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| Main Authors: | , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Biofabrication
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/39970480/ |
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| _version_ | 1868266242325872640 |
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| author | Zhu, Kai Ding, Yi Chen, Yuqiang Su, Kechuan Zheng, Jintu Zhang, Yu Hu, Ying Wei, Jun Wang, Zenan |
| author_facet | Zhu, Kai Ding, Yi Chen, Yuqiang Su, Kechuan Zheng, Jintu Zhang, Yu Hu, Ying Wei, Jun Wang, Zenan Zhu, Kai Ding, Yi Chen, Yuqiang Su, Kechuan Zheng, Jintu Zhang, Yu Hu, Ying Wei, Jun Wang, Zenan |
| collection | PubMed - marine biology |
| contents | Advancing regenerative medicine: the Aceman system's pioneering automation and machine learning in mesenchymal stem cell biofabrication. Zhu, Kai Ding, Yi Chen, Yuqiang Su, Kechuan Zheng, Jintu Zhang, Yu Hu, Ying Wei, Jun Wang, Zenan Machine Learning Mesenchymal Stem Cells Humans Regenerative Medicine Automation Tissue Engineering Mesenchymal stem cells (MSCs) are pivotal in advancing regenerative medicine; however, the large-scale production of MSCs for clinical applications faces significant challenges related to efficiency, cost, and quality assurance. We introduce the Automated Cell Manufacturing System (Aceman), a revolutionary solution that leverages machine learning and robotics integration to optimize MSC production. This innovative system enhances both efficiency and quality in the field of regenerative medicine. With a modular design that adheres to good manufacturing practice standards, Aceman allows for scalable adherent cell cultures. A sophisticated machine learning algorithm has been developed to streamline cell counting and confluence assessment, while the accompanying control software features customization options, robust data management, and real-time monitoring capabilities. Comparative studies reveal that Aceman achieves superior efficiency in analytical and repeatable tasks compared to traditional manual methods. The system's continuous operation minimizes human error, offering substantial long-term benefits. Comprehensive cell biology assays, including Bulk RNA-Seq analysis and flow cytometry, support that the cells produced by Aceman function comparably to those cultivated through conventional techniques. Importantly, Aceman maintains the characteristic immunophenotype of MSCs during automated subcultures, representing a significant advancement in cell production technology. This system lays a solid foundation for future innovations in healthcare biomanufacturing, ultimately enhancing the potential of MSCs in therapeutic applications. |
| format | Artículo científico |
| id | pubmed_39970480 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Biofabrication |
| record_format | pubmed |
| spellingShingle | Advancing regenerative medicine: the Aceman system's pioneering automation and machine learning in mesenchymal stem cell biofabrication. Zhu, Kai Ding, Yi Chen, Yuqiang Su, Kechuan Zheng, Jintu Zhang, Yu Hu, Ying Wei, Jun Wang, Zenan Machine Learning Mesenchymal Stem Cells Humans Regenerative Medicine Automation Tissue Engineering Advancing regenerative medicine: the Aceman system's pioneering automation and machine learning in mesenchymal stem cell biofabrication. Zhu, Kai Ding, Yi Chen, Yuqiang Su, Kechuan Zheng, Jintu Zhang, Yu Hu, Ying Wei, Jun Wang, Zenan Machine Learning Mesenchymal Stem Cells Humans Regenerative Medicine Automation Tissue Engineering Mesenchymal stem cells (MSCs) are pivotal in advancing regenerative medicine; however, the large-scale production of MSCs for clinical applications faces significant challenges related to efficiency, cost, and quality assurance. We introduce the Automated Cell Manufacturing System (Aceman), a revolutionary solution that leverages machine learning and robotics integration to optimize MSC production. This innovative system enhances both efficiency and quality in the field of regenerative medicine. With a modular design that adheres to good manufacturing practice standards, Aceman allows for scalable adherent cell cultures. A sophisticated machine learning algorithm has been developed to streamline cell counting and confluence assessment, while the accompanying control software features customization options, robust data management, and real-time monitoring capabilities. Comparative studies reveal that Aceman achieves superior efficiency in analytical and repeatable tasks compared to traditional manual methods. The system's continuous operation minimizes human error, offering substantial long-term benefits. Comprehensive cell biology assays, including Bulk RNA-Seq analysis and flow cytometry, support that the cells produced by Aceman function comparably to those cultivated through conventional techniques. Importantly, Aceman maintains the characteristic immunophenotype of MSCs during automated subcultures, representing a significant advancement in cell production technology. This system lays a solid foundation for future innovations in healthcare biomanufacturing, ultimately enhancing the potential of MSCs in therapeutic applications. |
| title | Advancing regenerative medicine: the Aceman system's pioneering automation and machine learning in mesenchymal stem cell biofabrication. |
| topic | Machine Learning Mesenchymal Stem Cells Humans Regenerative Medicine Automation Tissue Engineering |
| url | https://pubmed.ncbi.nlm.nih.gov/39970480/ |