The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning
Injectable recombinant collagen hydrogels (RCHs) are crucial in biomedical applications. Culture conditions play an important role in the preparation of hydrogels. However, determining the characteristics of hydrogels under certain conditions and determining the optimal conditions swiftly still rema...
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MDPI AG
2025-02-01
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| Series: | Gels |
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| Online Access: | https://www.mdpi.com/2310-2861/11/2/141 |
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| author | Mengyu Li Long Zhao Yanan Ren Linfei Zuo Ziyi Shen Jiawei Wu |
| author_facet | Mengyu Li Long Zhao Yanan Ren Linfei Zuo Ziyi Shen Jiawei Wu |
| author_sort | Mengyu Li |
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| description | Injectable recombinant collagen hydrogels (RCHs) are crucial in biomedical applications. Culture conditions play an important role in the preparation of hydrogels. However, determining the characteristics of hydrogels under certain conditions and determining the optimal conditions swiftly still remain challenging tasks. In this study, a machine learning approach was introduced to explore the correlation between hydrogel characteristics and culture conditions and determine the optimal culture conditions. The study focused on four key factors as independent variables: initial substrate concentration, reaction temperature, pH level, and reaction time, while the dependent variable was the elastic modulus of the hydrogels. To analyze the impact of these factors on the elastic modulus, four mathematical models were employed, including multiple linear regression (ML), decision tree (DT), support vector machine (SVM), and neural network (NN). The theoretical outputs of NN were closest to the actual values. Therefore, NN proved to be the most suitable model. Subsequently, the optimal culture conditions were identified as a substrate concentration of 15% (<i>W</i>/<i>V</i>), a reaction temperature of 4 °C, a pH of 7.0, and a reaction time of 12 h. The hydrogels prepared under these specific conditions exhibited a predicted elastic modulus of 15,340 Pa, approaching that of natural elastic cartilage. |
| format | Article |
| id | doaj-art-ad8caabcfce84be696d3318e90d800f7 |
| institution | DOAJ |
| issn | 2310-2861 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Gels |
| spelling | doaj-art-ad8caabcfce84be696d3318e90d800f72025-08-20T03:12:14ZengMDPI AGGels2310-28612025-02-0111214110.3390/gels11020141The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine LearningMengyu Li0Long Zhao1Yanan Ren2Linfei Zuo3Ziyi Shen4Jiawei Wu5Key Laboratory of Resource Biology and Modern Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi’an 710069, ChinaBytedance, Beijing 100034, ChinaProvincial Key Laboratory of Biotechnology and Biochemical Engineering, School of Medicine, Northwest University, Xi’an 710069, ChinaProvincial Key Laboratory of Biotechnology and Biochemical Engineering, School of Medicine, Northwest University, Xi’an 710069, ChinaProvincial Key Laboratory of Biotechnology and Biochemical Engineering, School of Medicine, Northwest University, Xi’an 710069, ChinaProvincial Key Laboratory of Biotechnology and Biochemical Engineering, School of Medicine, Northwest University, Xi’an 710069, ChinaInjectable recombinant collagen hydrogels (RCHs) are crucial in biomedical applications. Culture conditions play an important role in the preparation of hydrogels. However, determining the characteristics of hydrogels under certain conditions and determining the optimal conditions swiftly still remain challenging tasks. In this study, a machine learning approach was introduced to explore the correlation between hydrogel characteristics and culture conditions and determine the optimal culture conditions. The study focused on four key factors as independent variables: initial substrate concentration, reaction temperature, pH level, and reaction time, while the dependent variable was the elastic modulus of the hydrogels. To analyze the impact of these factors on the elastic modulus, four mathematical models were employed, including multiple linear regression (ML), decision tree (DT), support vector machine (SVM), and neural network (NN). The theoretical outputs of NN were closest to the actual values. Therefore, NN proved to be the most suitable model. Subsequently, the optimal culture conditions were identified as a substrate concentration of 15% (<i>W</i>/<i>V</i>), a reaction temperature of 4 °C, a pH of 7.0, and a reaction time of 12 h. The hydrogels prepared under these specific conditions exhibited a predicted elastic modulus of 15,340 Pa, approaching that of natural elastic cartilage.https://www.mdpi.com/2310-2861/11/2/141recombinant collagenhydrogelmachine learningdecision treesupport vector machineneural network |
| spellingShingle | Mengyu Li Long Zhao Yanan Ren Linfei Zuo Ziyi Shen Jiawei Wu The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning Gels recombinant collagen hydrogel machine learning decision tree support vector machine neural network |
| title | The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning |
| title_full | The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning |
| title_fullStr | The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning |
| title_full_unstemmed | The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning |
| title_short | The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning |
| title_sort | optimization of culture conditions for injectable recombinant collagen hydrogel preparation using machine learning |
| topic | recombinant collagen hydrogel machine learning decision tree support vector machine neural network |
| url | https://www.mdpi.com/2310-2861/11/2/141 |
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