting
A1 - Qian Qiao
A1 - Xiang Zhang
A1 - Zhenhao Yan
A1 - Chuanyu Hou
A1 - Juanli Zhang
A1 - Yong He
A1 - Na Zhao
A1 - Shujie Yan
A1 - Youping Gong & Qian Li
J0 - Journal of Zhejiang University Science D
VL - 6
IS - 4
SP - 464
EP - 477
%@ 1869-1951
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1007/s42242-023-00244-4
Abstract: cryobioprinting has tremendous potential to solve problems to do with lack of shelf availability in traditional bioprinting
by combining extrusion bioprinting and cryopreservation. In order to ensure the viability of cells in the frozen state and
avoid the possible toxicity of dimethyl sulfoxide (DMSO), DMSO-free bioink design is critical for achieving successful
cryobioprinting. A nontoxic gelatin methacryloyl-based bioink used in cryobioprinting is composed of cryoprotective agents
(CPAs) and a buffer solution. The selection and ratio of CPAs in the bioink directly affect the survival of cells in the frozen
state. However, the development of universal and efficient cryoprotective bioinks requires extensive experimentation. We
first compared two commonly used CPA formulations via experiments in this study. Results show that the effect of using
ethylene glycol as the permeable CPA was 6.07% better than that of glycerol. Two datasets were obtained and four machinelearning models were established to predict experimental outcomes. The predictive powers of multiple linear regression
(MLR), decision tree (DT), random forest (RF), and artificial neural network (ANN) approaches were compared, suggesting
an order of ANN>RF>DT>MLR. The final selected ANN model was then applied to another dataset. Results reveal that this
machine-learning method can accurately predict the effects of cryoprotective bioinks composed of different CPAs. Outcomes
also suggest that the formulations presented here have universality. Our findings are likely to greatly accelerate research and
development on the use of bioinks for cryobioprinting.
本研究论文聚焦于冷冻生物打印中冷冻保护剂的配方及其性能预测研究。利用机器学习算法，以细胞存活率为指标，预测不同配方的冷冻保护剂的性能。冷冻生物打印将挤出式生物打印与冷冻保存相结合，具有解决传统生物打印缺乏货架可用性问题的巨大潜力。为了保证细胞在冷冻状态下的活性，同时避免二甲基亚砜(DMSO)可能产生的毒性，无DMSO的生物墨水设计是冷冻生物打印的关键。然而，开发通用和高效的生物墨水需要大量的实验。本研究首先通过实验比较了两种常用的冷冻保护剂（CPA）配方。结果表明，乙二醇作为渗透性CPA的效果比甘油的保护性能好6.07%。随后建立了两个数据集和四个不同的机器学习模型来预测实验结果。比较了多元线性回归(MLR)、决策树(DT)、随机森林(RF)和人工神经网络(ANN)算法的预测能力，其顺序为ANN>RF>DT>MLR。然后将所建立的ANN模型应用于另一个数据集。结果表明，使用机器学习算法可以准确预测由不同CPA组成的生物墨水的效果。此外，本研究提出的方法具有泛化性，有望加快用于冷冻生物打印的生物墨水的研究和发展。
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